The Professional’s Handbook of Financial Risk Management
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The Professional’s Handbook of Financial Risk Management

The Professional’s Handbook of Financial Risk Management Edited by Marc Lore and Lev Borodovsky Endorsed by the Global Association of Risk Professionals

OXFORD AUCKLAND BOSTON JOHANNESBURG MELBOURNE NEW DELHI

Butterworth-Heinemann Linacre House, Jordan Hill, Oxford OX2 8DP 225 Wildwood Avenue, Woburn, MA 01801–2041 A division of Reed Educational and Professional Publishing Ltd A member of the Reed Elsevier plc group First published 2000 © Reed Educational and Professional Publishing Ltd 2000 All rights reserved. No part of this publication may be reproduced in any material form (including photocopying or storing in any medium by electronic means and whether or not transiently or incidentally to some other use of this publication) without the written permission of the copyright holder except in accordance with the provisions of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London, England W1P 9HE. Applications for the copyright holder’s written permission to reproduce any part of this publication should be addressed to the publishers

British Library Cataloguing in Publication Data The professional’s handbook of financial risk management 1 Risk management 2 Investments – Management I Lore, Marc II Borodovsky, Lev 332.6 Library of Congress Cataloguing in Publication Data The professional’s handbook of financial risk management/edited by Marc Lore and Lev Borodovsky. p.cm. Includes bibliographical references and index. ISBN 0 7506 4111 8 1 Risk management 2 Finance I Lore, Marc II Borodovsky, Lev. HD61 1.P76 [email protected]—dc21 99–088517 ISBN 0 7506 4111 8

Typeset by AccComputing, Castle Cary, Somerset Printed and bound in Great Britain

Contents FOREWORD PREFACE ABOUT GARP LIST OF CONTRIBUTORS ACKNOWLEDGEMENTS INTRODUCTION

xi xiii xiv xv xviii xix

PART 1 FOUNDATION OF RISK MANAGEMENT 1. DERIVATIVES BASICS Allan M. Malz Introduction Behavior of asset prices Forwards, futures and swaps Forward interest rates and swaps Option basics Option markets Option valuation Option risk management The volatility smile Over-the-counter option market conventions

3 3 4 7 14 16 21 24 29 34 37

2. MEASURING VOLATILITY Kostas Giannopoulos Introduction Overview of historical volatility methods Assumptions Conditional volatility models ARCH models: a review Using GARCH to measure correlation Asymmetric ARCH models Identification and diagnostic tests for ARCH An application of ARCH models to risk management Conclusions

42 42 42 43 45 46 50 52 53 55 67

3. THE YIELD CURVE P. K. Satish Introduction Bootstrapping swap curve Government Bond Curve Model review Summary

75 75 77 100 106 108

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The Professional’s Handbook of Financial Risk Management

4. CHOOSING APPROPRIATE VaR MODEL PARAMETERS AND RISK MEASUREMENT METHODS Ian Hawkins Choosing appropriate VaR model parameters Applicability of VaR Uses of VaR Risk measurement methods Sources of market risk Portfolio response to market changes Market parameter estimation Choice of distribution Volatility and correlation estimation Beta estimation Yield curve estimation Risk-aggregation methods Covariance approach Historical simulation VaR Monte Carlo simulation VaR Current practice Specific risk Concentration risk Conclusion

111 112 114 115 115 117 124 128 128 130 133 134 134 138 144 145 146 147 148 148

PART 2 MARKET RISK, CREDIT RISK AND OPERATIONAL RISK 5. YIELD CURVE RISK FACTORS: DOMESTIC AND GLOBAL CONTEXTS Wesley Phoa Introduction: handling multiple risk factors Principal component analysis International bonds Practical implications

155 155 158 168 174

6. IMPLEMENTATION OF A VALUE-AT-RISK SYSTEM Alvin Kuruc Introduction Overview of VaR methodologies Variance/covariance methodology for VaR Asset-flow mapping Mapping derivatives Gathering portfolio information from source systems Translation tables Design strategy summary Covariance data Heterogeneous unwinding periods and liquidity risk Change of base currency Information access Portfolio selection and reporting

185 185 185 187 191 194 196 199 200 200 201 201 202 203

Contents

7

7. ADDITIONAL RISKS IN FIXED-INCOME MARKETS Teri L. Geske Introduction Spread duration Prepayment uncertainty Summary

215 215 216 223 231

8. STRESS TESTING Philip Best Does VaR measure risk? Extreme value theory – an introduction Scenario analysis Stressing VaR – covariance and Monte Carlo simulation methods The problem with scenario analysis Systematic testing Credit risk stress testing Determining risk appetite and stress test limits Conclusion

233 233 237 239 242 244 244 247 251 254

9. BACKTESTING Mark Deans Introduction Comparing risk measurements and P&L Profit and loss calculation for backtesting Regulatory requirements Benefits of backtesting beyond regulatory compliance Systems requirements Review of backtesting results in annual reports Conclusion

261 261 263 265 269 271 282 285 286

10. CREDIT RISK MANAGEMENT MODELS Richard K. Skora Introduction Motivation Functionality of a good credit risk management model Review of Markowitz’s portfolio selection theory Adapting portfolio selection theory to credit risk management A framework for credit risk management models Value-at-Risk Credit risk pricing model Market risk pricing model Exposure model Risk calculation engine Capital and regulation Conclusion

290 290 290 291 293 294 295 296 299 301 301 302 302 304

11. RISK MANAGEMENT OF CREDIT DERIVATIVES Kurt S. Wilhelm Introduction Size of the credit derivatives market and impediments to growth What are credit derivatives? Risks of credit derivatives Regulatory capital issues

307 307 308 312 318 330

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The Professional’s Handbook of Financial Risk Management

A portfolio approach to credit risk management Overreliance on statistical models Future of credit risk management

333 338 339

12. OPERATIONAL RISK Michel Crouhy, Dan Galai and Bob Mark Introduction Typology of operational risks Who manages operational risk? The key to implementing bank-wide operational risk management A four-step measurement process for operational risk Capital attribution for operational risks Self-assessment versus risk management assessment Integrated operational risk Conclusions

342 342 344 346 348 351 360 363 364 365

13. OPERATIONAL RISK Duncan Wilson Introduction Why invest in operational risk management? Defining operational risk Measuring operational risk Technology risk Best practice Regulatory guidance Operational risk systems/solutions Conclusion

377 377 377 378 386 396 399 403 404 412

PART 3 ADDITIONAL RISK TYPES 14. COPING WITH MODEL RISK Franc¸ois-Serge Lhabitant Introduction Model risk: towards a definition How do we create model risk? Consequences of model risk Model risk management Conclusions

415 415 416 417 426 431 436

15. LIQUIDITY RISK Robert E. Fiedler Notation First approach Re-approaching the problem Probabilistic measurement of liquidity – Concepts Probabilistic measurement of liquidity – Methods Dynamic modeling of liquidity Liquidity portfolios Term structure of liquidity Transfer pricing of liquidity

441 441 442 449 451 455 464 468 469 471

Contents

9

16. ACCOUNTING RISK Richard Sage Definition Accounting for market-makers Accounting for end-users Conclusion

473 473 474 486 490

17. EXTERNAL REPORTING: COMPLIANCE AND DOCUMENTATION RISK Thomas Donahoe Introduction Defining compliance risk Structuring a compliance unit Creating enforceable policies Implementing compliance policies Reporting and documentation controls Summary

491 491 492 493 499 508 513 520

18. ENERGY RISK MANAGEMENT Grant Thain Introduction Background Development of alternative approaches to risk in the energy markets The energy forward curve Estimating market risk Volatility models and model risk Correlations Energy options – financial and ‘real’ options Model risk Value-at-Risk for energy Stress testing Pricing issues Credit risk – why 3000% plus volatility matters Operational risk Summary

524 524 524 525 526 536 542 543 543 545 546 547 548 548 551 555

19. IMPLEMENTATION OF PRICE TESTING Andrew Fishman Overview Objectives and defining the control framework Implementing the strategy Managing the price testing process Reporting Conclusion

557 557 559 563 573 574 578

PART 4 CAPITAL MANAGEMENT, TECHNOLOGY AND REGULATION 20. IMPLEMENTING A FIRM-WIDE RISK MANAGEMENT FRAMEWORK Shyam Venkat Introduction Understanding the risk management landscape Establishing the scope for firm-wide risk management Defining a firm-wide risk management framework Conclusion

581 581 583 585 587 612

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The Professional’s Handbook of Financial Risk Management

21. SELECTING AND IMPLEMENTING ENTERPRISE RISK MANAGEMENT TECHNOLOGIES Deborah L. Williams Introduction: enterprise risk management, a system implementation like no other The challenges The solution components Enterprise risk technology market segments Different sources for different pieces: whom to ask for what? The selection process Key issues in launching a successful implementation Conclusions

614 615 618 623 627 629 631 633

22. ESTABLISHING A CAPITAL-BASED LIMIT STRUCTURE Michael Hanrahan Introduction Purpose of limits Economic capital Types of limit Monitoring of capital-based limits Summary

635 635 635 637 644 654 655

23. A FRAMEWORK FOR ATTRIBUTING ECONOMIC CAPITAL AND ENHANCING SHAREHOLDER VALUE Michael Haubenstock and Frank Morisano Introduction Capital-at-risk or economic capital A methodology for computing economic capital Applications of an economic capital framework Applying economic capital methodologies to improve shareholder value Conclusion

614

657 657 658 659 675 680 687

24. INTERNATIONAL REGULATORY REQUIREMENTS FOR RISK MANAGEMENT (1988–1998) Mattia L. Rattaggi Introduction Quantitative capital adequacy rules for banks Risk management organization of financial intermediaries and disclosure recommendations Cross-border and conglomerates supervision Conclusion

716 723 726

25. RISK TRANSPARENCY Alan Laubsch Introduction Risk reporting External risk disclosures

740 740 740 764

INDEX

690 690 691

777

Foreword The role and importance of the risk management process (and by definition the professional risk manager) has evolved dramatically over the past several years. Until recently risk management was actually either only risk reporting or primarily a reactive function. The limited risk management tasks and technology support that did exist were usually assigned to ex-traders or product controllers with little or no support from the rest of business. The term professional risk manager was virtually unheard of in all but the largest and most sophisticated organizations. Only after a series of well-publicised losses and the accompanying failure of the firms involved did the need for sophisticated, proactive and comprehensive financial risk management processes become widely accepted. The new world of the professional risk manager is one that begins in the boardroom rather than the back office. The risk management process and professionals are now recognized as not only protecting the organization against unexpected losses, but also fundamental to the efficient allocation of capital to optimize the returns on risk. The professional risk manager, when properly supported and utilized, truly provides added value to the organization. A number of risk books were published in the latter half of the 1990s. They addressed the history of risk, how it evolved, the psychological factors that caused individuals to be good or bad risk takers and a myriad of other topics. Unfortunately, few books were written on the proper management of the growing population and complexity of risks confronting institutions. Marc Lore, Lev Borodovsky and their colleagues in the Global Association of Risk Professionals recognized this void and this book is their first attempt to fill in some of the blank spaces. KPMG is pleased to the be the primary sponsor of GARP’s The Professional’s Handbook of Financial Risk Management. We believe that this volume offers the reader practical, real world insights into leading edge practices for the management of financial risk regardless of the size and sophistication of their own organization. For those contemplating a career in risk management, the authors of this text are practising financial risk managers who provide knowledgeable insights concerning their rapidly maturing profession. No one volume can ever hope to be the ultimate last word on a topic that is evolving as rapidly as the field of financial risk management. However, we expect that this collection of articles, written by leading industry professionals who understand the risk management process, will become the industry standard reference text. We hope that after reviewing their work you will agree. Martin E. Titus, Jr Chairman, KPMG GlobeRisk

Preface Risk management encompasses a broad array of concepts and techniques, some of which may be quantified, while others must be treated in a more subjective manner. The financial fiascos of recent years have made it clear that a successful risk manager must respect both the intuitive and technical aspects (the ‘art’ and the ‘science’) of the discipline. But no matter what types of methods are used, the key to risk management is delivering the risk information in a timely and succinct fashion, while ensuring that key decision makers have the time, the tools, and the incentive to act upon it. Too often the key decision makers receive information that is either too complex to understand or too large to process. In fact, Gerald Corrigan, former President of the New York Federal Reserve, described risk management as ‘getting the right information to the right people at the right time’. History has taught us time and time again that senior decision makers become so overwhelmed with VaR reports, complex models, and unnecessary formalism that they fail to account for the most fundamental of risks. An integral part of the risk manager’s job therefore is to present risk information to the decision maker in a format which not only highlights the main points but also directs the decision maker to the most appropriate course of action. A number of financial debacles in 1998, such as LTCM, are quite representative of this problem. Risk managers must work proactively to discover new ways of looking at risk and embrace a ‘common sense’ approach to delivering this information. As a profession, risk management needs to evolve beyond its traditional role of calculating and assessing risk to actually making effective use of the results. This entails the risk manager examining and presenting results from the perspective of the decision maker, bearing in mind the knowledge base of the decision maker. It will be essential over the next few years for the risk manager’s focus to shift from calculation to presentation and delivery. However, presenting the right information to the right people is not enough. The information must also be timely. The deadliest type of risk is that which we don’t recognize in time. Correlations that appear stable break down, and a VaR model that explains earnings volatility for years can suddenly go awry. It is an overwhelming and counterproductive task for risk managers to attempt to foresee all the potential risks that an organization will be exposed to before they arise. The key is to be able to separate those risks that may hurt an institution from those that may destroy it, and deliver that information before it is too late. In summary, in order for risk management to truly add value to an organization, the risk information must be utilized in such a way as to influence or alter the business decision-making process. This can only be accomplished if the appropriate information is presented in a concise and well-defined manner to the key decision makers of the firm on a timely basis. Editors: Marc Lore and Lev Borodovsky Co-ordinating Editor: Nawal K. Roy Assistant Editors: Lakshman Chandra and Michael Hanrahan

About GARP The Global Association of Risk Professionals (GARP) is a not-for-profit, independent organization of over 10 000 financial risk management practitioners and researchers from over 90 countries. GARP was founded by Marc Lore and Lev Borodovsky in 1996. They felt that the financial risk management profession should extend beyond the risk control departments of financial institutions. GARP is now a diverse international association of professionals from a variety of backgrounds and organizations who share a common interest in the field. GARP’s mission is to serve its members by facilitating the exchange of information, developing educational programs, and promoting standards in the area of financial risk management. GARP members discuss risk management techniques and standards, critique current practices and regulation, and help bring forth potential risks in the financial markets to the attention of other members and the public. GARP seeks to provide open forums for discussion and access to information such as events, publications, consulting and software services, jobs, Internet sites, etc. To join GARP visit the web site at www.garp.com

Contributors EDITORS Ω Marc Lore Executive Vice President and Head of Firm-Wide Risk Management and Control, Sanwa Bank International, City Place House, PO Box 245, 55 Basinghall St, London EC2V 5DJ, UK Ω Lev Borodovsky Director, Risk Measurement and Management Dept, Credit Suisse First Boston, 11 Madison Avenue, New York, NY 10010-3629, USA Co-ordinating Editor Ω Nawal K. Roy Associate Vice President, Credit Suisse First Boston, 11 Madison Avenue, New York, NY10010-3629, USA Assistant Editors Ω Lakshman Chandra Business Manager, Risk Management Group, Sanwa Bank International, City Place House, PO Box 245, 55 Basinghall St, London EC2V 5DJ, UK Ω Michael Hanrahan Assistant Vice President, Head of Risk Policy, Sanwa Bank International, City Place House, PO Box 245, 55 Basinghall St, London EC2V 5DJ, UK

CONTRIBUTORS Ω Philip Best Risk specialist, The Capital Markets Company, Clements House, 14–18 Gresham St, London, EC2V 7JE, UK Ω Michel Crouhy Senior Vice President, Market Risk Management, Canadian Imperial Bank of Commerce, 161 Bay Street, Toronto, Ontario M5J 2S8, Canada Ω Mark Deans Head of Risk Management and Regulation, Sanwa Bank International, 55 Basinghall Street, London, EC2V 5DJ, UK Ω Thomas Donahoe Director, MetLife, 334 Madison Avenue, Area 2, Convent Station, NJ 07961, USA Ω Robert E. Fiedler Head of Treasury and Liquidity Risk, Methodology and Policy Group Market Risk Management, Deutsche Bank AG, D-60262 Frankfurt, Germany Ω Andrew Fishman Principal Consultant, The Capital Markets Company, Clements House, 14–18 Gresham St, London, EC2V 7JE, UK

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The Professional’s Handbook of Financial Risk Management

Ω Dan Galai Abe Gray Professor, Finance and Administration, Hebrew University, School of Business Administration in Jerusalem, Israel Ω Teri L. Geske Senior Vice President, Product Development, Capital Management Sciences, 11766 Wilshire Blvd, Suite 300, Los Angeles, CA 90025, USA Ω Kostas Giannopoulos Senior Lecturer in Finance, Westminster Business School, University of Westminster, 309 Regent St, London W1R 8AL, UK Ω Michael Haubenstock PricewaterhouseCoopers LLP, 1177 Avenue of the Americas, New York, NY 10036, USA Ω Ian Hawkins Assistant Director, Global Derivatives and Fixed Income, Westdeutsche Landesbank Girozentrale, 1211 Avenue of the Americas, New York, NY 10036, USA Ω Alvin Kuruc Senior Vice President, Infinity, a SunGard Company, 640 Clyde Court, Mountain View, CA 04043, USA Ω Alan Laubsch Partner, RiskMetrics Group, 44 Wall Street, New York, NY 10005, USA Ω Franc¸ois-Serge Lhabitant Director, UBS Ag, Aeschenplatz 6, 4002 Basel, Switzerland, and Assistant Professor of Finance, Thunderbird, the American Graduate School of International Management, Glendale, USA Ω Allan M. Malz Partner, RiskMetrics Group, 44 Wall St, NY 10005, USA Ω Bob Mark Executive Vice President, Canadian Imperial Bank of Commerce, 161 Bay Street, Toronto, Ontario, M5J 2S8, Canada Ω Frank Morisano Director, PricewaterhouseCoopers LLP, 1177 Avenue of the Americas, New York, NY 10036, USA Ω Wesley Phoa Associate, Quantitative Research, Capital Strategy Research, 11100 Santa Monica Boulevard, Los Angeles, CA 90025, USA Ω Mattia L. Rattaggi Corporate Risk Control, UBS, AG, Pelikanstrasse 6, PO Box 8090, Zurich, Switzerland Ω Richard Sage, FRM Director, Enron Europe, Flat 1, 25 Bedford Street, London WC2E 9EQ Ω P. K. Satish, CFA Managing Director, Head of Financial Engineering and Research, Askari Risk Management Solutions, State St Bank & Trust Company, 100 Avenue of the Americas, 5th Floor, New York, NY 10013, USA Ω Richard K. Skora President, Skora & Company Inc., 26 Broadway, Suite 400, New York, NY 10004, USA Ω Grant Thain Senior Vice President, Risk Management, Citizens Power LLC, 160 Federal Street, Boston, MA 02110, USA

Contributors

17

Ω Shyam Venkat Partner, PricewaterhouseCoopers LLP, 1177 Avenue of the Americas, New York, NY 10036, USA Ω Kurt S. Wilhelm, FRM, CFA National Bank Examiner, Comptroller of the Currency, 250 E St SW, Washington, DC 20219, USA Ω Deborah L. Williams Co-founder and Research Director, Meridien Research, 2020 Commonwealth Avenue, Newton, MA 02466, USA Ω Duncan Wilson Partner, Global Risk Management Practice, Ernst & Young, Rolls House, 7 Rolls Building, Fetter Lane, London EC4A 1NH, UK

Acknowledgements We would like to thank our wives Carolyn and Lisa for all their tremendous help, support, and patience. We also wish to thank all the authors who have contributed to this book. Special thanks go to Nawal Roy for his superb effort in pulling this project together and always keeping it on track, as well as his remarkable assistance with the editing process. We wish to thank Michael Hanrahan and Lakshman Chandra for writing the Introduction and their help in editing. Finally we would like to thank all GARP’s members for their continued support. Marc Lore Lev Borodovsky

Introduction The purpose of this book is to provide risk professionals with the latest standards that represent best practice in the risk industry. The book has been created with the risk practitioner in mind. While no undertaking of this size can be devoid of theory, especially considering the ongoing changes and advances within the profession itself, the heart of this book is aimed at providing practising risk managers with usable and sensible information that will assist them in their day-to-day work. The successful growth of GARP, the Global Association of Risk Professionals, has brought together thousands of risk professionals and has enabled the sharing of ideas and knowledge throughout the risk community. The existence of this forum has also made apparent that despite the growing size and importance of risk management in the financial world, there is no book in the marketplace that covers the wide array of topics that a risk manager can encounter on a daily basis in a manner that suits the practitioner. Rather, the practitioner is besieged by books that are theoretical in nature. While such books contain valuable insights that are critical to the advancement of our profession, most risk professionals are never able to utilize and test the concepts within them. Consequently, a familiar theme has emerged at various GARP meetings and conferences that a risk handbook needs to be created to provide risk practitioners with knowledge of the practices that other risk professionals have employed at their own jobs. This is especially important considering the evolving nature of risk management that can be characterized by the continuous refinement and improvement of risk management techniques, which have been driven by the increasingly complex financial environment. One of the challenges of this book has been to design its contents so that it can cover the vast area that a risk manager encounters in his or her job and at the same time be an aid to both the experienced and the more novice risk professional. Obviously, this is no easy task. While great care has been taken to include material on as many topics that a risk manager might, and even should, encounter at his or her job, it is impossible to provide answers to every single question that one might have. This is especially difficult considering the very nature of the risk management profession, as there are very few single answers that can automatically be applied to problems with any certainty. The risk management function in an organization should be a fully integrated one. While it is independent in its authority from other areas within the bank, it is at the same time dependent on them in that it receives and synthesizes information, information which is critical to its own operations, from these other areas. Consequently, the decisions made by risk managers can impact on the entire firm. The risk manager, therefore, must tailor solutions that are appropriate considering the circumstances of the institution in which he or she is working, the impact that the solution might have on other areas of the organization, and the practical considerations associated with implementation that must be factored into any chosen decision.

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The Professional’s Handbook of Financial Risk Management

This handbook has thus been designed to delve into the various roles of the risk management function. Rather than describing every possible role in exhaustive detail, the authors have attempted to provide a story line for each of the discussed topics, including practical issues that a risk manager needs to consider when tackling the subject, possible solutions to difficulties that might be encountered, background knowledge that is essential to know, and more intricate practices and techniques that are being used. By providing these fundamentals, the novice risk professional can gain a thorough understanding of the topic in question while the more experienced professional can use some of the more advanced concepts within the book. Thus the book can be used to broaden one’s own knowledge of the risk world, both by familiarizing oneself with areas in which the risk manager lacks experience and by enhancing one’s knowledge in areas in which one already has expertise. When starting this project we thought long and hard as to how we could condense the myriad ideas and topics which risk management has come to represent. We realized early on that the growth in risk management ideas and techniques over the last few years meant that we could not possibly explain them all in detail in one book. However, we have attempted to outline all the main areas of risk management to the point where a risk manager can have a clear idea of the concepts being explained. It is hoped that these will fire the thought processes to the point where a competent risk manager could tailor the ideas to arrive at an effective solution for their own particular problem. One of the obstacles we faced in bringing this book together was to decide on the level of detail for each chapter. This included the decision as to whether or not each chapter should include practical examples of the ideas being described and what it would take practically to implement the ideas. It was felt, however, that the essence of the book would be best served by not restricting the authors to a set format for their particular chapter. The range of topics is so diverse that it would not be practical in many cases to stick to a required format. It was also felt that the character of the book would benefit from a ‘free form’ style, which essentially meant giving the authors a topic and letting them loose. This also makes the book more interesting from the reader’s perspective and allowed the authors to write in a style with which they were comfortable. Therefore as well as a book that is diverse in the range of topics covered we have one with a range of perspectives towards the topics being covered. This is a facet that we think will distinguish this book from other broad-ranging risk management books. Each author has taken a different view of how his or her topic should be covered. This in turn allows us to get a feel for the many ways in which we can approach a problem in the risk management realm. Some authors have taken a high-level approach, which may befit some topics, while others have gone into the detail. In addition, there are a number of chapters that outline approaches we should take to any risk management problem. For example, Deborah Williams’ excellent chapter on enterprise risk management technologies gives us a good grounding on the approach to take in implementing a systems-based solution to a risk management problem. As well as providing practical solutions this book also covers many topics which practitioners in the financial sector would not necessarily ever encounter. We are sure that readers will find these insights into areas outside their normal everyday environments to be both interesting and informative. For any practitioners who think the subtleties of interest rate risk are complicated, we would recommend a read of Grant Thain’s chapter on energy risk management!

Introduction

21

As mentioned earlier, we could not hope to impose limits on the authors of this book while allowing them free reign to explore their particular topic. For this reason we feel that a glossary of terms for this book would not necessarily be useful. Different authors may interpret the same term in many different ways, and therefore we would ask that the reader be careful to understand the context in which a particular phrase is being used. All authors have been quite clear in defining ambiguous words or phrases, whether formally or within the body of the text, so the reader should not have too much difficulty in understanding the scope in which phrases are being used. Rather than one writer compiling the works of various authors or research papers it was felt that the best approach to producing a practical risk management guide was to let the practitioners write it themselves. Using the extensive base of contacts that was available from the GARP membership, leaders in each field were asked to produce a chapter encapsulating their knowledge and giving it a practical edge. Condensing their vast knowledge into one chapter was by no means an easy feat when we consider that all our contributors could quite easily produce a whole book on their specialist subject. Naturally The Professional’s Handbook of Financial Risk Management would not have come about without their efforts and a willingness or even eagerness to share their ideas and concepts. It should be noted that every effort was made to select people from across the risk management spectrum, from insurance and banking to the regulatory bodies and also the corporations and utility firms who are the main end-users of financial products. Each sector has its own view on risk management and this diverse outlook is well represented throughout the book. All authors are leaders in their field who between them have the experience and knowledge, both practical and theoretical, to produce the definitive risk management guide. When asking the contributors to partake in this project we were quite overwhelmed by the enthusiasm with which they took up the cause. It is essential for those of us in the risk management arena to share knowledge and disseminate what we know in order to assist each other in our one common aim of mitigating risk. This book demonstrates how the risk management profession has come of age in realizing that we have to help each other to do our jobs effectively. This is best illustrated by the manner in which all our authors contrived to ensure we understand their subject matter, thus guaranteeing that we can use their solutions to our problems. Editorial team

Part 1

Foundation of risk management

1

Derivatives basics ALLAN M. MALZ

Introduction Derivative assets are assets whose values are determined by the value of some other asset, called the underlying. There are two common types of derivative contracts, those patterned on forwards and on options. Derivatives based on forwards have linear payoffs, meaning their payoffs move one-for-one with changes in the underlying price. Such contracts are generally relatively easy to understand, value, and manage. Derivatives based on options have non-linear payoffs, meaning their payoffs may move proportionally more or less than the underlying price. Such contracts can be quite difficult to understand, value, and manage. The goal of this chapter is to describe the main types of derivatives currently in use, and to provide some understanding of the standard models used to value these instruments and manage their risks. There is a vast and growing variety of derivative products – among recent innovations are credit and energy-related derivatives. We will focus on the most widely used instruments and on some basic analytical concepts which we hope will improve readers’ understanding of any derivatives issues they confront. Because a model of derivative prices often starts out from a view of how the underlying asset price moves over time, the chapter begins with an introduction to the ‘standard view’ of asset price behavior, and a survey of how asset prices actually behave. An understanding of these issues will be helpful later in the chapter, when we discuss the limitations of some of the benchmark models employed in derivatives pricing. The chapter then proceeds to a description of forwards, futures and options. The following sections provide an introduction to the Black–Scholes model. Rather than focusing primarily on the theory underlying the model, we focus on the option market conventions the model has fostered, particularly the use of implied volatility as a metric for option pricing and the use of the so-called ‘greeks’ as the key concepts in option risk management. In recent years, much attention has focused on differences between the predictions of benchmark option pricing models and the actual patterns of option prices, particularly the volatility smile, and we describe these anomalies. This chapter concludes with a sections discussing certain option combinations, risk reversals and strangles, by means of which the market ‘trades’ the smile.

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The Professional’s Handbook of Financial Risk Management

Behavior of asset prices Efﬁcient markets hypothesis The efficient market approach to explaining asset prices views them as the present values of the income streams they generate. Efficient market theory implies that all available information regarding future asset prices is impounded in current asset prices. It provides a useful starting point for analyzing derivatives. One implication of market efficiency is that asset returns follow a random walk. The motion of the asset price has two parts, a drift rate, that is, a deterministic rate at which the asset price is expected to change over time, and a variance rate, that is, a random change in the asset price, also proportional to the time elapsed, and also unobservable. The variance rate has a mean of zero and a per-period variance equal to a parameter p, called the volatility. This assumption implies that the percent changes in the asset price are normally distributed with a mean equal to the drift rate and a variance equal to p 2. The random walk hypothesis is widely used in financial modeling and has several implications: Ω The percent change in the asset price over the next time interval is independent of both the percent change over the last time interval and the level of the asset price. The random walk is sometimes described as ‘memoryless’ for this reason. There is no tendency for an up move to be followed by another up move, or by a down move. That means that the asset price can only have a non-stochastic trend equal to the drift rate, and does not revert to the historical mean or other ‘correct’ level. If the assumption were true, technical analysis would be irrelevant. Ω Precisely because of this lack of memory, the asset price tends over time to wander further and further from any starting point. The proportional distance the asset price can be expected to wander randomly over a discrete time interval q is the volatility times the square root of the time interval, pYq. Ω Asset prices are continuous; they move in small steps, but do not jump. Over a given time interval, they may wander quite a distance from where they started, but they do it by moving a little each day. Ω Asset returns are normally distributed with a mean equal to the drift rate and a standard deviation equal to the volatility. The return distribution is the same each period. The Black–Scholes model assumes that volatility can be different for different asset prices, but is a constant for a particular asset. That implies that asset prices are homoskedastic, showing no tendency towards ‘volatility bunching’. A wild day in the markets is as likely to be followed by a quiet day as by another wild day. An asset price following geometric Brownian motion can be thought of as having an urge to wander away from any starting point, but not in any particular direction. The volatility parameter can be thought of as a scaling factor for that urge to wander. Figure 1.1 illustrates its properties with six possible time paths over a year of an asset price, the sterling–dollar exchange rate, with a starting value of USD 1.60, an annual volatility of 12%, and an expected rate of return of zero.

Empirical research on asset price behavior While the random walk is a perfectly serviceable first approximation to the behavior of asset prices, in reality, it is only an approximation. Even though most widely

Derivatives basics

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1.80

1.60

1.40 0

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Τ

Figure 1.1 Random walk.

traded cash asset returns are close to normal, they display small but important ‘nonnormalities’. In particular, the frequency and direction of large moves in asset prices, which are very important in risk management, can be quite different in real-life markets than the random walk model predicts. Moreover, a few cash assets behave very differently from a random walk. The random walk hypothesis on which the Black–Scholes model is based is a good first approximation to the behavior of most asset prices most of the time. However, even nominal asset returns that are quite close to normally distributed display small but important deviations from normality. The option price patterns discussed below reveal how market participants perceive the distribution of future asset prices. Empirical studies of the stochastic properties of nominal returns focus on the behavior of realized asset prices. The two approaches largely agree. Kurtosis The kurtosis or leptokurtosis (literally, ‘fat tails’) of a distribution is a measure of the frequency of large positive or negative asset returns. Specifically, it measures the frequency of large squared deviations from the mean. The distribution of asset returns will show high kurtosis if asset returns which are far above or below the mean occur relatively often, regardless of whether they are mostly above, mostly below, or both above and below the mean return. Kurtosis is measured in comparison with the normal distribution, which has a coefficient of kurtosis of exactly 3. If the kurtosis of an asset return distribution is significantly higher than 3, it indicates that large-magnitude returns occur more frequently than in a normal distribution. In other words, a coefficient of kurtosis well over 3 is inconsistent with the assumption that returns are normal. Figure 1.2 compares a kurtotic distribution with a normal distribution with the same variance. Skewness The skewness of a distribution is a measure of the frequency with which large returns in a particular direction occur. An asset which displays large negative returns more

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kurtotic

normal

0.50

return

0.25

0

0.25

0.50

Figure 1.2 Kurtosis.

frequently than large positive returns is said to have a return distribution skewed to the left or to have a ‘fat left tail’. An asset which displays large positive returns more frequently than large negative returns is said to have a return distribution skewed to the right or to have a ‘fat right tail’. The normal distribution is symmetrical, that is, its coefficient of skewness is exactly zero. Thus a significantly positive or negative skewness coefficient is inconsistent with the assumption that returns are normal. Figure 1.3 compares a skewed, but non-kurtotic, distribution with a normal distribution with the same variance. Table 1.1 presents estimates of the kurtosis and skewness of some widely traded assets. All the assets displayed have significant positive or negative skewness, and most also have a coefficient of kurtosis significantly greater than 3.0. The exchange rates of the Mexican peso and Thai baht vis-a`-vis the dollar have the largest coefficients of kurtosis. They are examples of intermittently fixed exchange

skewed normal

0.75

0.50

0.25

return 0

0.25

Figure 1.3 Skewness.

0.50

0.75

Derivatives basics

7

rates, which are kept within very narrow fluctuation limits by the monetary authorities. Typically, fixed exchange rates are a temporary phenomenon, lasting decades in rare cases, but only a few years in most. When a fixed exchange rate can no longer be sustained, the rate is either adjusted to new fixed level (for example, the European Monetary System in the 1980s and 1990s and the Bretton Woods system until 1971) or permitted to ‘float’, that is, find a free-market price (for example, most emerging market currencies). In either case, the return pattern of the currency is one of extremely low returns during the fixed-rate period and extremely large positive or negative returns when the fixed rate is abandoned, leading to extremely high kurtosis. The return patterns of intermittently pegged exchange rates also diminishes the forecasting power of forward exchange rates for these currencies, a phenomenon known as regime-switching or the peso problem. The term ‘peso problem’ has its origin in experience with spot and forward rates on the Mexican peso in the 1970s. Observers were puzzled by the fact that forward rates for years ‘predicted’ a significant short-term depreciation of the peso vis-a`-vis the US dollar, although the peso–dollar exchange rate was fixed. One proposed solution was that the exchange rate peg was not perfectly credible, so market participants expected a switch to a new, lower value of the peso with a positive probability. In the event, the peso has in fact been periodically permitted to float, invariably depreciating sharply. Autocorrelation of returns The distribution of many asset returns is not only kurtotic and skewed. The return distribution may also change over time and successive returns may not be independent of one another. These phenomena will be reflected in the serial correlation or autocorrelation of returns. Table 1.1 displays evidence that asset returns are not typically independently and identically distributed. The rightmost column displays a statistic which measures the likelihood that there is serial correlation between returns on a given day and returns on the same asset during the prior five trading days. High values of this statistic indicate a high likelihood that returns are autocorrelated. Table 1.1 Statistical properties of selected daily asset returns Asset

Standard deviation

Skewness

Kurtosis

Autocorrelation

0.0069 0.0078 0.0132 0.0080 0.0204 0.0065 0.0138 0.0087

0.347 0.660 ñ3.015 ñ0.461 0.249 ñ0.165 0.213 ñ0.578

2.485 6.181 65.947 25.879 4.681 4.983 3.131 8.391

6.0 8.0 56.7 87.4 41.1 21.3 26.3 25.6

Dollar–Swiss franc Dollar–yen Dollar–Mexican peso Dollar–Thai baht Crude oil Gold Nikkei 225 average S&P 500 average

Forwards, futures and swaps Forwards and forward prices In a forward contract, one party agrees to deliver a specified amount of a specified commodity – the underlying asset – to the other at a specified date in the future (the

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maturity date of the contract) at a specified price (the forward price). The commodity may be a commodity in the narrow sense, e.g. gold or wheat, or a financial asset, e.g. foreign exchange or shares. The price of the underlying asset for immediate (rather than future) delivery is called the cash or spot price. The party obliged to deliver the commodity is said to have a short position and the party obliged to take delivery of the commodity and pay the forward price for it is said to have a long position. A party with no obligation offsetting the forward contract is said to have an open position. A party with an open position is sometimes called a speculator. A party with an obligation offsetting the forward contract is said to have a covered position. A party with a closed position is sometimes called a hedger. The market sets forward prices so there are no cash flows – no money changes hands – until maturity. The payoff at maturity is the difference between forward price, which is set contractually in the market at initiation, and the future cash price, which is learned at maturity. Thus the long position gets S T ñFt,T and the short gets Ft,T ñS T , where Tñt is the maturity, in years, of the forward contract (for example, Tó1/12 for a one-month forward), S T is the price of the underlying asset on the maturity date, and Ft,T is the forward price agreed at time t for delivery at time T. Figure 1.4 illustrates with a dollar forward against sterling, initiated at a forward outright rate (see below) of USD 1.60. Note that the payoff is linearly related to the terminal value S T of the underlying exchange rate, that is, it is a constant multiple, in this case unity, of S T .

payoff 0.10

0.05

forward rate

1.45

1.50

1.55

1.60

ST

0.05 0.10 Figure 1.4 Payoff on a long forward.

No-arbitrage conditions for forward prices One condition for markets to be termed efficient is the absence of arbitrage. The term ‘arbitrage’ has been used in two very different senses which it is important to distinguish: Ω To carry out arbitrage in the first sense, one would simultaneously execute a set of transactions which have zero net cash flow now, but have a non-zero probability

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of a positive payoff without risk, i.e. with a zero probability of a negative payoff in the future. Ω Arbitrage in the second sense is related to a model of how asset prices behave. To perform arbitrage in this sense, one carries out a set of transactions with a zero net cash flow now and a positive expected value at some date in the future. Derivative assets, e.g. forwards, can often be constructed from combinations of underlying assets. Such constructed assets are called synthetic assets. Covered parity or cost-of-carry relations are relations are between the prices of forward and underlying assets. These relations are enforced by arbitrage and tell us how to determine arbitrage-based forward asset prices. Throughout this discussion, we will assume that there are no transactions costs or taxes, that markets are in session around the clock, that nominal interest rates are positive, and that unlimited short sales are possible. These assumptions are fairly innocuous: in the international financial markets, transactions costs typically are quite low for most standard financial instruments, and most of the instruments discussed here are not taxed, since they are conducted in the Euromarkets or on organized exchanges. Cost-of-carry with no dividends The mechanics of covered parity are somewhat different in different markets, depending on what instruments are most actively traded. The simplest case is that of a fictitious commodity which has no convenience value, no storage and insurance cost, and pays out no interest, dividends, or other cash flows. The only cost of holding the commodity is then the opportunity cost of funding the position. Imagine creating a long forward payoff synthetically. It might be needed by a dealer hedging a short forward position: Ω Buy the commodity with borrowed funds, paying S t for one unit of the commodity borrowed at rt,T , the Tñt-year annually compounded spot interest rate at time t. Like a forward, this set of transactions has a net cash flow of zero. Ω At time T, repay the loan and sell the commodity. The net cash flow is S T ñ[1òrt,T (Tñt)]St . This strategy is called a synthetic long forward. Similarly, in a synthetic short forward, you borrow the commodity and sell it, lending the funds at rate rt,T ,: the net cash flow now is zero. At time T, buy the commodity at price S T and return it: the net cash flow is [1òrt,T (Tñt)]St ñS T . The payoff on this synthetic long or short forward must equal that of a forward contract: S T ñ[1òrt,T (Tñt)]S T óS T ñFt,T . If it were greater (smaller), one could make a riskless profit by taking a short (long) forward position and creating a synthetic long (short) forward. This implies that the forward price is equal to the future value of the current spot price, i.e. the long must commit to paying the financing cost of the position: Ft,T ó[1òrt,T (Tñt)]St . Two things are noteworthy about this cost-of-carry formula. First, the unknown future commodity price is irrelevant to the determination of the forward price and has dropped out. Second, the forward price must be higher than the spot price, since the interest rate rt,T is positive. Short positions can be readily taken in most financial asset markets. However, in some commodity markets, short positions cannot be taken and thus synthetic short

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forwards cannot be constructed in sufficient volume to eliminate arbitrage entirely. Even, in that case, arbitrage is only possible in one direction, and the no-arbitrage condition becomes an inequality: Ft,T O[1òrt,T (Tñt)]St . Cost-of-carry with a known dividend If the commodity pays dividends or a return d T (expressed as a percent per period of the commodity price, discretely compounded), which is known in advance, the analysis becomes slightly more complicated. You can think of d t,T as the dividend rate per ‘share’ of the asset: a share of IBM receives a dividend, an equity index unit receives a basket of dividends, $100 of par value of a bond receives a coupon, etc. The d T may be negative for some assets: you receive a bill for storage and insurance costs, not a dividend check, on your 100 ounces of platinum. The amount of dividends received over Tñt years in currency units is d t,T St (Tñt). The synthetic long forward position is still constructed the same way, but in this case the accrued dividend will be received at time T in addition to the commodity price. The net cash flow is S T òd t,T St (Tñt)ñ[1òrt,T (Tñt)]St . The no-arbitrage condition is now S T ñFt,T óS T ñ[1ò(rt,T ñd t,T )(Tñt)]St . The forward price will be lower, the higher the dividends paid: Ft,T ó[1ò(rt,T ñd t,T )(Tñt)St . The forward price may be greater than, less than or equal to than the spot price if there is a dividend. The long’s implied financing cost is reduced by the dividend received. Foreign exchange Forward foreign exchange is foreign currency deliverable in the future. Its price is called forward exchange rate or the forward outright rate, and the differential of the forward minus the spot exchange rate is called the swap rate (not to be confused with the rate on plain-vanilla interest rate swaps). To apply the general mechanics of a forward transaction described above to this case, let rt,T and r*t ,T represent the domestic and foreign money-market interest rates. The spot and forward outright exchange rates are S t and Ft,T , expressed in domestic currency units per foreign currency unit. To create a synthetic long forward, Ω Borrow St /(1òr*t ,T q) domestic currency units at rate rt,T and buy 1/(1òr*t ,T q) foreign currency units. Deposit the foreign currency proceeds at rate r*t ,T . There is no net cash outlay now. Ω At time T, the foreign currency deposit has grown to one foreign currency unit, and you must repay the borrowed St (1òrt,T q) 1òr*t ,T q including interest. This implies that the forward rate is Ft,T ó

1òrt,T q St 1òr*t ,T q

Here is a numerical example of this relationship. Suppose the Euro-dollar spot

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exchange rate today is USD 1.02 per Euro. Note that we are treating the US dollar as the domestic and the Euro as the foreign currency. Suppose further that the US 1-year deposit rate is 5.75% and that the 1-year Euro deposit rate is 3.0%. The 1-year forward outright rate must then be 1.0575 1.02ó1.0472 1.03 Typically, forward foreign exchange rates are quoted not as outright rates but in terms of forward points. The points are a positive or negative quantity which is added to the spot rate to arrive at the forward outright rate, usually after dividing by a standard factor such as 10 000. In our example, the 1-year points amount to 10 000 · (1.0472ñ1.02)ó272. If Euro deposit rates were above rather than below US rates, the points would be negative. Gold leasing Market participants can borrow and lend gold in the gold lease market. Typical lenders of gold in the lease market are entities with large stocks of physical gold on which they wish to earn a rate of return, such as central banks. Typical borrowers of gold are gold dealing desks. Suppose you are a bank intermediating in the gold market. Let the spot gold price (in US dollars) be St ó275.00, let the US dollar 6-month deposit rate be 5.6% and let the 6-month gold lease rate be 2% per annum. The 6-month forward gold price must then be

Ft,T ó 1ò

0.056ñ0.02 · 275ó279.95 2

The gold lease rate plays the role of the dividend rate in our framework. A mining company sells 1000 ounces gold forward for delivery in 6 months at the market price of USD 279.95. You now have a long forward position to hedge, which you can do in several ways. You can use the futures market, but perhaps the delivery dates do not coincide with the forward. Alternatively, you can lease gold from a central bank for 6 months and sell it immediately in the spot market at a price of USD 275.00, investing the proceeds (USD 275 000) in a 6-month deposit at 5.6%. Note that there is no net cash flow now. In 6 months, these contracts are settled. First, you take delivery of forward gold from the miner and immediately return it to the central bank along with a wire transfer of USD 2750. You redeem the deposit, now grown to USD 282 700, from the bank and pay USD 279 950 to the miner. As noted above, it is often difficult to take short positions in physical commodities. The role of the lease market is to create the possibility of shorting gold. Borrowing gold creates a ‘temporary long’ for the hedger, an obligation to divest himself of gold 6 months hence, which can be used to construct the synthetic short forward needed to offset the customer business.

Futures Futures are similar to forwards in all except two important and related respects. First, futures trade on organized commodity exchanges. Forwards, in contrast, trade

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over-the-counter, that is, as simple bilateral transactions, conducted as a rule by telephone, without posted prices. Second, a forward contract involves only one cash flow, at the maturity of the contract, while futures contracts generally require interim cash flows prior to maturity. The most important consequence of the restriction of futures contracts to organized exchanges is the radical reduction of credit risk by introducing a clearinghouse as the counterparty to each contract. The clearinghouse, composed of exchange members, becomes the counterparty to each contract and provides a guarantee of performance: in practice, default on exchange-traded futures and options is exceedingly rare. Over-the-counter contracts are between two individual counterparties and have as much or as little credit risk as those counterparties. Clearinghouses bring other advantages as well, such as consolidating payment and delivery obligations of participants with positions in many different contracts. In order to preserve these advantages, exchanges offer only a limited number of contract types and maturities. For example, contracts expire on fixed dates that may or may not coincide precisely with the needs of participants. While there is much standardization in over-the-counter markets, it is possible in principle to enter into obligations with any maturity date. It is always possible to unwind a futures position via an offsetting transaction, while over-the-counter contracts can be offset at a reasonable price only if there is a liquid market in the offsetting transaction. Settlement of futures contracts may be by net cash amounts or by delivery of the underlying. In order to guarantee performance while limiting risk to exchange members, the clearinghouse requires performance bond from each counterparty. At the initiation of a contract, both counterparties put up initial or original margin to cover potential default losses. Both parties put up margin because at the time a contract is initiated, it is not known whether the terminal spot price will favor the long or the short. Each day, at that day’s closing price, one counterparty will have gained and the other will have lost a precisely offsetting amount. The loser for that day is obliged to increase his margin account and the gainer is permitted to reduce his margin account by an amount, called variation margin, determined by the exchange on the basis of the change in the futures price. Both counterparties earn a short-term rate of interest on their margin accounts. Margining introduces an importance difference between the structure of futures and forwards. If the contract declines in value, the long will be putting larger and larger amounts into an account that earns essentially the overnight rate, while the short will progressively reduce his money market position. Thus the value of the futures, in contrast to that of a forward, will depend not only on the expected future price of the underlying asset, but also on expected future short-term interest rates and on their correlation with future prices of the underlying asset. The price of a futures contract may therefore be higher or lower than the price of a congruent forward contract. In practice, however, the differences are very small. Futures prices are expressed in currency units, with a minimum price movement called a tick size. In other words, futures prices cannot be any positive number, but must be rounded off to the nearest tick. For example, the underlying for the Eurodollar futures contract on the Chicago Mercantile Exchange (CME) is a threemonth USD 1 000 000 deposit at Libor. Prices are expressed as 100 minus the Libor rate at futures contract expiry, so a price of 95.00 corresponds to a terminal Libor rate of 5%. The tick size is one basis point (0.01). The value of one tick is the increment

Derivatives basics

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in simple interest resulting from a rise of one basis point: USD 1 000 000 · 0.0001· 90 360 ó25. Another example is the Chicago Board of Trade (CBOT) US Treasury bond futures contract. The underlying is a T-bond with a face value of USD 100 000 and a minimum remaining maturity of 15 years. Prices are in percent of par, and the tick 1 of a percentage point of par. size is 32 The difference between a futures price and the cash price of the commodity is called the basis and basis risk is the risk that the basis will change unpredictably. The qualification ‘unpredictably’ is important: futures and cash prices converge as the expiry date nears, so part of the change in basis is predictable. For market participants using futures to manage exposures in the cash markets, basis risk is the risk that their hedges will offset only a smaller part of losses in the underlying asset. At expiration, counterparties with a short position are obliged to make delivery to the exchange, while the exchange is obliged to make delivery to the longs. The deliverable commodities, that is, the assets which the short can deliver to the long to settle the futures contract, are carefully defined. Squeezes occur when a large part of the supply of a deliverable commodity is concentrated in a few hands. The shorts can then be forced to pay a high price for the deliverable in order to avoid defaulting on the futures contract. In most futures markets, a futures contract will be cash settled by having the short or long make a cash payment based on the difference between the futures price at which the contract was initiated and the cash price at expiry. In practice, margining will have seen to it that the contract is already largely cash settled by the expiration date, so only a relatively small cash payment must be made on the expiration date itself. The CBOT bond futures contract has a number of complicating features that make it difficult to understand and have provided opportunities for a generation of traders: Ω In order to make many different bonds deliverable and thus avoid squeezes, the contract permits a large class of long-term US Treasury bonds to be delivered into the futures. To make these bonds at least remotely equally attractive to deliver, the exchange establishes conversion factors for each deliverable bond and each futures contract. The futures settlement is then based on the invoice price, which is equal to the futures price times the conversion factor of the bond being delivered (plus accrued interest, if any, attached to the delivered bond). Ω Invoice prices can be calculated prior to expiry using current futures prices. On any trading day, the cash flows generated by buying a deliverable bond in the cash market, selling a futures contract and delivering the purchased bond into the contract can be calculated. This set of transactions is called a long basis position. Of course, delivery will not be made until contract maturity, but the bond that maximizes the return on a long basis position, called the implied repo rate, given today’s futures and bond prices, is called the cheapest-to-deliver. Ω Additional complications arise from the T-bond contract’s delivery schedule. A short can deliver throughout the contract’s expiry month, even though the contract does not expire until the third week of the month. Delivery is a three-day procedure: the short first declares to the exchange her intent to deliver, specifies on the next day which bond she will deliver, and actually delivers the bond on the next day.

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Forward interest rates and swaps Term structure of interest rates The term structure of interest rates is determined in part by expectations of future short-term interest rates, exchange rates, inflation, and the real economy, and therefore provides information on these expectations. Unfortunately, most of the term structure of interest rates is unobservable, in contrast to prices of most assets, such as spot exchange rates or stock-index futures, prices of which are directly observable. The term structure is difficult to describe because fixed-income investments differ widely in the structure of their cash flows. Any one issuer will have debt outstanding for only a relative handful of maturities. Also, most bonds with original maturities longer than a year or two are coupon bonds, so their yields are affected not only by the underlying term structure of interest rates, but by the accident of coupon size.

Spot and forward interest rates To compensate for these gaps and distortions, one can try to build a standard representation of the term structure using observable interest rates. This is typically done in terms of prices and interest rates of discount bonds, fixed-income investments with only one payment at maturity, and spot or zero coupon interest rates, or interest rates on notional discount bonds of different maturities. The spot interest rate is the constant annual rate at which a fixed-income investment’s value must grow starting at time to reach $1 at a future. The spot or zero coupon curve is a function relating spot interest rates to the time to maturity. Most of the zero-coupon curve cannot be observed directly, with two major exceptions: bank deposit rates and short-term government bonds, which are generally discount paper. A forward interest rate is an interest rate contracted today to be paid from one future date called the settlement date to a still later future date called the maturity date. The forward curve relates forward rates of a given time to maturity to the time to settlement. There is thus a distinct forward curve for each time to maturity. For example, the 3-month forward curve is the curve relating the rates on forward 3month deposits to the future date on which the deposits settle. Any forward rate can be derived from a set of spot rates via arbitrage arguments by identifying the set of deposits or discount bonds which will lock in a rate prevailing from one future date to another, without any current cash outlay.

Forward rate agreements Forward rate agreements (FRAs) are forwards on time deposits. In a FRA, one party agrees to pay a specific interest rate on a Eurodeposit of a specified currency, maturity, and amount, beginning at a specified date in the future. FRA prices are defined as the spot rate the buyer agrees to pay on a notional deposit of a given maturity on a given settlement date. Usually, the reference rate is Libor. For example, a 3î6 (spoken ‘3 by 6’) Japanese yen FRA on ó Y 100 000 000 can be thought of as a commitment by one counterparty to pay another the difference between the contracted FRA rate and the realized level of the reference rate on a ó Y 100 000 000 deposit. Suppose the three-month and six-month Swiss franc Libor rates are respectively

Derivatives basics

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3.55% and 3.45%. Say Bank A takes the long side and Bank B takes the short side of a DM 10 000 000 3î6 FRA on 1 January at a rate of 3.30%, and suppose threemonth DM Libor is 3.50%. If the FRA were settled by delivery, Bank A would place a three-month deposit with Bank B at a rate of 3.30%. It could then close out its 90 î position by taking a deposit at the going rate of 3.50%, gaining 0.002î 360 10 000 000ó5000 marks when the deposits mature on 1 June. FRAs are generally cash-settled by the difference between the amount the notional deposit would earn at the FRA rate and the amount it would earn at the realized Libor or other reference rate, discounted back to the settlement date. The FRA is cash-settled by Bank B paying Bank A the present value of DM 5000 on 1 March. 90 With a discount factor of 1.045î 360 ó1.01125, that comes to DM 4944.38.

Swaps and forward swaps A plain vanilla interest rate swap is an agreement between two counterparties to exchange a stream of fixed interest rate payments for a stream of floating interest rate payments. Both streams are denominated in the same currency and are based on a notional principal amount. The notional principal is not exchanged. The design of a swap has three features that determine its price: the maturity of the swap, the maturity of the floating rate, and the frequency of payments. We will assume for expository purposes that the latter two features coincide, e.g. if the swap design is fixed against six-month Libor, then payments are exchanged semiannually. At initiation, the price of a plain-vanilla swap is set so its current value – the net value of the two interest payment streams, fixed and floating – is zero. The swap can be seen as a portfolio which, from the point of view of the payer of fixed interest (called the ‘payer’ in market parlance) is long a fixed-rate bond and short a floatingrate bond, both in the amount of the notional principal. The payer of floating-rate interest (called the ‘receiver’ in market parlance) is long the floater and short the fixed-rate bond. The price of a swap is usually quoted as the swap rate, that is, as the yield to maturity on a notional par bond. What determines this rate? A floating-rate bond always trades at par at the time it is issued. The fixed-rate bond, which represents the payer’s commitment in the swap, must then also trade at par if the swap is to have an initial value of zero. In other words, the swap rate is the market-adjusted yield to maturity on a par bond. Swap rates are also often quoted as a spread over the government bond with a maturity closest to that of the swap. This spread, called the swap-Treasury spread, is almost invariably positive, but varies widely in response to factors such as liquidity and risk appetites in the fixed-income markets. A forward swap is an agreement between two counterparties to commence a swap at some future settlement date. As in the case of a cash swap, the forward swap rate is the market-adjusted par rate on a coupon bond issued at the settlement date. The rate on a forward swap can be calculated from forward rates or spot rates.

The expectations hypothesis of the term structure In fixed-income markets, the efficient markets hypothesis is called the expectations hypothesis of the term structure. As is the case for efficient markets models of other asset prices, the expectations hypothesis can be readily formulated in terms of the forward interest rate, the price at which a future interest rate exposure can be locked

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in. Forward interest rates are often interpreted as a forecast of the future spot interest rate. Equivalently, the term premium or the slope of the term structure – the spread of a long-term rate over a short-term rate – can be interpreted as a forecast of changes in future short-term rates. The interpretation of forward rates as forecasts implies that an increase in the spread between long- and short-term rates predicts a rise in both short- and long-term rates. The forecasting performance of forward rates with respect to short-term rates has generally been better than that for long-term rates. Central banks in industrialized countries generally adopt a short-term interest rate as an intermediate target, but they also attempt to reduce short-term fluctuations in interest rates. Rather than immediately raising or lowering interest rates quickly by a large amount to adjust them to changes in economic conditions, they change them in small increments over a long period of time. This practice, called interest rate smoothing, results in protracted periods in which the direction and likelihood, but not the precise timing, of the next change in the target interest rate can be guessed with some accuracy, reducing the error in market predictions of short-term rates generally. Central banks’ interest rate smoothing improves the forecasting power of shortterm interest rate futures and forwards at short forecasting horizons. This is in contrast to forwards on foreign exchange, which tend to predict better at long horizons. At longer horizons, the ability of forward interest rates to predict future short-term deteriorates. Forward rates have less ability to predict turning points in central banks’ monetary stance than to predict the direction of the next move in an already established stance.

Option basics Option terminology A call option is a contract giving the owner the right, but not the obligation, to purchase, at expiration, an amount of an asset at a specified price called the strike or exercise price. A put option is a contract giving the owner the right, but not the obligation, to sell, at expiration, an amount of an asset at the exercise price. The amount of the underlying asset is called the notional principal or underlying amount. The price of the option contract is called the option premium. The issuer of the option contract is called the writer and is said to have the short position. The owner of the option is said to be long. Figure 1.5 illustrates the payoff profile at maturity of a long position in a European call on one pound sterling against the dollar with an exercise price of USD 1.60. There are thus several ways to be long an asset: Ω Ω Ω Ω

long the spot asset long a forward on the asset long a call on the asset short a put on the asset

There are many types of options. A European option can be exercised only at expiration. An American option can be exercised at any time between initiation of the contract and expiration. A standard or plain vanilla option has no additional contractual features. An

Derivatives basics

17

Intrinsic value 0.10

0.05

Spot 1.50

1.55

1.60

1.65

1.70

Figure 1.5 Payoff on a European call option.

exotic option has additional features affecting the payoff. Some examples of exotics are Ω Barrier options, in which the option contract is initiated or cancelled if the asset’s cash price reaches a specified level. Ω Average rate options, for which the option payoff is based on the average spot price over the duration of the option contract rather than spot price at the time of exercise. Ω Binary options, which have a lump sum option payoff if the spot price is above (call) or below (put) the exercise price at maturity. Currency options have an added twist: a domestic currency put is also a foreign currency put. For example, if I give you the right to buy one pound sterling for USD 1.60 in three months, I also give you the right to sell USD 1.60 at £0.625 per dollar.

Intrinsic value, moneyness and exercise The intrinsic value of a call option is the larger of the exercise price minus the current asset price or zero. The intrinsic value of a put is the larger of the current asset price minus the exercise or zero. Denoting the exercise price by X, the intrinsic value of a call is St ñX and that of a put is XñSt . Intrinsic value can also be thought of as the value of an option if it were expiring or exercised today. By definition, intrinsic value is always greater than or equal to zero. For this reason, the owner of an option is said to enjoy limited liability, meaning that the worst-case outcome for the owner of the option is to throw it away valueless and unexercised. The intrinsic value of an option is often described by its moneyness: Ω If intrinsic value is positive, the option is said to be in-the-money. Ω If the exchange rate is below the exchange rate, a call option is said to be out-ofthe-money. Ω If the intrinsic value is zero, the option is said to be at-the-money.

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If intrinsic value is positive at maturity, the owner of the option will exercise it, that is, call the underlying away from the writer. Figure 1.6 illustrates these definitions for a European sterling call with an exercise price of USD 1.60.

Intrinsic value 0.10 atthemoney

0.05

outof themoney

inthemoney Spot

1.50

1.55

1.60

1.65

1.70

Figure 1.6 Moneyness.

Owning a call option or selling a put option on an asset is like being long the asset. Owning a deep in-the-money call option on the dollar is like being long an amount of the asset that is close to the notional underlying value of the option. Owning a deep out-of-the-money call option on the dollar is like being long an amount of the asset that is much smaller than the notional underlying value of the option.

Valuation basics Distribution- and preference-free restrictions on plain-vanilla option prices Options have an asymmetric payoff profile at maturity: a change in the exchange rate at expiration may or may not translate into an equal change in option value. The difficulty in valuing options and managing option risks arises from the asymmetry in the option payoff. Options have an asymmetric payoff profile at maturity: a change in the exchange rate at expiration may or may not translate into an equal change in option value. In contrast, the payoff on a forward increases one-for-one with the exchange rate. In this section, we study some of the many true statements about option prices that do not depend on a model. These facts, sometimes called distribution- and preference-free restrictions on option prices, meaning that they don’t depend on assumptions about the probability distribution of the exchange rate or about market participants’ positions or risk appetites. They are also called arbitrage restrictions to signal the reliance of these propositions on no-arbitrage arguments. Here is one of the simplest examples of such a proposition: Ω No plain vanilla option European or American put or call, can have a negative value: Of course not: the owner enjoys limited liability.

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Another pair of ‘obvious’ restrictions is: Ω A plain vanilla European or American call option cannot be worth more than the current cash price of the asset. The exercise price can be no lower than zero, so the benefit of exercising can be no greater than the cash price. Ω A plain vanilla European or American put option cannot be worth more than the exercise price. The cash price can be no lower than zero, so the benefit of exercising can be no greater than the exercise price. Buying a deep out-of-the-money call is often likened to buying a lottery ticket. The call has a potentially unlimited payoff if the asset appreciates significantly. On the other hand, the call is cheap, so if the asset fails to appreciate significantly, the loss is relatively small. This helps us to understand the strategy of a famous investor who in mid-1995 bought deep out-of-the-money calls on a large dollar amount against the Japanese yen (yen puts) at very low cost and with very little price risk. The dollar subsequently appreciated sharply against the yen, so the option position was then equivalent to having a long cash position in nearly the full notional underlying amount of dollars. The following restrictions pertain to sets of options which are identical in every respect – time to maturity, underlying currency pair, European or American style – except their exercise prices: Ω A plain-vanilla European or American call option must be worth more than a similar option with a lower exercise price. Ω A plain-vanilla European or American put option must be worth more than a similar option with a higher exercise price. We will state a less obvious, but very important, restriction: Ω A plain-vanilla European put or call option is a convex function of the exercise price. To understand this restriction, think about two European calls with different exercise prices. Now introduce a third call option with an exercise price midway between the exercise prices of the first two calls. The market value of this third option cannot be greater than the average value of the first two. Current value of an option Prior to expiration, an option is usually worth at least its intrinsic value. As an example, consider an at-the-money option. Assume a 50% probability the exchange rate rises USD 0.01 and a 50% probability that the rate falls USD 0.01 by the expiration date. The expected value of changes in the exchange rate is 0.5 · 0.01ò0.5 · (ñ1.01)ó0. The expected value of changes in the option’s value is 0.5 · 0.01ò0.5 · 0óñ0.005. Because of the asymmetry of option payoff, only the possibility of a rising rate affects a call option’s value. Analogous arguments hold for in- and out-of-the-money options. ‘But suppose the call is in-the-money. Wouldn’t you rather have the underlying, since the option might go back out-of-the-money? And shouldn’t the option then be worth less than its intrinsic value?’ The answer is, ‘almost never’. To be precise: Ω A European call must be worth at least as much as the present value of the forward price minus the exercise price.

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This restriction states that no matter how high or low the underlying price is, an option is always worth at least its ‘intrinsic present value’. We can express this restriction algebraically. Denote by C(X,t,T ) the current (time t) market value of a European call with an exercise price X, expiring at time T. The proposition states that C(X,t,T )P[1òrt,T (Tñt)]ñ1(Ft,T ñX). In other words, the call must be worth at least its discounted ‘forward intrinsic value’. Let us prove this using a no-arbitrage argument. A no-arbitrage argument is based on the impossibility of a set of contracts that involve no cash outlay now and give you the possibility of a positive cash flow later with no possibility of a negative cash flow later. The set of contracts is Ω Buy a European call on one dollar at a cost of C(X,t,T ). Ω Finance the call purchase by borrowing. Ω Sell one dollar forward at a rate Ft,T . The option, the loan, and the forward all have the same maturity. The net cash flow now is zero. At expiry of the loan, option and forward, you have to repay [1òrt,T (Tñt)]C(X,t,T ), the borrowed option price with interest. You deliver one dollar and receive Ft,T to settle the forward contract. There are now two cases to examine: Case (i): If the option expires in-the-money (S T [X), exercise it to get the dollar to deliver into the forward contract. The dollar then costs K and your net proceeds from settling all the contracts at maturity are Ft,T ñXñ[1òrt,T (Tñt)]C(K,t,T ). Case (ii): If the option expires out-of-the-money (S T OX), buy a dollar at the spot rate S T to deliver into the forward contract. The dollar then costs S T and your net proceeds from settling all the contracts at maturity is Ft,T ñS T ñ[1òrt,T (Tñt)]C(X,t,T ). For arbitrage to be impossible, these net proceeds must be non-positive, regardless of the value of S T . Case (i): If the option expires in-the-money, the impossibility of arbitrage implies Ft,T ñXñ[1òrt,T (Tñt)]C(X,t,T )O0. Case (ii): If the option expires out-of-the-money, the impossibility of arbitrage implies Ft,T ñS T ñ[1òrt,T (Tñt)]C(X,t,T )O0, which in turn implies Ft,T ñXñ[1òrt,T (Tñt)]C(X,t,T )O0. This proves the restriction. Time value Time value is defined as the option value minus intrinsic value and is rarely negative, since option value is usually greater than intrinsic value. Time value is greatest for at-the-money-options and declines at a declining rate as the option goes in- or outof-the-money. The following restriction pertains to sets of American options which are identical in every respect – exercise prices, underlying asset – except their times to maturity. Ω A plain-vanilla American call or put option must be worth more than a similar option with a shorter time to maturity. This restriction does not necessarily hold for European options, but usually does.

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Put-call parity Calls can be combined with forwards or with positions in the underlying asset and the money market to construct synthetic puts with the same exercise price (and vice versa). In the special case of European at-the-money forward options: Ω The value of an at-the-money forward European call is equal to the value of an at-the-money forward European put. The reason is that, at maturity, the forward payoff equals the call payoff minus the put payoff. In other words, you can create a synthetic long forward by going long one ATM forward call and short one ATM forward put. The construction is illustrated in Figure 1.7.

Intrinsic value 0.10 long call payoff 0.05 short put payoff 1.55

1.60

1.65

1.70

ST

0.05 forward payoff 0.10 Figure 1.7 Put-call parity.

Option markets Exchange-traded and over-the-counter options Options are traded both on organized exchanges and over-the-counter. The two modes of trading are quite different and lead to important differences in market conventions. The over-the-counter currency and interest rate option markets have become much more liquid in recent years. Many option market participants prefer the over-the-counter markets because of the ease with which option contracts tailored to a particular need can be acquired. The exchanges attract market participants who prefer or are required to minimize the credit risk of derivatives transactions, or who are required to transact in markets with publicly posted prices. Most money-center commercial banks and many securities firms quote over-thecounter currency, interest rate, equity and commodity option prices to customers. A smaller number participates in the interbank core of over-the-counter option trading, making two-way prices to one another. The Bank for International Settlements (BIS) compiles data on the size and liquidity of the derivatives markets from national surveys of dealers and exchanges. The most recent survey, for 1995, reveals that

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over-the-counter markets dominate trading in foreign exchange derivatives and a substantial portion of the interest rate, equity, and commodity derivatives markets. We can summarize the key differences between exchange-traded and over-thecounter option contracts as follows: Ω Exchange-traded options have standard contract sizes, while over-the-counter options may have any notional underlying amount. Ω Most exchange-traded options are written on futures contracts traded on the same exchange. Their expiration dates do not necessarily coincide with those of the futures contracts, but are generally fixed dates, say, the third Wednesday of the month, so that prices on successive days pertain to options of decreasing maturity. Over-the-counter options, in contrast, may have any maturity date. Ω Exchange-traded option contracts have fixed exercise prices. As the spot price changes, such an option contract may switch from out-of-the-money to in-themoney, or become deeper or less deep in- or out-of-the-money. It is rarely exactly at-the-money. Thus prices on succesive days pertain to options with different moneyness. Ω Mostly American options are traded on the exchanges, while primarily European options, which are simpler to evaluate, are traded over-the-counter. Prices of exchange traded options are expressed in currency units. The lumpiness of the tick size is not a major issue with futures prices, but can be quite important for option prices, particularly prices of deep out-of-the-money options with prices close to zero. The price of such an option, if rounded off to the nearest basis point 1 or 32 may be zero, close to half, or close to double its true market value. This in turn can violate no-arbitrage conditions on option prices. For example, if two options with adjacent exercise prices both have the same price, the convexity requirement is violated. It can also lead to absurdly high or low, or even undefined, implied volatilities and greeks. In spite of their flexibility, there is a good deal of standardization of over-thecounter option contracts, particularly with respect to maturity and exercise prices: Ω The typical maturities correspond to those of forwards: overnight, one week, one, two, three, six, and nine months, and one year. Interest rate options tend to have longer maturities, with five- or ten-year common. A fresh option for standard maturities can be purchased daily, so a series of prices on successive days of options of like maturity can be constructed. Ω Many over-the-counter options are initiated at-the-money forward, meaning their exercise prices are set equal to the current forward rate, or have fixed deltas, so a series of prices on successive days of options of like moneyness can be constructed.

Fixed income options The prices, payoffs, and exercise prices of interest rate options can be expressed in terms of bond prices or interest rates, and the convention differs for different instruments. The terms and conditions of all exchange-traded interest rate options and some over-the-counter interest rate options are expressed as prices rather than rates. The terms and conditions of certain types of over-the-counter interest rate options are expressed as rates. A call expressed in terms of interest rates is identical to a put expressed in terms of prices.

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Caplets and floorlets are over-the-counter calls and puts on interbank deposit rates. The exercise price, called the cap rate or floor rate, is expressed as an interest rates rather than a security price. The payoff is thus a number of basis points rather than a currency amount. Ω In the case of a caplet, the payoff is equal to the cap rate minus the prevailing rate on the maturity date of the caplet, or zero, which ever is larger. For example, a three-month caplet on six-month US dollar Libor with a cap rate of 5.00% has a payoff of 50 basis points if the six-month Libor rate six months hence ends up at 5.50%, and a payoff of zero if the six-month Libor rate ends up at 4.50% Ω In the case of a floorlet, the payoff is equal to the prevailing rate on the maturity date of the cap minus the floor rate, or zero, which ever is larger. For example, a three-month floorlet on six-month US dollar Libor with a floor rate of 5.00% has a payoff of 50 basis points if the six-month Libor rate six months hence ends up at 4.50%, and a payoff of zero if the six-month Libor rate ends up at 5.50% Figure 1.8 compares the payoffs of caplets and floorlets with that of a FRA.

payoff bp 40 20 0 20 payoff on FRA payoff on caplet payoff on floorlet

40 4.6

4.8

5

5.2

5.4

ST

Figure 1.8 FRAs, caps and ﬂoors.

A caplet or a floorlet also specifies a notional principal amount. The obligation of the writer to the option owner is equal to the notional principal amount times the payoff times the term of the underlying interest rate. For example, for a caplet or floorlet on six-month Libor with a payoff of 50 basis points and a notional principal amount of USD 1 000 000, the obligation of the option writer to the owner is USD 0.0050 · 21 · 1 000 000ó2500. To see the equivalence between a caplet and a put on a bond price, consider a caplet on six-month Libor struck at 5%. This is equivalent to a put option on a sixmonth zero coupon security with an exercise price of 97.50% of par. Similarly. A floor rate of 5% would be equivalent to a call on a six-month zero coupon security with an exercise price of 97.50. A contract containing a series of caplets or floorlets with increasing maturities is called a cap or floor. A collar is a combination of a long cap and a short floor. It protects the owner against rising short-term rates at a lower cost than a cap, since

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the premium is reduced by approximately the value of the short floor, but limits the extent to which he benefits from falling short-term rates. Swaptions are options on interest rate swaps. The exercise prices of swaptions, like those of caps and floors, are expressed as interest rates. Every swaption obliges the writer to enter into a swap at the initiative of the swaption owner. The owner will exercise the swaption by initiating the swap if the swap rate at the maturity of the swaption is in his favor. A receiver swaption gives the owner the right to initiate a swap in which he receives the fixed rate, while a payer swaption gives the owner the right to initiate a swap in which he pays the fixed rate. There are two maturities involved in any fixed-income option, the maturity of the option and the maturity of the underlying instrument. To avoid confusion, traders in the cap, floor and swaption markets will describe, say, a six-month option on a twoyear swap as a ‘six-month into two year’ swaption, since the six-month option is exercised ‘into’ a two-year swap (if exercised). There are highly liquid over-the-counter and futures options on actively traded government bond and bond futures of industrialized countries. There are also liquid markets in over-the-counter options on Brady bonds.

Currency options The exchange-traded currency option markets are concentrated on two US exchanges, the International Monetary Market (IMM) division of the Chicago Mercantile Exchange and the Philadelphia Stock Exchange (PHLX). Options on major currencies such as the German mark, Japanese yen, pound sterling and Swiss franc against the dollar, and on major cross rates such as sterling–mark and mark–yen are traded. There are liquid over-the-counter markets in a much wider variety of currency pairs and maturities than on the exchanges.

Equity and commodities There are small but significant markets for over-the-counter equity derivatives, many with option-like features. There are also old and well established, albeit small, markets in options on shares of individual companies. Similarly, while most commodity options are on futures, there exists a parallel market in over-the-counter options, which are frequently components of highly structured transactions. The over-the-counter gold options market is also quite active and is structured in many ways like the foreign exchange options markets.

Option valuation Black–Scholes model In the previous section, we got an idea of the constraints on option prices imposed by ruling out the possibility of arbitrage. For more specific results on option prices, one needs either a market or a model. Options that trade actively are valued in the market; less actively traded options can be valued using a model. The most common option valuation model is the Black–Scholes model. The language and concepts with which option traders do business are borrowed from the Black–Scholes model. Understanding how option markets work and how

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market participants’ probability beliefs are expressed through option prices therefore requires some acquaintance with the model, even though neither traders nor academics believe in its literal truth. It is easiest to understand how asset prices actually behave, or how the markets believe they behave, through a comparison with this benchmark. Like any model, the Black–Scholes model rests on assumptions. The most important is about how asset prices move over time: the model assumes that the asset price is a geometric Brownian motion or diffusion process, meaning that it behaves over time like a random walk with very tiny increments. The Black–Scholes assumptions imply that a European call option can be replicated with a continuously adjusted trading strategy involving positions in the underlying asset and the risk-free bond. This, in turn, implies that the option can be valued using risk-neutral valuation, that is, by taking the mathematical expectation of the option payoff using the risk-neutral probability distribution. The Black–Scholes model also assumes there are no taxes or transactions costs, and that markets are continuously in session. Together with the assumptions about the underlying asset price’s behavior over time, this implies that a portfolio, called the delta hedge, containing the underlying asset and the risk-free bond can be constructed and continuously adjusted over time so as to exactly mimic the changes in value of a call option. Because the option can be perfectly and costlessly hedged, it can be priced by risk-neutral pricing, that is, as though the unobservable equilibrium expected return on the asset were equal to the observable forward premium. These assumptions are collectively called the Black–Scholes model. The model results in formulas for pricing plain-vanilla European options, which we will discuss presently, and in a prescription for risk management, which we will address in more detail below. The formulas for both calls and puts have the same six inputs or arguments: Ω The value of a call rises as the spot price of the underlying asset price rises (see Figure 1.9). The opposite holds for puts.

Call value 0.1 0.08 0.06 0.04 0.02 0

Spot 1.55

1.60

1.65

1.70

Figure 1.9 Call value as a function of underlying spot price.

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Ω The value of a call falls as the exercise price rises (see Figure 1.10). The opposite holds for puts. For calls and puts, the effect of a rise in the exercise price is almost identical to that of a fall in the underlying price.

Call value 0.1 0.08 0.06 0.04 0.02 0

Strike 1.55

1.60

1.65

1.70

Figure 1.10 Call value as a function of exercise price.

Ω The value of a call rises as the call’s time to maturity or tenor rises (see Figure 1.11).

Call value 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0

Days 30

90

180

360

Figure 1.11 Call value as a function of time to maturity.

Ω Call and put values rise with volatility, the degree to which the asset price is expected to wander up or down from where it is now (see Figure 1.12). Ω The call value rises with the domestic interest rate: since the call is a way to be long the asset, its value must be higher when the money market rate – the opportunity cost of being long the cash asset – rises. The opposite is true for put options, since they are an alternative method of being short an asset (see Figure 1.13).

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Call value 0.05 0.04

inthemoney

0.03 atthemoney 0.02 outthemoney

0.01 0

Volatility 0

0.05

0.1

0.15

0.2

Figure 1.12 Call value as a function of volatility.

Call value

0.03

domestic money market

0.025 0.02 dividends

0.015

Volatility 0

0.05

0.1

0.15

0.2

Figure 1.13 Call value as a function of interest and dividend rates.

Ω The call value falls with the dividend yield of the asset, e.g. the coupon rate on a bond, the dividend rate of an equity, or the foreign interest rate in the case of a currency (see Figure 1.13). The reason is that the call owner foregoes this cash income by being long the asset in the form of an option rather than the cash asset. This penalty rises with the dividend yield. The opposite is true for put options. This summary describes the effect of variations in the inputs taken one at a time, that is, holding the other inputs constant. As the graphs indicate, it is important to keep in mind that there are important ‘cross-variation’ effects, that is, the effect of, say, a change in volatility when an option is in-the-money may be different from when it is out-of-the-money. Similarly, the effect of a declining time to maturity may be different when the interest rates are high from the effect when rates are low.

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As a rough approximation, we can take the Black–Scholes formula as a reasonable approximation to the market prices of plain vanilla options. In other words, while we have undertaken to describe how the formula values change in response to the formula inputs, we have also sketched how market prices of options vary with changes in maturity and market conditions.

Implied volatility Volatility is one of the six variables in the Black–Scholes option pricing formulas, but it is the only one which is not part of the contract or an observable market price. Implied volatility is the number obtained by solving one of the Black–Scholes formulas for the volatility, given the numerical values of the other variables. Let us look at implied volatility purely as a number for a moment, without worrying about its meaning. Denote the Black–Scholes formula for the value of a call by v(St ,t,X,T,p,r,r*). Implied volatility is found from the equation C(X,t,T )óv(St ,t,X,T,p,r,r*), which sets the observed market price of an option on the left-hand side equal to the Black– Scholes value on the right-hand side. To calculate, one must find the ‘root’ p of this equation. This is relatively straightforward in a spreadsheet program and there is a great deal of commercial software that performs this as well as other option-related calculations. Figure 1.14 shows that except for deep in- or out-of-the-money options with very low volatility, the Black–Scholes value of an option is strictly increasing in implied volatility. There are several other types of volatility: Ω Historical volatility is a measure of the standard deviation of changes in an asset price over some period in the past. Typically, it is the standard deviation of daily percent changes in the asset price over several months or years. Occasionally, historical volatility is calculated over very short intervals in the very recent past: the standard deviation of minute-to-minute or second-to-second changes over the course of a trading day is called intraday volatility. Ω Expected volatility: an estimate or guess at the standard deviation of daily percent changes in the asset price for, say, the next year. Implied volatility is often interpreted as the market’s expected volatility. The interpretation of volatility is based on the Black–Scholes model assumption that the asset price follows a random walk. If the model holds true precisely, then implied volatility is the market’s expected volatility over the life of the option from which it is calculated. If the model does not hold true precisely then implied volatility is closely related to expected volatility, but may differ from it somewhat. Volatility, whether implied or historical, has several time dimensions that can be a source of confusion: Ω Standard deviations of percent changes over what time intervals? Usually, closeover-close daily percent changes are squared and averaged to calculate the standard deviation, but minute-to-minute changes can also be used, for example, in measuring intraday volatility. Ω Percent changes averaged during what period? This varies: it can be the past day, month, year or week. Ω Volatility at what per-period rate? The units of both historical and implied volatility are generally percent per year. In risk management volatility may be scaled to the

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one-day or ten-day horizon of a value-at-risk calculation. To convert an annual volatility to a volatility per some shorter period – a month or a day – multiply by the square root of the fraction of a year involved. This is called the square-rootof-time rule.

Price volatility and yield volatility In fixed-income option markets, prices are often expressed as yield volatilities rather than the price volatilities on which we have focused. The choice between yield volatility and price volatility corresponds to the choice of considering the option as written on an interest rate or on a bond price. By assuming that the interest rate the option is written on behaves as a random walk, the Black–Scholes model assumption, the Black–Scholes formulas can be applied with interest rates substituted for bond prices. The yield volatility of a fixedincome option, like the price volatility, is thus a Black–Scholes implied volatility. As is the case for other options quoted in volatility term, this practice does not imply that dealers believe in the Black–Scholes model. It means only that they find it convenient to use the formula to express prices. There is a useful approximation that relates yield and price volatilities: Yield volatility5

Price volatility Durationîyield

To use the approximation, the yield must be expressed as a decimal. Note that when yields are low, yield volatility tends to be higher.

Option risk management Option sensitivities and risks Option sensitivities (also known as the ‘greeks’) describe how option values change when the variables and parameters change. We looked at this subject in discussing the variables that go into the Black–Scholes model. Now, we will discuss their application to option risk management. We will begin by defining the key sensitivities, and then describe how they are employed in option risk management practice: Ω Delta is the sensitivity of option value to changes in the underlying asset price. Ω Gamma is the sensitivity of the option delta to changes in the underlying asset price. Ω Vega is the sensitivity of the option delta to changes in the implied volatility of the underlying asset price. Ω Theta is the sensitivity of the option delta to the declining maturity of the option as time passes. Formally, all the option sensitivities can be described as mathematical partial derivatives with respect to the factors that determine option values. Thus, delta is the first derivative of the option’s market price or fair value with respect to the price of the underlying, gamma is the second derivative with respect to the underlying price, vega is the derivative with respect to implied volatility, etc. The sensitivities

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are defined as partial derivatives, so each one assumes that the other factors are held constant. Delta Delta is important for two main reasons. First, delta is a widely used measure of the exposure of an option position to the underlying. Option dealers are guided by delta in determining option hedges. Second, delta is a widely used measure of the degree to which an option is in- or out-of-the-money. The option delta is expressed in percent or as a decimal. Market parlance drops the word ‘percent’, and drops the minus sign on the put delta: a ‘25-delta put’ is a put with a delta of ñ0.25 or minus 25%. Delta is the part of a move in the underlying price that shows up in the price or value of the option. When a call option is deep in-the-money, its value increases almost one-for-one with the underlying asset price, so delta is close to unity. When a call option is deep out-of-the-money, its value is virtually unchanged when the underlying asset price changes, so delta is close to zero. Figure 1.14 illustrates the relationship between the call delta and the rate of change of the option value with respect to the underlying asset price.

Call value 0.020 0.015 0.010 delta at 1.60 0.005 delta at 1.61 Spot 1.58

1.59

1.60

1.61

1.62

Figure 1.14 Delta as the slope of the call function.

The reader may find a summary of the technical properties of delta useful for reference. (Figure 1.15 displays the delta for a typical call option): Ω The call option delta must be greater than 0 and less than or equal to the present value of one currency unit (slightly less than 1). For example, if the discount or risk-free rate is 5%, then the delta of a three-month call cannot exceed 1.0125ñ1 (e ñ0.0125, to be exact). Ω Similarly, the put option delta must be less than 0 and greater or equal to the negative of the present value of one currency unit. For example, if the discount or risk-free rate is 5%, then the delta of a three-month call cannot be less than ñ1.0125ñ1. Ω The put delta is equal to the call delta minus the present value of one currency unit.

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Ω Put-call parity implies that puts and calls with the same exercise price must have identical implied volatilities. For example, the volatility of a 25-delta put equals the volatility of a 75-delta call.

Call delta 1.00

PV of underlying

0.75

0.50

0.25

Spot 1.45

1.50

1.55

1.60

1.65

1.70

Figure 1.15 Call delta.

Gamma Formally, gamma is the second partial derivative of the option price with respect to the underlying price. The units in which gamma is expressed depend on the units of the underlying. If the underlying is expressed in small units (Nikkei average), gamma will be a larger number. If the underlying is expressed in larger units (dollar–mark), gamma will be a smaller number. Gamma is typically greatest for at-the-money options and for options that are close to expiry. Figure 1.16 displays the gamma for a typical call option. Gamma is important because it is a guide to how readily delta will change if there is a small change in the underlying price. This tells dealers how susceptible their positions are to becoming unhedged if there is even a small change in the underlying price. Vega Vega is the exposure of an option position to changes in the implied volatility of the option. Formally, it is defined as the partial derivative of the option value with respect to the implied volatility of the option. Vega is measured in dollars or other base currency units. The change in implied volatility is measured in vols (one voló0.01). Figure 1.17 displays the vega of a typical European call option. Implied volatility is a measure of the general level of option prices. As the name suggests, an implied volatility is linked to a particular option valuation model. In the context of the valuation model on which it is based, an implied volatility has an interpretation as the market-adjusted or risk neutral estimate of the standard deviation of returns on the underlying asset over the life of the option. The most common option valuation model is the Black–Scholes model. This model is now

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Figure 1.16 Gamma.

Κ 14 12

outof themoney

inthemoney

10 8 6 4 2

atthemoney forward

0 110

115

120

125

spot

130

Figure 1.17 Vega.

familiar enough that in some over-the-counter markets, dealers quote prices in terms of the Black–Scholes implied volatility. Vega risk can be thought of as the ‘own’ price risk of an option position. Since implied volatility can be used as a measure of option prices and has the interpretation as the market’s perceived future volatility of returns, option markets can be viewed as markets for the ‘commodity’ asset price volatility: Exposures to volatility are traded and volatility price discovery occur in option markets. Vega risk is unique to portfolios containing options. In her capacity as a pure market maker in options, an option dealer maintains a book, a portfolio of purchased and written options, and delta hedges the book. This leaves the dealer with risks that are unique to options: gamma and vega. The non-linearity of the option payoff with respect to the underlying price generates gamma risk. The sensitivity of the option book to the general price of options generates vega risk.

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Because implied volatility is defined only in the context of a particular model, an option pricing model is required to measure vega. Vega is then defined as the partial derivative of the call or put pricing formula with respect to the implied volatility. Theta Theta is the exposure of an option position to changes in short-term interest rates, in particular, to the rate at which the option position is financed. Formally, it is defined as the partial derivative of the option value with respect to the interest rate. Like vega, theta is measured in dollars or other base currency units. Theta, in a sense, is not a risk, since it not random. Rather, it is a cost of holding options and is similar to cost-of-carry in forward and futures markets. However, unlike cost-of-carry, theta is not a constant rate per unit time, but depends on other factors influencing option prices, particularly moneyness and implied volatility.

Delta hedging and gamma risk Individuals and firms buy or write options in order to hedge or manage a risk to which they are already exposed. The option offsets the risk. The dealers who provide these long and short option positions to end-users take the opposite side of the option contracts and must manage the risks thus generated. The standard procedure for hedging option risks is called delta or dynamic hedging. It ordains Ω Buying or selling forward an amount of the underlying equal to the option delta when the option is entered into, and Ω Adjusting that amount incrementally as the underlying price and other market prices change and the option nears maturity. A dealer hedging, say, a short call, would run a long forward foreign exchange position consisting of delta units of the underlying currency. As the exchange rate and implied volatility changes and the option nears expiration, the delta changes, so the dealer would adjust the delta hedge incrementally by buying or selling currency. The motivation for this hedging procedure is the fact that delta, as the first derivative of the option value, is the basis for a linear approximation to the option value in the vicinity of a specific point. For small moves in the exchange rate, the value of the hedge changes in an equal, but opposite, way to changes in the value of the option position. The delta of the option or of a portfolio of options, multiplied by the underlying amounts of the options, is called the delta exposure of the options. A dealer may immediately delta hedge each option bought or sold, or hedge the net exposure of her entire portfolio at the end of the trading session. In trades between currency dealers, the counterparties may exchange forward foreign exchange in the amount of the delta along with the option and option premium. The option and forward transactions then leave both dealers with no additional delta exposure. This practice is known as crossing the delta. Delta hedging is a linear approximation of changes in the option’s value. However, the option’s value changes non-linearly with changes in the value of the underlying asset: the option’s value is convex. This mismatch between the non-linearity of the option’s value and the linearity of the hedge is called gamma risk. If you are long a call or put, and you delta hedge the option, then a perturbation

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of the exchange rate in either direction will result in a gain on the hedged position. This is referred to as good gamma. Good gamma is illustrated in Figure 1.18. The graph displays a long call position and its hedge. The option owner initially delta hedges the sterling call against the dollar at USD 1.60 by buying an amount of US dollars equal to the delta. The payoff on the hedge is the heavy negatively sloped line. If the pound falls one cent, the value of the call falls by an amount equal to line segment bc, but the value of the hedge rises by ab[bc. If the pound rises one cent, the value of the hedge falls ef, but the call rises by de[ef. Thus the hedged position gains regardless of whether sterling rises or falls.

0.020

long call d

0.015 0.010

a

0.005

b c

e

0

f hedge

0.005 1.58

1.59

1.60

1.61

1.62

Figure 1.18 Good gamma.

If you are short a call or put, and you delta hedge the option, then a perturbation of the exchange rate in either direction will result in a loss on the hedged position. This is referred to as bad gamma. Bad gamma is illustrated in Figure 1.19. The graph displays a short call position and its hedge. The option owner initially delta hedges the sterling call against the dollar at USD 1.60 by selling an amount of US dollars equal to the delta. The payoff on the hedge is the heavy positively sloped line. If the pound falls one cent, the value of the short call position rises by an amount equal to line segment ab, but the value of the hedge falls by bc[ad. If the pound rises one cent, the value of the hedge rises de, but the short call rises by ef[de. Thus the hedged position loses regardless of whether sterling rises or falls. Why not hedge with both delta and gamma? The problem is, if you hedge only with the underlying asset, you cannot get a non-linear payoff on the hedge. To get a ‘curvature’ payoff, you must use a derivative. In effect, the only way to hedge gamma risk is to lay the option position off.

The volatility smile The Black–Scholes model implies that all options on the same asset have identical implied volatilities, regardless of time to maturity and moneyness. However, there

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Value 0.005 hedge 0

d

a

0.005

b

0.010

c

e

f

0.015

short call

0.020 1.58

1.59

1.60

1.61

Spot 1.62

Figure 1.19 Bad gamma.

are systematic ‘biases’ in the implied volatilities of options on most assets. This is a convenient if somewhat misleading label, since the phenomena in question are biased only from the point of view of the Black–Scholes model, which neither dealers nor academics consider an exact description of reality: Ω Implied volatility is not constant but changes constantly. Ω Options with the same exercise price but different tenors often have different implied volatilities, giving rise to a term structure of implied volatility and indicating that market participants expect the implied volatility of short-dated options to change over time. Ω Out-of-the money options often have higher implied volatilities than at-the-money options, indicating that the market perceives asset prices to be kurtotic, that is, the likelihood of large moves is greater than is consistent with the lognormal distribution. Ω Out-of-the money call options often have implied volatilities which differ from those of equally out-of the money puts, indicating that the market perceives the distribution of asset prices to be skewed. The latter two phenomena are known as the volatility smile because of the characteristic shape of the plot of implied volatilities of options of a given tenor against the delta or against the exercise price.

The term structure of implied volatility A rising term structural volatility indicates that market participants expect shortterm implied volatility to rise or that they are willing to pay more for protection against near-term asset price volatility. Figure 1.20 illustrates a plot of the implied volatilities of options on a 10-year US dollar swap (swaptions) with option maturities between one month and 5 years. Typically, longer-term implied volatilities vary less over time than shorter-term volatilities on the same asset. Also typically, there are only small differences among the historical averages of implied volatilities of different maturities. Longer-term

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Σ

16 15 14 13

0

1

2

3

4

5

Τ

Figure 1.20 Term structure of volatility.

volatilities are therefore usually closer to the historical average of implied volatility than shorter-term implied volatilities. Shorter-term implied volatilities may be below the longer-term volatilities, giving rise to an upward-sloping term structure, or above the longer-term volatilities, giving rise to a downward-sloping term structure. Downward-sloping term structures typically occur when shocks to the market have abruptly raised volatilities across the term structure. Short-term volatility responds most readily, since shocks are usually expected to abate over the course of a year.

The volatility smile Option markets contain much information about market perceptions that asset returns are not normal. Earlier we discussed the differences between the actual behavior of asset returns and the random walk hypothesis which underlies the Black–Scholes option pricing model. The relationship between in- or out-of-the money option prices and those of at-the-money options contains a great deal of information about the market perception of the likelihood of large changes, or changes in a particular direction, in the cash price. The two phenomena of curvature and skewness generally are both present in the volatility smile, as in the case depicted in Figure 1.21. The chart tells us that options which pay off if asset prices fall by a given amount are more highly valued than options which pay off if asset prices rise. That could be due to a strong market view that asset prices are more likely to fall than to rise; it could also be due in part to market participants seeking to protect themselves against losses from falling rates or losses in other markets associated with falling rates. The market is seeking to protect itself particularly against falling rather than rising rates. Also, the market is willing to pay a premium for option protection against large price changes in either direction. Different asset classes have different characteristic volatility smiles. In some markets, the typical pattern is highly persistent. For example, the negatively sloping smile for S&P 500 futures options illustrated in Figure 1.22 has been a virtually

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Figure 1.21 Volatility smile in the foreign exchange market.

permanent feature of US equity index options since October 1987, reflecting market eagerness to protect against a sharp decline in US equity prices. In contrast, the volatility smiles of many currency pairs such as dollar–mark have been skewed, depending on market conditions, against either the mark or the dollar.

Σ 33

futures price

32

31

30 Strike 1025

1030

1035

1040

1045

1050

1055

1060

Figure 1.22 Volatility smile in the S&P futures market.

Over-the-counter option market conventions Implied volatility as a price metric One of the most important market conventions in the over-the-counter option markets is to express option prices in terms of the Black–Scholes implied volatility. This convention is employed in the over-the-counter markets for options on currencies, gold, caps, floors, and swaptions. The prices of over-the-counter options on

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bonds are generally expressed in bond price units, that is, percent of par. It is generally used only in price quotations and trades among interbank dealers, rather than trades between dealers and option end-users. The unit of measure of option prices under this convention, is implied volatility at an annual percent rate. Dealers refer to the units as vols. If, for example, a customer inquires about dollar–mark calls, the dealer might reply that ‘one-month at-themoney forward dollar calls are 12 at 12.5’, meaning that the dealer buys the calls at an implied volatility of 12 vols and sells them at 12.5 vols. It is completely straightforward to express options in terms of vols, since as Figure 1.12 makes clear, a price in currency units corresponds unambiguously to any price in vols. When a deal is struck between two traders in terms of vols, the appropriate Black–Scholes formula is used to translate into a price in currency units. This requires the counterparties to agree on the remaining market data inputs to the formula, such as the current forward price of the underlying and the money market rate. Although the Black–Scholes pricing formulas are used to move back and forth between vols and currency units, this does not imply that dealers believe in the Black–Scholes model. The formulas, in this context, are divorced from the model and used only as a metric for price. Option dealers find this convenient because they are in the business of trading volatility, not the underlying. Imagine the dealer maintaining a chalkboard displaying his current price quotations for options with different underlying assets, maturities and exercise prices. If the option prices are expressed in currency units, than as the prices of the underlying assets fluctuate in the course of the trading day, the dealer will be obliged to constantly revise the option prices. The price fluctuations in the underlying may, however, be transitory and random, related perhaps to the idiosyncrasies of order flow, and have no significance for future volatility. By expressing prices in vols, the dealer avoids the need to respond to these fluctuations.

Delta as a metric for moneyness The moneyness of an option was defined earlier in terms of the difference between the underlying asset price and the exercise price. Dealers in the currency option markets often rely on a different metric for moneyness, the option delta, which we encountered earlier as an option sensitivity and risk management tool. As shown in Figure 1.23, the call delta declines monotonically as exercise price rises – and the option goes further out-of-the-money – so dealers can readily find the unique exercise price corresponding to a given delta, and vice versa. The same holds for puts. Recall that the delta of a put is equal to the delta of a call with the same exercise price, minus the present value of one currency unit (slightly less than one). Often, exercise prices are set to an exchange rate such that delta is equal to a round number like 25% or 75%. A useful consequence of put–call parity (discussed above) is that puts and calls with the same exercise price must have identical implied volatilities. The volatility of a 25-delta put is thus equal to the volatility of a 75-delta call. Because delta varies as market conditions, including implied volatility, the exercise price corresponding to a given delta and the difference between that exercise price and the current forward rate vary over time. For example, at an implied volatility of 15%, the exercise prices of one-month 25-delta calls and puts are about 3% above and below the current forward price, while at an implied volatility of 5%, they are about 1% above and below.

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Exercise price

1.70 1.60 1.50 1.40 Call delta 0

0.2

0.4

0.6

0.8

1

Figure 1.23 Exercise price as a function of delta.

The motivation for this convention is similar to that for using implied volatility: it obviates the need to revise price quotes in response to transitory fluctuations in the underlying asset price. In addition, delta can be taken as an approximation to the market’s assessment of the probability that the option will be exercised. In some trading or hedging techniques involving options, the probability of exercise is more relevant than the percent difference between the cash and exercise prices. For example, a trader taking the view that large moves in the asset price are more likely than the market is assessing might go long a 10-delta strangle. He is likely to care only about the market view that there is a 20% chance one of the component options will expire in-the-money, and not about how large a move that is.

Risk reversals and strangles A combination is an option portfolio containing both calls and puts. A spread is a portfolio containing only calls or only puts. Most over-the-counter currency option trading is in combinations. The most common in the interbank currency option markets is the straddle, a combination of an at-the-money forward call and an atthe-money forward put with the same maturity. Straddles are also quite common in other over-the-counter option markets. Figure 1.24 illustrates the payoff at maturity of a sterling–dollar straddle struck at USD 1.60. Also common in the interbank currency market are combinations of out-of-themoney options, particularly the strangle and the risk reversal. These combinations both consist of an out-of-the-money call and out-of-the-money put. The exercise price of the call component is higher than the current forward exchange rate and the exercise price of the put is lower. In a strangle, the dealer sells or buys both out-of-the-money options from the counterparty. Dealers usually quote strangle prices by stating the implied volatility at which they buy or sell both options. For example, the dealer might quote his selling price as 14.6 vols, meaning that he sells a 25-delta call and a 25-delta put at an implied volatility of 14.6 vols each. If market participants were convinced that exchange rates move as random walks, the out-of-the-money options would have the

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Payoff 0.1 0.08 0.06 0.04 0.02 0 1.55

1.60

1.65

1.70

Terminal price

Figure 1.24 Straddle payoff.

same implied volatility as at-the-money options and strangle spreads would be zero. Strangles, then, indicate the degree of curvature of the volatility smile. Figure 1.25 illustrates the payoff at maturity of a 25-delta dollar–mark strangle.

Payoff 0.08 0.06 0.04 0.02 0 1.50

1.55

1.60

1.65

1.70

Terminal price

Figure 1.25 Strangle payoff.

In a risk reversal, the dealer exchanges one of the options for the other with the counterparty. Because the put and the call are generally not of equal value, the dealer pays or receives a premium for exchanging the options. This premium is expressed as the difference between the implied volatilities of the put and the call. The dealer quotes the implied volatility differential at which he is prepared to exchange a 25-delta call for a 25-delta put. For example, if dollar–mark is strongly expected to fall (dollar depreciation), an options dealer might quote dollar–mark risk reversals as follows: ‘one-month 25-delta risk reversals are 0.8 at 1.2 mark calls over’. This means he stands ready to pay a net premium of 0.8 vols to buy a 25-delta

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mark call and sell a 25-delta mark put against the dollar, and charges a net premium of 1.2 vols to sell a 25-delta mark call and buy a 25-delta mark put. Figure 1.26 illustrates the payoff at maturity of a 25-delta dollar–mark risk reversal.

Payoff

0.05 0 0.05

1.50

1.55

1.60

1.65

1.70

Terminal price

Figure 1.26 Risk reversal payoff.

Risk reversals are commonly used to hedge foreign exchange exposures at low cost. For example, a Japanese exporter might buy a dollar-bearish risk reversal consisting of a long 25-delta dollar put against the yen and short 25-delta dollar call. This would provide protection against a sharp depreciation of the dollar, and provide a limited zone – that between the two exercise prices – within which the position gains from a stronger dollar. However, losses can be incurred if the dollar strengthens sharply. On average during the 1990s, a market participant desiring to put on such a position has paid the counterparty a net premium, typically amounting to a few tenths of a vol. This might be due to the general tendency for the dollar to weaken against the yen during the floating exchange rate period, or to the persistent US trade deficit with Japan.

2

Measuring volatility KOSTAS GIANNOPOULOS

Introduction The objective of this chapter is to examine the ARCH family of volatility models and its use in risk analysis and measurement. An overview of unconditional and conditional volatility models is provided. The former is based on constant volatilities while the latter uses all information available to produce current (or up-do-date) volatility estimates. Unconditional models are based on rigorous assumptions about the distributional properties of security returns while the conditional models are less rigorous and treat unconditional models as a special case. In order to simplify the VaR calculations unconditional models make strong assumptions about the distributional properties of financial time series. However, the convenience of these assumptions is offset by the overwhelming evidence found in the empirical distribution of security returns, e.g. fat tails and volatility clusters. VaR calculations based on assumptions that do not hold, underpredict uncommonly large (but possible) losses. In this chapter we will argue that one particular type of conditional model (ARCH/ GARCH family) provides more accurate measures of risk because it captures the volatility clusters present in the majority of security returns. A comprehensive review of the conditional heteroskedastic models is provided. This is followed by an application of the models for use in risk management. This shows how the use of historical returns of portfolio components and current portfolio weights can generate accurate estimates of current risk for a portfolio of traded securities. Finally, the properties of the GARCH family of models are treated rigorously in the Appendix.

Overview of historical volatility models Historical volatility is a static measure of variability of security returns around their mean; they do not utilize current information to update their estimate. This implies that the mean, variance and covariance of the series are not allowed to vary over time in response to current information. This is based on the assumption that the returns series is stationary. That is, the series of returns (and, in general, any time series) has constant statistical moments over different periods. If the series is stationary then the historical mean and variance are well defined and there is no conceptual problem in computing them. However, unlike stationary time series, the mean of the sample may become a function of its length when the series is nonstationary. Non-stationarity, as this is referred to, implies that the historical means,

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variances and covariances estimates of security return are subject to error estimation. While sample variances and covariances can be computed, it is unlikely that it provides any information regarding the true unconditional second moments since the latter are not well defined. The stationarity of the first two moments in security return has been challenged for a long time and this has called into question the historical volatility models. This is highlighted in Figure 2.1 which shows the historical volatility of FTSE-100 Index returns over a 26- and 52-week interval.

Figure 2.1 Historical volatility of FTSE-100 index.

Figure 2.1 shows that when historical volatilities are computed on overlapping samples and non-equal length time periods, they change over time. This is attributable to the time-varying nature of historical means, variances and covariances caused by sampling error.

Assumptions VaR is estimated using the expression VaRóPpp 2.33t

(2.1)

where Ppp 2.33 is Daily-Earnings-at-Risk (DEaR), which describes the magnitude of the daily losses on the portfolio at a probability of 99%; pp is the daily volatility (standard deviation) of portfolio returns; t is the number of days, usually ten, over which the VaR is estimated; p is usually estimated using the historical variance– covariance. In the historical variance–covariance approach, the variances are defined in 1 T 2 p t2 ó ; e tñi (2.2) T ió1

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where e is the residual returns (defined as actual returns minus the mean). On the other hand, the ES approach is expressed as ê 2 2 p t2 óje tñ1 ò(1ñj) ; jie tñi

(2.3)

ió1

where 0OjO1 and is defined as the decay factor that attaches different weights over the sample period of past squared residual returns. The ES approach attaches greater weight to more recent observations than observations well in the past. The implication of this is that recent shocks will have a greater impact on current volatility than earlier ones. However, both the variance–covariance and the ES approaches require strong assumptions regarding the distributional properties of security returns. The above volatility estimates relies on strong assumptions on the distributional properties of security returns, i.e. they are independently and identically distributed (i.i.d. thereafter). The identically distributed assumption ensures that the mean and the variance of returns do not vary across time and conforms to a fixed probability assumption. The independence assumption ensures that speculative price changes are unrelated to each other at any point of time. These two conditions form the basis of the random walk model. Where security returns are i.i.d. and the mean and the variance of the distribution are known, inferences made regarding the potential portfolio losses will be accurate and remain unchanged over a period of time. In these circumstances, calculating portfolio VaR only requires one estimate, the standard deviation of the change in the value of the portfolio. Stationarity in the mean and variance implies that the likelihood of a specified loss will be the same for each day. Hence, focusing on the distributional properties of security returns is of paramount importance to the measurement of risk. In the next section, we examine whether these assumptions are valid.

The independence assumption Investigating the validity of the independence assumption has focused on testing for serial correlation in changes in price. The general conclusion reached by past investigations is that successive price changes are autocorrelated, but are too weak to be of economic importance. This observation has led most investigations to accept the random walk hypothesis. However, evidence has shown that a lack of autocorrelation does not imply the acceptance of the independence assumption. Some investigations has found that security returns are governed by non-linear processes that allow successive price changes to be linked through the variance. This phenomenon was observed in the pioneering investigations of the 1960s, where large price movements were followed by large price movements and vice versa. More convincing evidence is provided in later investigations in their more rigorous challenge to the identical and independence assumptions. In the late 1980s much attention was focused on using different time series data ranging from foreign exchange currencies to commodity prices to test the validity of the i.i.d. assumptions. Another development in those investigations is the employment of more sophisticated models such as the conditional heteroskedastic models (i.e. ARCH/GARCH) used to establish the extent to which the i.i.d. assumption is violated. These type of models will be examined below.

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The form of the distribution The assumption of normality aims to simplify the measurement of risk. If security returns are normally distributed with stable moments, then the two parameters, the mean and the variance, are sufficient to describe it. Stability implies that the probability of a specified portfolio loss is the same for each day. These assumptions, however, are offset by the overwhelming evidence suggesting the contrary which is relevant to measuring risk, namely the existence of fat tails or leptokurtosis in the distribution that exceeds those of the normal. Leptokurtosis in the distribution of security returns was reported as early as the 1950s. This arises where the empirical distribution of daily changes in stock returns have more observations around the means and in the extreme tails than that of a normal distribution. Consequently, the non-normality in the distribution has led some studies to suggest that attaching alternative probability distributions may be more representative of the data and observed leptokurtosis. One such is the Paretian distribution, which has the characteristic exponent. This is a peakness parameter that measures the tail of the distribution. However, the problem with this distribution is that unlike normal distribution, the variance and higher moments are not defined except as a special case of the normal. Another distribution suggested is the Student t-distribution which has fatter tails than that of the normal, assuming that the degrees of freedom are less than unity. While investigations have found that t-distributions adequately describe weekly and monthly data, as the interval length in which the security returns are measured increases, the t-distribution tends to converge to a normal. Other investigations have suggested that security returns follow a mixture of distributions where the distribution is described as a combination of normal distributions that possess different variances and possible different means.

Non-stationarity in the distribution The empirical evidence does not support the hypothesis of serial dependence (autocorrelation) in security returns. This has caused investigators to focus more directly on non-stationary nature of the two statistical moments, the mean and the variance, which arises when both moments vary over time. Changes in the means and variance of security returns is an alternative explanation to the existence of leptokurtosis in the distribution. Investigations that focus on the non-stationarity in the means have been found to be inconclusive in their findings. However, more concrete evidence is provided when focusing on non-stationarity with respect to the variance. It has been found that it is the conditional dependency in the variance that causes fatter tails in the unconditional distribution that is greater than that of the conditional one. Fatter tails and the non-stationarity in the distribution in the second moments are caused by volatility clustering in the data set. This occurs where rates of return are characterized by very volatile and tranquil periods. If the variances are not stationary then the formula DEaRt does not hold.1

Conditional volatility models The time-varying nature of the variance may be captured using conditional time series models. Unlike historical volatility models, this class of statistical models make more effective use of the information set available at time t to estimate the means

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and variances as varying with time. One type of model that has been successful in capturing time-varying variances and covariances is a state space technique such as the Kalman filter. This is a form of time-varying parameter model which bases regression estimates on historical data up to and including the current time period. A useful attribute of this model lies in its ability to describe historical data that is generated from state variables. Hence the usefulness of Kalman filter in constructing volatility forecasts on the basis of historical data. Another type of model is the conditional heteroskedastic models such as the Autoregressive Conditional Heteroskedastic (ARCH) and the generalized ARCH (GARCH). These are designed to remove the systematically changing variance from the data which accounts for much of the leptokurtosis in the distribution of speculative price changes. Essentially, these models allow the distribution of the data to exhibit leptokurtosis and hence are better able to describe the empirical distribution of financial data.

ARCH models: a review The ARCH(1) The ARCH model is based on the principal that speculative price changes contain volatility clusters. Suppose that a security’s returns Yt can be modeled as: Yt ómt dòet

(2.4)

where mt is a vector of variables with impact on the conditional mean of Yt , and et is the residual return with zero mean, Etñ1 (et )ó0, and variance Etñ1 (e t2 )óh t . The conditional mean are expected returns that changes in response to current information. The square of the residual return, often referred to as the squared error term, e t2 , can be modeled as an autoregressive process. It is this that forms the basis of the Autoregressive Conditional Heteroskedastic (ARCH) model. Hence the first-order ARCH can be written: 2 h t óuòae tñ1

(2.5)

where u[0 and aP0, and h t denotes the time-varying conditional variance of Yt . 2 This is described as a first-order ARCH process because the squared error term e tñ1 is lagged one period back. Thus, the conditional distribution of et is normal but its conditional variance is a linear function of past squared errors. ARCH models can validate scientifically a key characteristic of time series data that ‘large changes tend to be followed by large changes – of either sign – and small changes tend to be followed by small changes’. This is often referred to as the clustering effect and, as discussed earlier, is one of the major explanations behind the violation of the i.i.d. assumptions. The usefulness of ARCH models relates to its 2 ability to deal with this effect by using squared past forecast errors e tñ1 to predict future variances. Hence, in the ARCH methodology the variance of Yt is expressed as a (non-linear) function of past information, it validates earlier concerns about heteroskedastic stock returns and meets a necessary condition for modeling volatility as conditional on past information and as time varying.

Higher-order ARCH The way in which equations (2.4) and (2.5) can be formulated is very flexible. For

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example, Yt can be written as an autoregressive process, e.g. Yt óYtñ1 òet , and/or can include exogenous variables. More important is the way the conditional variance h t can be expressed. The ARCH order of equation (2.5) can be increased to express today’s conditional variance as a linear function of a greater amount of past information. Thus the ARCH(q) can be written as: 2 2 h t óuòa1 e tñ1 ò. . . òaq e tñq

(2.6)

Such a model specification is generally preferred to a first-order ARCH since now the conditional variance depends on a greater amount of information that goes as far as q periods in the past. With an ARCH(1) model the estimated h t is highly unstable since single large (small) surprises are allowed to drive h t to inadmissible extreme values. With the higher-order ARCH of equation (2.6), the memory of the process is spread over a larger number of past observations. As a result, the conditional variance changes more slowly, which seems more plausible.

Problems As with the ARCH(1), for the variance to be computable, the sum of a1 , . . . , aq in equation (2.6) must be less than one. Generally, this is not a problem with financial return series. However, a problem that often arises with the higher-order ARCH is that not every one of the a1 , . . . , aq coefficients is positive, even if the conditional variance computed is positive at all times. This fact cannot be easily explained in economic terms, since it implies that a single large residual return could drive the conditional variance negative. The model in equation (2.6) has an additional disadvantage. The number of parameters increases with the ARCH order and makes the estimation process formidable. One way of attempting to overcome this problem is to express past errors in an ad hoc linear declining way. Equation (2.6) can then be written as: 2 h t óuòa ; wk e tñk

(2.7)

where wk , kó1, . . . , q and &wk ó1 are the constant linearly declining weights. While the conditional variance is expressed as a linear function of past information, this model attaches greater importance to more recent shocks in accounting for the most of the h t changes. This model was adopted by Engle which has been found to give a good description of the conditional variance in his 1982 study and a later version published in 1983. The restrictions for the variance equation parameters remain as in ARCH(1).

GARCH An alternative and more flexible lag structure of the ARCH(q) model is provided by the GARCH(p, q), or Generalized ARCH model: p

q

2 ò ; bj h tñj h t óuò ; ai e tñi ió1

(2.8)

jó1

with ió1, . . . , p and jó1, . . . , q. In equation (2.8) the conditional variance h t is a function of both past innovations and lagged values conditional variance, i.e.

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h tñ1 , . . . , htñp . The lagged conditional variance is often referred to as old news because it is defined as p

q

2 ò ; bj h tñjñ1 h tñj óuò ; ae tñiñ1 ió1

(2.9)

jó1

In other words, if i, jó1, then htñj in equation (2.8) and formulated in equation (2.9) is explainable by past information and the conditional variance at time tñ2 or lagged two periods back. In order for a GARCH(p, q) model to make sense the next condition must be satisfied: 1PG; ai ò; bj H[0 In this situation the GARCH(p, q) corresponds to an infinite-order ARCH process with exponentially decaying weights for longer lags. Researchers have suggested that loworder GARCH(p, q) processes may have properties similar to high-order ARCH but with the advantage that they have significantly fewer parameters to estimate. Empirical evidence also exists that a low-order GARCH model fits as well or even better than a higher-order ARCH model with linearly declining weights. A large number of empirical studies has found that a GARCH(1, 1) is adequate for most financial time series.

GARCH versus exponential smoothing (ES) In many respects the GARCH(1, 1) representation shares many features of the popular exponential smoothing to which can be added the interpretation that the level of current volatility is a function of the previous period’s volatility and the square of the previous period’s returns. These two models have many similarities, i.e. today’s volatility is estimated conditionally upon the information set available at each period. Both the GARCH(1, 1) model in equation (2.8) and the (ES) model in equation (2.7) use the last period’s returns to determine current levels of volatility. Subsequently, it follows that today’s volatility is forecastable immediately after yesterday’s market closure.2 Since the latest available information set is used, it can be shown that both models will provide more accurate estimators of volatility than the use of historical volatility. However, there are several differences in the operational characteristics of the two models. The GARCH model, for example, uses two independent coefficients to estimate the impact the variables have in determining current volatility, while the ES 2 model uses only one coefficient and forces the variables e tñ1 and h tñ1 to have a unit effect on current period volatility. Thus, a large shock will have longer lasting impact on volatility using the GARCH model of equation (2.8) than the ES model of (2.3) The terms a and b in GARCH do not need to sum to unity and one parameter is not the complement of the other. Hence, it avoids the potential for simultaneity bias in the conditional variance. Their estimation is achieved by maximizing the likelihood function.3 This is a very important point since the values of a and b are critical in determining the current levels of volatility. Incorrect selection of the parameter values will adversely affect the estimation of volatility. The assumption that a and b sum to unity is, however, very strong and presents an hypothesis that can be tested rather than a condition to be imposed. Acceptance of the hypothesis that a and b sum to unity indicates the existence of an Integrated GARCH process or I-GARCH. This is a specification that characterizes the conditional variance h t as exhibiting a

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49

nonstationary component. The implication of this is that shocks in the lagged 2 squared error term e tñi will have a permanent effect on the conditional variance. Furthermore, the GARCH model has an additional parameter, u, that acts as a floor and prevents the volatility dropping to below that level. In the extreme case where a and bó0, volatility is constant and equal to u. The value of u is estimated together with a and b using maximum likelihood estimation and the hypothesis uó0 can be tested easily. The absence of the u parameter in the ES model allows volatility, after a few quiet trading days, to drop to very low levels.

Forecasting with ARCH models In view of the fact that ARCH models make better use of the available information set than standard time series methodology by allowing excess kurtosis in the distribution of the data, the resulting model fits the observed data set better. However, perhaps the strongest argument in favor of ARCH models lies in their ability to predict future variances. The way the ARCH model is constructed it can ‘predict’ the next period’s variance without uncertainty. Since the error term at time t, et , is known we can rewrite equation (2.5) as h tò1 óuòat2

(2.10)

Thus, the next period’s volatility is found recursively by updating the last observed error in the variance equation (2.5). For the GARCH type of models it is possible to deliver a multi-step-ahead forecast. For example, for the GARCH(1, 1) it is only necessary to update forecasts using: E(h tòs D't )óuò(aòb)E(h tòsñ1 D't )

(2.11)

where u, a and b are GARCH(1, 1) parameters of equation (2.8) and are estimated using the data set available, until period t. Of course, the long-run forecasting procedure must be formed on the basis that the variance equation has been parameterized. For example, the implied volatility at time t can enter as an exogenous variable in the variance equation: E(h tò1 D't )óuòae t2 òbh t òdp t2

if só1

(2.12a)

E(h tòs D't )óuòdp t2 ò(aòb)E(h tòsñ1 D't )

if sP2

(2.12b)

where the term p t2 is the implied volatility of a traded option on the same underlying asset, Y.

ARCH-M (in mean) The ARCH model can be extended to allow the mean of the series to be a function of its own variance. This parameterization is referred to as the ARCH-in-Mean (or ARCH-M) model and is formed by adding a risk-related component to the return equation, in other words, the conditional variance h t . Hence, equation (2.4) can be rewritten as: Yt ómt dòjh t òet

(2.13)

Therefore the ARCH-M model allows the conditional variance to explain directly the dependent variable in the mean equation of (2.13). The estimation process consists

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of solving equations (2.13) and (2.5) recursively. The term jh t has a time-varying impact on the conditional mean of the series. A positive j implies that the conditional mean of Y increases as the conditional variance increases. The (G)ARCH-M model specification is ideal for equity returns since it provides a unified framework to estimate jointly the volatility and the time-varying expected return (mean) of the series by the inclusion of the conditional variance in the mean equation. Unbiased estimates of assets risk and return are crucial to the mean variance utility approach and other related asset pricing theories. Finance theory states that rational investors should expect a higher return for riskier assets. The parameter j in equation (2.13) can be interpreted as the coefficient of relative risk aversion of a representative investor and when recursively estimated, the same coefficient jh t can be seen as the time-varying risk premium. Since, in the presence of ARCH, the variance of returns might increase over time, the agents will ask for greater compensation in order to hold the asset. A positive j implies that the agent is compensated for any additional risk. Thus, the introduction of h t into the mean is another non-linear function of past information. Since the next period’s variance, h tò1 , is known with certainty the next period’s return forecast, E(Ytò1 ), can be obtained recursively. Assuming that mt is known we can rearrange equation (2.13) as E[Ytò1 DIt ]•mtò1 ó'tò1 dò#h tò1

(2.14)

Thus, the series’ expectation at tò1 (i.e. one period ahead into the future) is equal to the series’ conditional mean, mtò1 , at the same period. Unlike the unconditional mean, kóE(Y ), which is not a random variable, the conditional mean is a function of past volatility, and because it uses information for the period up to t, can generally be forecasted more accurately. In contrast to the linear GARCH model, consistent estimation of the parameter estimates of an ARCH-M model are sensitive to the model specification. A model is said to be misspecified in the presence of simultaneity bias in the conditional mean equation as defined in equation (2.13). This arises because the estimates for the parameters in the conditional mean equation are not independent of the estimates of the parameters in the conditional variance. Therefore, it has been argued that a misspecification in the variance equation will lead to biased and inconsistent estimates for the conditional mean equation.

Using GARCH to measure correlation Historical variance–covariance matrix: problems In risk management, the monitoring of changes in the variance and covariance of the assets that comprises a portfolio is an extensive process of distinguishing shifts over a period of time. Overseeing changes in the variance (or risk) of each asset and the relationship between the assets in a portfolio through the variance is achieved using the variance–covariance matrix. For the variance and covariance estimates in the matrix to be reliable it is necessary that the joint distributions of security returns are multivariate normal and stationary. However, as discussed earlier, investigations have found that the distribution of speculative price changes are not normally distributed and exhibit fat tails. Consequently, if the distribution of return for

Measuring volatility

51

individual series fails to satisfy the i.i.d. assumptions, it is inconceivable to expect the joint distribution to do so. Therefore, forecasts based on past extrapolations of historical estimates must be viewed with skepticism. Hence, the usefulness of conditional heteroskedastic models in multivariate setting to be discussed next.

The multivariate (G)ARCH model All the models described earlier are univariate. However, risk analysis of speculative prices examines both an asset’s return volatility and its co-movement with other securities in the market. Modeling the co-movement among assets in a portfolio is best archived using a multivariate conditional heteroskedastic model which accounts for the non-normality in the multivariate distribution of speculative price changes. Hence, the ARCH models can find more prominent use in empirical finance if they could describe risk in a multivariate context. There are several reasons for examining the variance parameter of a multivariate distribution of financial time series after modeling within the ARCH framework. For example, covariances and the beta coefficient which in finance theory is used as a measure of the risk could be represented and forecasted in the same way as variances. The ARCH model has been extended to a multivariate case using different parameterizations. The most popular is the diagonal one where each element of the conditional variance–covariance matrix Ht is restricted to depend only on its own lagged squared errors.4 Thus, a diagonal bivariate GARCH(1, 1) is written as: Y1,t ó'T1,t d1 òe1,t

(2.15a)

Y2,t ó'T2,t d2 òe2,t

(2.15b)

with

e1,t

e2,t

~N(0, Ht )

where Y1,t , Y2,t is the return on the two assets over the period (tñ1, t). Conditional on the information available up to time (tñ1), the vector with the surprise errors et is assumed to follow a bivariate normal distribution with zero mean and conditional variance–covariance matrix Ht . Considering a two-asset portfolio, the variance– covariance matrix Ht can be decomposed as 2 h 1,t óu1 òa1 e 1,tñ1 òb1 h 1,tñ1

h 12,t óu12 òa12 e1,tñ1 e2,tñ1 òb12 h 12,tñ1 2 h 2,t óu2 òa2 e 2,tñ1 òb2 h 2,tñ1

(2.16a) (2.16b) (2.16c)

Here h1,t and h2,t can be seen as the conditional variances of assets 1 and 2 respectively. These are expressed as past realizations of their own squared distur2 bances denoted as e 1,tñ1 . The covariance of the two return series, h12,t , is a function of the cross-product between past disturbances in the two assets. The ratio h 1,t h 2,t /h 12,t forms the correlation between assets 1 and 2. However, using the ARCH in a multivariate context is subject to limitations in modeling the variances and covariances in a matrix, most notably, the number of

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variances and covariances that are required to be estimated. For example, in a widely diversified portfolio containing 100 assets, there are 4950 conditional covariances and 100 variances to be estimated. Any model used to update the covariances must keep to the multivariate normal distribution otherwise the risk measure will be biased. Given the computationally intensive nature of the exercise, there is no guarantee that the multivariate distribution will hold.

Asymmetric ARCH models The feature of capturing the volatility clustering in asset returns has made (G)ARCH models very popular in empirical studies. Nevertheless, these models are subject to limitations. Empirical studies have observed that stock returns are negatively related with changes in return volatility. Volatility tends to rise when prices are falling, and to fall when prices are rising. Hence, the existence of asymmetry in volatility, which is often referred to as the leverage effect. All the models described in the previous section assumed that only the magnitude and not the sign of past returns determines the characteristics of the conditional variance, h t . In other words, the ARCH and GARCH models described earlier do not discriminate negative from positive shocks which has been shown to have differing impacts on the conditional variance.

Exponential ARCH (EGARCH) To address some of the limitations an exponential ARCH parameterization or EGARCH has been proposed. The variance of the residual error term for the EGARCH(1, 1) is given by ln(h t )óuòb ln(h tñ1 )òcttñ1 ò{(Dttñ1 Dñ(2/n)1/2 )

(2.17)

where tt óet /h t (standardized residual). Hence, the logarithm of the conditional variance ln(h t ) at period t is a function of the logarithm of the conditional period variance, lagged one period back ln(h tñ1 ), the standarized value of the last residual error, ttñ1 , and the deviation of the absolute value of ttñ1 from the expected absolute value of the standardized normal variate, (2/n)1/2. The parameter c measures the impact ‘asymmetries’ on the last period’s shocks have on current volatility. Thus, if c\0 then negative past errors have a greater impact on the conditional variance ln(h t ) than positive errors. The conditional variance h t is expressed as a function of both the size and sign of lagged errors.

Asymmetric ARCH (AARCH) An ARCH model with properties similar to those of EGARCH is the asymmetric ARCH (AARCH). In its simplest form the conditional variance h t can be written as h t óuòa(etñ1 òc)2 òbh tñ1

(2.18)

The conditional variance parameterization in equation (2.18) is a quadratic function of one-period-past error (etñ1 òc)2 . Since the model of equation (2.18) and higherorder versions of this model formulation still lie within the parametric ARCH, it can therefore, be interpreted as the quadratic projection of the squared series on the information set. The (G)AARCH has similar properties to the GARCH but unlike the latter, which

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53

explores only the magnitude of past errors, the (G)AARCH allows past errors to have an asymmetric effect on h t . That is, because c can take any value, a dynamic asymmetric effect of positive and negative lagged values of et on h t is permitted. If c is negative the conditional variance will be higher when etñ1 is negative than when it is positive. If có0 the (G)AARCH reduces to a (G)ARCH model. Therefore, the (G)AARCH, like the EGARCH, can capture the leverage effect present in the stock market data. When the sum of ai and bj (where ió1, . . . , p and jó1, . . . , q are the orders of the (G)AARCH(p, q) process) is unity then analogously to the GARCH, the model is referred to as Integrated (G)AARCH. As with the GARCH process, the autocorrelogram and partial autocorrelogram of the squares of e, as obtained by an AR(1), can be used to identify the {p, q} orders. In the case of the (G)AARCH(1, 1), the unconditional variance of the process is given by p 2 ó(uòc2a)/(1ñañb)

(2.19)

Other asymmetric speciﬁcations GJR or threshold: 2 2 ¯ tñ1 e tñ1 h t óuòbh tñ1 òae tñ1 òcS

(2.20)

¯ t ó1 if et \0, S ¯ t ó0 otherwise. where S Non-linear asymmetric GARCH: h t óuòbh tñ1 òa(etñ1 òch tñ1 )2

(2.21)

h t óuòbh tñ1 òa(etñ1 /h tñ1 òc)2

(2.22)

VGARCH:

If the coefficient c is positive in the threshold model then negative values of etñ1 have an additive impact on the conditional variance. This allows asymmetry on the conditional variance in the same way as EGARCH and AARCH. If có0 the model reduces to a GARCH(1, 1). Similarly, as in the last two models, the coefficient c measures the asymmetric effect where the negative errors have a greater impact on the variance when c\0.

Identiﬁcation and diagnostic tests for ARCH ARCH models are almost always estimated using the maximum likelihood method and with the use of computationally expensive techniques. Although linear GARCH fits well with a variety of data series and is less sensitive to misspecification, others like the ARCH-M requires that the full model be correctly specified. Thus identification tests for ARCH effects need to be carried out before, and misspecification tests after the estimation process are necessary for proper ARCH modeling.

Identiﬁcation tests One test proposed by Engle is based on the Lagrange Multiplier (LM) principle. To perform the test only estimates of the homoskedastic model are required. Assume that the AR(p) is a stationary process which generates the set of returns, Yt , such

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that {e}~i.i.d. and N(0, p 2 ) and that Yñpñ1 , . . . , Y0 is an initial fixed part of this series. Subsequent inference will be based on a conditional likelihood function. The least squares estimators of the parameters in the above process are denoted by kˆ , a ˆ2, . . . , a ˆ p , where pˆ 2 is an estimator of p 2 , and setting zˆ 2 ó(ñ1, Ytñ1 , . . . , ˆ1, a Ytñ2 , . . . , Ytñp )@. When H0 for homoskedasticity is tested against an ARCH(p) process then the LM statistic is asymptotically equivalent to nR2 from the auxiliary regression 2 òet ˆe t2 óaò&aeˆ tñi

for ió1, . . . , p

H0 : a1 óa2 ó. . . óap ó0

(2.23)

H1 : ai Ö0 Under H0 the LMónR 2 has a chi-squared distribution denoted as s2(p), where p represents the number of lags. However, when the squared residuals are expressed as linearly declining weights of past squared errors the LM test for ARCH, which will follow a s2(1) distribution, will be 2 òet ˆe t2 óaòa1 &w1 ˆe tñ1

(2.24)

where wi are the weights which decline at a constant rate. The above test has been extended to deal with the bivariate specification of the ARCH models. When the model is restricted to the diagonal representation, then 2 3N(R 12 òR 22 òR 12 ) is distributed as s2(3p), where R2 is the coefficient of determination. 2 The terms R 1 , R 22 stand for the autoregression of squared residuals for each of the 2 denotes the autoregression of the covariance for the two series two assets, and R 12 residuals. Researchers also suggest that the autocorrelogram and partial autocorrelogram for ˆe t2 can be used to specify the GARCH order {p, q} in a similar way to that used to identify the order of a Box–Jenkins ARMA process. The Box–Pierce Q-statistic for the normalized squared residuals (i.e. (eˆ t2 /h t )) can be used as a diagnostic test against higher-order specifications for the variance equation. If estimations are performed under both the null and alternative hypotheses, likelihood ratio (LR) tests can be obtained by LRóñ2(ML(h0 )ñML(ha ))~s2(k) where ML(h0 ) and ML(ha ) are the ML function evaluations and k is the number of restrictions in the parameters.

Diagnostic tests Correct specification of h t is very important. For example, because h t relates future variances to current information, the accuracy of forecast depends on the selection of h t . Therefore, diagnostic tests should be employed to test for model misspecification. Most of the tests dealing with misspecification examine the properties of the standardized residuals defined as ˆe t* óeˆt htñ1 which is designed to make the residual returns conform to a normal distribution. If the model is correctly specified the standardized residuals should behave as a white noise series. That is because under the ARCH model ˆe t* óeˆt htñ1 D'tñ1~N(0,1)

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55

For example, the autocorrelation of squared residual returns, ˆe t2 , or normalized squared residuals (eˆ *2 )2 may reveal a model failure. More advanced tests can also be used to detect non-linearities in (eˆ 2* )2 . An intuitively appealing test has been suggested by Pagan and Schwert. They propose regressing ˆe t2 against a constant and h t , the estimates of the conditional variance, to test the null hypothesis that the coefficient b in equation (2.25) is equal to unity: ˆe t2 óaòbh t òlt

(2.25)

If the forecasts are unbiased, aó0 and bó1. A high R 2 in the above regression indicates that the model has high forecasting power for the variance.

An alternative test A second test, based on the Ljung–Box Q-statistic, tests the standardized squared residuals (Y 12/p 2 ) for normality and hence the acceptance of the i.i.d. assumptions. Large values of the Q-statistic could be regarded as evidence that the standardized residuals violate the i.i.d. assumption and hence normality, while low values of the Q-statistic would provide evidence that the standardized residuals are independent. In other words, when using this test failure to accept the maintained hypothesis of independence would indicate that the estimated variance has not removed all the clusters of volatility. This in turn would imply that the data still holds information that can be usefully translated into volatility.

An application of ARCH models in risk management In this section, we provide an application of how to use GARCH techniques in a simplified approach to estimate a portfolio’s VaR. We will show that the use of historical returns of portfolio components and current weights can produce accurate estimates of current risk for a portfolio of traded securities. Information on the time series properties of returns of the portfolio components is transformed into a conditional estimate of current portfolio volatility without needing to use complex time series procedures. Stress testing and correlation stability are discussed in this framework.

A simpliﬁed way to compute a portfolio’s VaR Traditional VaR models require risk estimates for the portfolio holdings, i.e. variance and correlations. Historical volatilities are ill-behaved measures of risk because they presume that the statistical moments of the security returns remain constant over different time periods. Conditional multivariate time series techniques are more appropriate since they use past information in a more efficient way to compute current variances and covariances. One such model which fits well with financial data is the multivariate GARCH. Its use, however, is restricted to few assets at a time. A simple procedure to overcome the difficulties of inferring current portfolio volatility from past data, is to utilize the knowledge of current portfolio weights and historical returns of the portfolio components in order to construct a hypothetical series of the returns that the portfolio would have earned if its current weights had

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been kept constant in the past. Let Rt be the Nî1 vector (R1,t , R2,t , . . . , Rn ,t ) where Ri ,t is the return on the ith asset over the period (tñ1, t) and let W be the Nî1 vector of the portfolio weights over the same period. The historical returns of our current portfolio holdings are given by: Yt óW TRt

(2.26)

In investment management, if W represents actual investment holdings, the series Y can be seen as the historical path of the portfolio returns.5 The portfolio’s risk and return trade-off can be expressed in terms of the statistical moments of the multivariate distribution of the weighted investments as: E(Yt )óE(W TR)óm

(2.27a)

var(Yt )óW T )Wóp 2

(2.27b)

where ) is the unconditional variance–covariance matrix of the returns of the N assets. A simplified way to find the portfolio’s risk and return characteristics is by estimating the first two moments of Y: E(Y)óm

(2.28a)

var(Y)óE[YñE(Y)]2 óp 2

(2.28b)

Hence, if historical returns are known the portfolio’s mean and variance can be found as in equation (2.28). This is easier than equation (2.27) and still yields identical results. The method in (2.28b) can easily be deployed in risk management to compute the value at risk at any given time t. However, p 2 will only characterize current conditional volatility if W has not changed. If positions are being modified, the series of past returns, Y, needs to be reconstructed and p 2 , the volatility of the new position, needs to be re-estimated as in equation (2.28b). This approach has many advantages. It is simple, easy to compute and overcomes the dimensionality and bias problems that arise when the NîN covariance matrix is being estimated. On the other hand, the portfolio’s past returns contain all the necessary information about the dynamics that govern aggregate current investment holdings. In this chapter we will use this approach to make the best use of this information.6 For example, it might be possible to capture the time path of portfolio (conditional) volatility using conditional models such as GARCH.

An empirical investigation In this example we selected closing daily price indices from thirteen national stock markets7 over a period of 10 years, from the first trading day of 1986 (2 January) until the last trading day of 1995 (29 December). The thirteen markets have been selected in a way that matches the regional and individual market capitalization of the world index. Our data sample represents 93.3% of the Morgan Stanley International world index capitalization.8 The Morgan Stanley Capital International (MSCI) World Index has been chosen as a proxy to the world portfolio. To illustrate how our methodology can be used to monitor portfolio risk we constructed a hypothetical portfolio, diversified across all thirteen national markets of our data sample. To form this hypothetical portfolio we weighted each national index in proportion to its

Measuring volatility

57 Table 2.1 Portfolio weights at December 1995 Country

Our portfolio

World index

0.004854 0.038444 0.041905 0.018918 0.013626 0.250371 0.024552 0.007147 0.010993 0.012406 0.036343 0.103207 0.437233

0.004528 0.035857 0.039086 0.017645 0.012709 0.233527 0.022900 0.006667 0.010254 0.011571 0.033898 0.096264 0.407818

Denmark France Germany Hong Kong Italy Japan Netherlands Singapore Spain Sweden Switzerland UK USA

capitalization in the world index as on December 1995. The portfolio weights are reported in Table 2.1. The 10-year historical returns of the thirteen national indexes have been weighted according to the numbers in the table to form the returns of our hypothetical portfolio. Since portfolio losses need to be measured in one currency, we expressed all local returns in US dollars and then formed the portfolio’s historical returns. Table 2.2 reports the portfolio’s descriptive statistics together with the Jarque–Bera normality test. The last column is the probability that our portfolio returns are generated from a normal distribution. Table 2.2 Descriptive statistics of the portfolio historical returns Mean (p.a.) 10.92%

Std dev. (p.a.)

Skewness

Kurtosis

JB test

p-value

12.34%

ñ2.828

62.362

3474.39

0.000

Notes: The test for normality is the Jarque–Bera test, N((p3 )2 /6ò(p4 ñ3)2 /24). The last column is the signiﬁcance level.

Modeling portfolio volatility The excess kurtosis in this portfolio is likely to be caused by changes in its variance. We can capture these shifts in the variance by employing GARCH modeling. For a portfolio diversified across a wide range of assets, the non-constant volatility hypothesis is an open issue.9 The LM test and the Ljung–Box statistic are employed to test this hypothesis. The test statistics with significance levels are reported in Table 2.3. Both tests are highly significant, indicating that the portfolio’s volatility is not constant over different days and the squares of the portfolio returns are serially correlated.10 Table 2.3 Testing for ARCH

Test statistic p-value

LM test (6)

Ljung–Box (6)

352.84 (0.00)

640.64 (0.00)

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One of the advantages that the model in (2.28) is that is simplifies econometric modeling on the portfolio variance. Because we have to model only a single series of returns we can select a conditional volatility model that bests fits the data. There are two families of models, the GARCH and SV (Stochastic Volatility), which are particularly suited to capturing changes in volatility of financial time series. To model the hypothetical portfolio volatility, we use GARCH modeling because it offers wide flexibility in the mean and variance specifications and its success in modeling conditional volatility has been well documented in the financial literature. We tested for a number of different GARCH parameterizations and found that an asymmetric GARCH(1, 1)-ARMA(0, 1) specification best fits11 our hypothetical portfolio. This is defined as: Yt ó'etñ1 òet

et ~NI(0, h t )

(2.29a)

h t óuòa(etñ1 òc)2 òbh tñ1

(2.29b)

The parameter estimates reported in Table 2.4 are all highly significant, confirming that portfolio volatility can be better modeled as conditionally heteroskedastic. The coefficient a that measures the impact of last period’s squared innovation, e, on today’s variance is found to be positive and significant; in addition, (uòac2 )/ (1ñañb)[0 indicating that the unconditional variance is constant. Table 2.4 Parameter estimates of equation (2.30) Series Estimate t-statistic

'

u

a

b

c

Likelihood

0.013 (2.25)

1.949 (3.15)

0.086 (6.44)

0.842 (29.20)

ñ3.393 (5.31)

ñ8339.79

Moreover, the constant volatility model, which is the special case of aóbó0, can be rejected. The coefficient c that captures any asymmetries in volatility that might exist is significant and negative, indicating that volatility tends to be higher when the portfolio’s values are falling. Figure 2.2 shows our hypothetical portfolio’s conditional volatility over the 10-year period. It is clear that the increase in portfolio volatility occurred during the 1987 crash and the 1990 Gulf War.

Diagnostics and stress analysis Correct model specification requires that diagnostic tests be carried out on the fitted residual, ˆe. Table 2.5 contains estimates of the regression: ˆe t2 óaòbhˆ t

(2.30)

with t-statistics given in parentheses. Table 2.5 Diagnostics on the GARCH residuals

Statistic Signiﬁcance

a

b

R2

Q(6) on eˆt

Q(6) on eˆ 2t

JB

ñ7.054 (1.73)

1.224 (1.87)

0.373

3.72 (0.71)

10.13 (0.12)

468.89 (0.00)

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59

Figure 2.2 Portfolio volatility based on bivariate GARCH.

The hypotheses that aó0 and bó1 cannot be rejected at the 95% confidence level, indicating that our GARCH model produces a consistent estimator for the portfolio’s time-varying variance. The uncentered coefficient of determination, R 2 in equation (2.30), measures the fraction of the total variation of everyday returns explained by the estimated conditional variance, and has a value 37.3%. Since the portfolio conditional variance uses the information set available from the previous day, the above result indicates that our model, on average, can predict more than one third of the next day’s squared price movement. The next two columns in Table 2.5 contain the Ljung–Box statistic of order 6 for the residuals and squared residuals. Both null hypotheses, for serial correlation and further GARCH effect, cannot be rejected, indicating that our model has removed the volatility clusters from the portfolio returns and left white noise residuals. The last column contains the Jarque–Bera normality test on the standardized residuals. Although these residuals still deviate from the normal distribution, most of the excess kurtosis has been removed, indicating that our model describes the portfolio returns well. Figure 2.3 illustrates the standardized empirical distribution of these portfolio returns which shows evidence of excess kurtosis in the distribution. The area under the continuous line represents the standardized empirical distribution of our hypothetical portfolio.12 The dashed line shows the shape of the distribution if returns were normally distributed. The values on the horizontal axis are far above and below the (3.0, ñ3.0) range, which is due to very large daily portfolio gains and losses. In Figure 2.4 the standardized innovations of portfolio returns are shown. The upper and lower horizontal lines represent the 2.33 standard deviations (0.01 probability) threshold. We can see that returns are moving randomly net of any volatility clusters. Figure 2.5 shows the Kernel distribution of these standardized innovations against

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Figure 2.3 Empirical distribution of standardized portfolio returns.

Figure 2.4 Portfolio stress analysis (standardized conditional residuals).

the normal distribution. It is apparent that the distribution of these scaled innovations is rather non-normal with values reaching up to fourteen standard deviations. However, when the outliers to the left (which reflect the large losses during the 1987 crash), are omitted, the empirical distribution of the portfolio residual returns matches that of a Gaussian.

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Figure 2.5 Empirical distribution of portfolio conditional distribution.

These results substantiate the credibility of the volatility model in equation (2.29) in monitoring portfolio risk. Our model captures all volatility clusters present in the portfolio returns, removes a large part of the excess kurtosis and leaves residuals approximately normal. Furthermore, our method for estimating portfolio volatility using only one series of past returns is much faster to compute than the variance–covariance method and provides unbiased volatility estimates with higher explanatory power.

Correlation stability and diversiﬁcation beneﬁts In a widely diversified portfolio, e.g. containing 100 assets, there are 4950 conditional covariances and 100 variances to be estimated. Furthermore, any model used to update the covariances must keep the multivariate features of the joint distribution. With a large matrix like that, it is unlikely to get unbiased estimates13 for all 4950 covariances and at the same time guarantee that the joint multivariate distribution still holds. Obviously, errors in covariances as well as in variances will affect the accuracy of our portfolio’s VaR estimate and will lead to wrong risk management decisions. Our approach estimates conditionally the volatility of only one univariate time series, the portfolio’s historical returns, and so overcomes all the above problems. Furthermore, since it does not require the estimation of the variance–covariance matrix, it can be easily computed and can handle an unlimited number of assets. On the other hand it takes into account all changes in assets’ variances and covariances. Another appealing property of our approach is to disclose the impact that the overall changes in correlations have on portfolio volatility. It can tell us what proportion an increase/decrease in the portfolio’s VaR is due to changes in asset variances or correlations. We will refer to this as correlation stability. It is known that each correlation coefficient is subject to changes at any time.

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Nevertheless, changes across the correlation matrix may not be correlated and their impact on the overall portfolio risk may be diminished. Our conditional VaR approach allows us to attribute any changes in the portfolio’s conditional volatility to two main components: changes in asset volatilities and changes in asset correlations. If h t is the portfolio’s conditional variance, as estimated in equation (2.25), its time-varying volatility is pt ó h t . This is the volatility estimate of a diversified portfolio at period t. By setting all pair-wise correlation coefficients in each period equal to 1.0, the portfolio’s volatility becomes the weighted volatility of its asset components. Conditional volatilities of the individual asset components can be obtained by fitting a GARCH-type model for each return series. We denote the volatility of this undiversified portfolio as st . The quantity 1ñ(pt /st ) tells us what proportion of portfolio volatility has been diversified away because of imperfect correlations. If that quantity does not change significantly over time, then the weighted overall effect of time-varying correlations is invariant and we have correlation stability. The correlation stability shown in Figure 2.6 can be used to measure the risk manager’s ability to diversify portfolio’s risk. On a well-diversified (constantly weighted) portfolio, the quantity 1ñ(pt /st ) should be invariant over different periods. It has been shown that a portfolio invested only in bonds is subject to greater correlation risk than a portfolio containing commodities and equities because of the tendency of bonds to fall into step in the presence of large market moves.

Figure 2.6 Portfolio correlation stability: volatility ratio (diversiﬁed versus non-diversiﬁed).

The ‘weighted’ effect of changes in correlations can also be shown by observing the diversified against the undiversified portfolio risk. Figure 2.7 illustrates how the daily annualized standard deviation of our hypothetical portfolio behaves over the tested period. The upper line shows the volatility of an undiversified portfolio; this is the volatility the same portfolio would have if all pair-wise correlation coefficients of the

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Figure 2.7 Portfolio conditional volatility (diversiﬁed versus non-diversiﬁed).

assets invested were 1.0 at all times. The undiversified portfolio’s volatility is simply the weighted average of the conditional volatilities of each asset included in the portfolio. Risk managers who rely on average standard historical risk measures will be surprised by the extreme values of volatility a portfolio may produce in a crash. Our conditional volatility estimates provide early warnings about the risk increase and therefore are a useful supplement to existing risk management systems. Descriptive statistics for diversified and undiversified portfolio risk are reported in Table 2.6. These range of volatility are those that would have been observed had the portfolio weights been effective over the whole sample period. Due to the diversification of risk, the portfolio’s volatility is reduced by an average of 40%.14 During the highly volatile period of the 1987 crash, the risk is reduced by a quarter. Table 2.6 Portfolio risk statistic Portfolio risk

Minimum

Maximum

Mean

Diversiﬁed Undiversiﬁed

0.0644 0.01192

0.2134 0.2978

0.0962 0.1632

Portfolio VaR and ‘worst case’ scenario Portfolio VaR A major advantage that our methodology has is that it forecasts portfolio volatility recursively upon the previous day’s volatility. Then it uses these volatility forecasts to calculate the VaR over the next few days. We discuss below how this method is implemented. By substituting the last day’s residual return and variance in equation (2.29b) we can estimate the portfolio’s volatility for day tò1 and by taking the expectation, we

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can estimate recursively the forecast for longer periods. Hence, the portfolio volatility forecast over the next 10 days is h tòi óuòa(et òc)2 òbh t

if ió1

(2.31a)

h tòi/t óuòac2(aòb)h tòiñ1/t

if i[1

(2.31b)

Therefore, when portfolio volatility is below average levels, the forecast values will be rising.15 The portfolio VaR that will be calculated on these forecasts will be more realistic about possible future losses.

Figure 2.8 Portfolio VaR.

Figure 2.8 shows our hypothetical portfolio’s VaR for 10 periods of length between one and 10 days. The portfolio VaR is estimated at the close of business on 29 December 1995. To estimate the VaR we obtain volatility forecasts for each of the next business days, as in equation (2.31). The DEaR is 1.104% while the 10-day VaR is 3.62%. Worst-case scenario VaR measures the market risk of a portfolio in terms of the frequency that a specific loss will be exceeded. In risk management, however, it is important to know the size of the loss rather than the number of times the losses will exceed a predefined threshold. The type of analysis which tells us the worst than can happen to a portfolio’s value over a given period is known as the ‘worst-case scenario’ (WCS). Hence, the WCS is concerned with the prediction of uncommon events which, by definition, are bound to happen. The WCS will answer the question, how badly will it hit?

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For a VaR model, the probability of exceeding a loss at the end of a short period is a function of the last day’s volatility and the square root of time (assuming no serial correlation). Here, however, the issue of fat tails arises. It is unlikely that there exists a volatility model that predicts the likelihood and size of extreme price moves. For example, in this study we observed that the GARCH model removes most of the kurtosis but still leaves residuals equal to several standard deviations. Given that extreme events, such as the 1987 crash, have a realistic probability of occurring again at any time, any reliable risk management system must account for them. The WCS is commonly calculated by using structured Monte Carlo simulation (SMC). This method aims to simulate the volatilities and correlations of all assets in the portfolio by using a series of random draws of the factor shocks (etò1 ). At each simulation run, the value of the portfolio is projected over the VaR period. By repeating the process several thousand times, the portfolio returns density function is found and the WCS is calculated as the loss that corresponds to a very small probability under that area. There are three major weaknesses with this analysis. First, there is a dimensionality problem which also translates to computation time. To overcome this, RiskMetrics proposes to simplify the calculation of the correlation matrix by using a kind of factorization. Second, the SMC method relies on a (timeinvariant) correlation structure of the data. But as we have seen, security covariances are changing over different periods and the betas tend to be higher during volatile periods like that of the 1987 crash. Hence, correlations in the extremes are higher and the WCS will underestimate the risk. Finally, the use of a correlation matrix requires returns in the Monte Carlo method to follow an arbitrary distribution. In practice the empirical histogram of returns is ‘smoothed’ to fit a known distribution. However, the WCS is highly dependent on a good prediction of uncommon events or catastrophic risk and the smoothing of the data leads to a cover-up of extreme events, thereby neutralizing the catastrophic risk. Univariate Monte Carlo methods can be employed to simulate directly various sample paths of the value of the current portfolio holdings. Hence, once a stochastic process for the portfolio returns is specified, a set of random numbers, which conform to a known distribution that matches the empirical distribution of portfolio returns, is added to form various sample paths of portfolio return. The portfolio VaR is then estimated from the corresponding density function. Nevertheless, this method is still exposed to a major weakness. The probability density of portfolio residual returns is assumed to be known.16 In this application, to further the acceptance of the VaR methodology, we will assess its reliability under conditions likely to be uncorrelated in financial markets. The logical method to investigate this issue is through the use of historical simulation which relies on a uniform distribution to select innovations from the past.17 These innovations are applied to current asset prices to simulate their future evolution. Once a sufficient number of different paths has been explored, it is possible to determine a portfolio VaR without making arbitrary distributional assumptions. This is especially useful in the presence of abnormally large portfolio returns. To make historical simulation consistent with the clustering of large returns, we will employ the GARCH volatility estimates of equation (2.29) to scale randomly selected past portfolio residual returns. First, the past daily portfolio residual returns are divided by the corresponding GARCH volatility estimates to obtain standardized residuals. Hence, the residual returns used in the historical simulation are i.i.d.,

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which ensures that the portfolio simulated returns will not be biased. A simulated portfolio return for tomorrow is obtained by multiplying randomly selected standardized residuals by the GARCH volatility to forecast the next day’s volatility. This simulated return is then used to update the GARCH forecast for the following days, that is, it is multiplied by a newly selected standardized residual to simulate the return for the second day. This recursive procedure is repeated until the VaR horizon (i.e. 10 days) is reached, generating a sample path of portfolio volatilities and returns. A batch of 10 000 sample paths of portfolio returns is computed and a confidence band for the portfolio return is built by taking the first and the ninety-ninth percentile of the frequency distribution of returns at each time. The lower percentile identifies the VaR over the next 10 days. To illustrate our methodology we use the standardized conditional residuals for our portfolio over the entire 1986–1995 period as shown in Figure 2.4. We then construct interactively the daily portfolio volatility that these returns imply according to equation (2.29). We use this volatility to rescale our returns. The resulting returns reflect current market conditions rather than historical conditions associated with the returns in Figure 2.3. To obtain the distribution of our portfolio returns we replicated the above procedure 10 000 times. The resulting–normalized–distribution is shown in Figure 2.9. The normal distribution is shown in the same figure for comparison. Not surprisingly, simulated returns on our well-diversified portfolio are almost

Figure 2.9 Normalized estimated distribution of returns in 10 days versus the normal density (10 000 simulations).

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normal, except for their steeper peaking around zero and some clustering in the tails. The general shape of the distribution supports the validity of the usual measure of VaR for our portfolio. However, a closer examination of our simulation results shows how even our well-diversified portfolio may depart from normality under worst-case scenarios. There are in fact several occurrences of very large negative returns, reaching a maximum loss of 7.22%. Our empirical distribution implies (under the WCS) losses of at least 3.28% and 2.24% at confidence levels of 1% and 5% respectively.18 The reason for this departure is the changing portfolio volatility and thus portfolio VaR, shown in Figure 2.10. Portfolio VaR over the next 10 days depends on the random returns selected in each simulation run. Its pattern is skewed to the right, showing how large returns tend to cluster in time. These clusters provide realistic WCS consistent with historical experience. Of course, our methodology may produce more extreme departures from normality for less diversified portfolios.

Figure 2.10 Estimated distribution of VaR.

Conclusions While portfolio holdings aim at diversifying risk, this risk is subject to continuous changes. The GARCH methodology allows us to estimate past and current and predicted future risk levels of our current position. However, the correlation-based VaR, which employed GARCH variance and covariance estimates, failed the diagnostic tests badly. The VaR model used in this application is a combination of historical

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simulation and GARCH volatility. It relies only on historical data for securities prices but applies the most current portfolio positions to historical returns. The use of historical returns of portfolio components and current weights can produce accurate estimates of current risk for a portfolio of traded securities. Information on the time series properties of returns of the portfolio components is transformed into a conditional estimate of the current portfolio volatility with no need to use complex multivariate time series procedures. Our approach leads to a simple formulation of stress analysis and correlation risk. There are three useful products of our methodology. The first is a simple and accurate measure of the volatility of the current portfolio from which an accurate assessment of current risk can be made. This is achieved without using computationally intensive multivariate methodologies. The second is the possibility of comparing a series of volatility patterns similar to Figure 2.7 with the historical volatility pattern of the actual portfolio with its changing weights. This comparison allows for an evaluation of the managers’ ability to ‘time’ volatility. Timing volatility is an important component of performance, especially if expected security returns are not positively related to current volatility levels. Finally, the possibility of using the GARCH residuals on the current portfolio weights allows for the implementation of meaningful stress testing procedures. Stress testing and the evaluation of correlation risk are important criteria in risk management models. To test our simplified approach to VaR we employed a hypothetical portfolio. We fitted an asymmetric GARCH on the portfolio returns and we forecasted portfolio volatility and VaR. The results indicate that this approach to estimating VaR is reliable. This is implied by the GARCH model yielding unbiased estimators for the portfolio conditional variance. Furthermore, this conditional variance estimate can now predict, on average, one third of the next day’s square price movement. We then applied the concept of correlation stability which we argue is a very useful tool in risk management in that it measures the proportion of an increase or decrease in the portfolio VaR caused by changes in asset correlations. In comparing the conditional volatility of our diversified and undiversified hypothetical portfolio, the effects of changes in correlations can be highlighted. While we found that the volatility of the diversified portfolio is lower than the undiversified portfolio, the use of correlation stability has the useful property of acting as an early warning to risk managers in relation to the effects of a negative shock, such as that of a stock market crash, on the riskiness of our portfolio. This is appealing to practitioners because it can be used to determine the ability of risk managers to diversify portfolio risk. Correlation stability is appealing to practitioners because it can be used, both in working with the portfolio selection and assessing the ability of risk managers to diversify portfolio risk. Thereafter, we show how ‘worst-case’ scenarios (WCS) for stress analysis may be constructed by applying the largest outliers in the innovation series to the current GARCH parameters. While the VaR estimated previously considers the market risk of a portfolio in relation to the frequency that a specific loss will be exceeded, it does not determine the size of the loss. Our exercise simulates the effect of the largest historical shock on current market conditions and evaluates the likelihood of a given loss occurring over the VaR horizon. In conclusion, our simulation methodology allows for a fast evaluation of VaR and WCS for large portfolios. It takes into account current market conditions and does not rely on the knowledge of the correlation matrix of security returns.

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Appendix: ARCH(1) properties The unconditional mean The ARCH model specifies that E(eD'tñ1 )ó0 for all realizations of 'tñ1 , the information set. Applying the law of iterated expectations we have E(et )ó0óE[E(et D'tñ1 )]

(A1)

Because the ARCH model specifies that E(et D'tñ1 )ó0 for all realizations of 'tñ1 this implies that E(et )ó0 and so the ARCH process has unconditional mean zero. The set 't contains all available information at time tñ1 but usually includes past returns and variances only.

The unconditional variance Similarly, the unconditional variance of the ARCH(1) process can be written as 2 E(e t2 )óE(e t2 D'tñ1 )óE(h t )óuòaE(e tñ1 )

(A2)

Assuming that the process began infinitely far in the past with finite initial variance, and by using the law of iterated expectations, it can be proved that the sequence of variances converge to a constant: E(e t2 )óh t óp 2 ó

u (1ña)

(A3)

The necessary and sufficient condition for the existence of the variance (the variance to be stationary) is u[0 and 1ña[0. Equation (A3) implies that although the variance of et conditional on 'tñ1 is allowed to change with the elements of the information set ', unconditionally the ARCH process is homoskedastic, hence E(e t2 )óh t óp 2 , the historical variance. After rearranging equation (A3) h t can be written as 2 h t ñp 2 óa(e tñ1 ñp 2 )

(A4)

It follows then that the conditional variance will be greater than the unconditional variance p 2 , whenever the squared past error exceeds p 2 .

The skewness and kurtosis of the ARCH process As et is conditionally normal, it follows that for all odd integers m we have E(e m t D'tñ1 )ó0 Hence, the third moment of an ARCH process is always 0, and is equal to the unconditional moment. However, an expression for the fourth moment, kurtosis, is available only for ARCH(1) and GARCH(1, 1) models. Using simple algebra we can write the kurtosis of an ARCH(1) as19 E(e t4 ) 3(1ña2 ) ó p e4 (1ñ3a2 )

(A5)

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which is greater than 3, the kurtosis coefficient of the normal distribution. This allows the distribution of Yt to exhibit fat tails without violating the normality assumption, and therefore to be symmetric. Researchers have established that the distribution, of price changes or their logarithm, in a variety of financial series symmetric but with fatter tails than those of normal distribution, even if the assumption of normality for the distribution of Yt is violated20 the estimates of an ARCH (family) model will still be consistent, in a statistical sense. Other researchers have advanced the idea that any serial correlation present in conditional second moments of speculative prices could be attributed to the arrival of news within a serial correlated process. We have seen so far that if a\1 and et is conditionally normal, the ARCH model has one very appealing property. It allows the errors, et , to be serially uncorrelated but not necessarily independent, since they can be related through their second moments (when a[0). Of course, if aó0 the process reduces to homoskedastic case.

Estimating the ARCH model The efficient and popular method for computing an ARCH model is maximum likelihood. The likelihood function usually assumes that the conditional density is Gaussian, so the logarithmic likelihood of the sample is given by the sum of the individual normal conditional densities. For example, given a process {yt } with constant mean and variance and drawn from a normal distribution the log likelihood function is given by ln(#)óñ

T T 1 2 ln(2n)ñ ln p 2 ñ p 2 2 2

T

; (Yt ñk)2

(A6a)

tó1

where ln(#) is the natural logarithm of the likelihood function for tó1, . . . , T. The procedure in maximizing the above likelihood function stands to maximize ln(#) for tó1, . . . , T. This involves finding the optimal values of the two parameters, p 2 and k. This is achieved by setting the first-order partial derivatives equal to zero and solving for the values of p 2 and k that yield the maximum value of ln(#). When the term k is replaced with bXt the likelihood function of the classical regression is derived. By replacing p 2 with h t and Yt ñk with e t2 , the variance residual error at time t, the likelihood for the ARCH process is derived. Thus the likelihood function of an ARCM process is given as 1 1 T 1 T e2 ln(#)ó ñ ln(2n)ñ ; ln(h t )ñ ; t 2 tó1 h t 2 2 tó1

(A6b)

Unfortunately ARCH models are highly non-linear and so analytical derivatives cannot be used to calculate the appropriate sums in equation (A6b). However, numerical methods can be used to maximize the likelihood function and obtain the parameter vector #. The preferred approach for maximizing the likelihood function and obtaining the required results is an algorithm proposed by Berndt, Hall and Hausman in a paper published in 1974. Other standard algorithms are available to undertake this task, for example, Newton. These are not recommended since they require the evaluation of the Hessian matrix, and often fail to converge. The strength of the maximum likelihood method to estimate an ARCH model lies in the fact that the conditional variance and mean can be estimated jointly, while

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exogenous variables can still have an impact in the return equation (A1), in the same way as in conventionally specified economic models. However, because ML estimation is expensive to compute, a number of alternative econometric techniques have been proposed to estimate ARCH models. Among those is the generalized method of moments (GMM) and a two-stage least squares method (2SLS). The second is very simple and consists of regressing the squared residuals of an AR(1) against past squared residuals. Although this method provides a consistent estimator of the parameters, it is not efficient.

GARCH(1, 1) properties Following the law of iterated expectations the unconditional variance for a GARCH(1, 1) can be written as E(e t2 )óp 2 óu/(1ñañb)

(A7)

which implies (aòb)\1 in order for h to be finite. An important property emerges from equation (A7). Shocks to volatility decay at a constant rate where the speed of the decay is measured by aòb. The closer that aòb is to one, the higher will be the persistence of shocks to current volatility. Obviously if aòbó1 then shocks to volatility persist for ever, and the unconditional variance is not determined by the model. Such a process is known as ‘Integrated GARCH’, or IGARCH. It can be shown that e t2 can be written as an ARMA(m, p) process with serially uncorrelated innovations Vt , where Vt •e t2 ñh t . The conditional variance of a GARCH(p, q) can be written as 2 e t2 óuò&(a1 òb1 )e tñ1 ò&bjVtñ1 òVt

(A8)

with ió1, . . . , m, mómax{p, q}, a•0 for i[q and bi •0 for i[p. The autoregressive parameters are a(L)òb(L), the moving average ones are ñb(L), and V are the serially non-correlated innovations. The autocorrelogram and partial autocorrelogram for e t2 can be used to identify the order {p, q} of the GARCH model. Thus if a(L)òb(L) is close to one, the autocorrelation function will decline quite slowly, indicating a relatively slow-changing conditional variance. An immediately recognized weakness of ARCH models is that a misspecification in the variance equation will lead to biased estimates for the parameters. Thus, estimates for the conditional variance and mean will no longer be valid in small samples but will be asymptotically consistent. However, GARCH models are not as sensitive to misspecification.

Exponential GARCH properties Unlike the linear (G)ARCH models, the exponential ARCH always guarantees a positive h t without imposing any restriction on the parameters in the variance equation (this is because logarithms are used). In addition, the parameters in the variance equation are always positive solving the problem of negative coefficients often faced in higher-order ARCH and GARCH models. It has been possible to overcome this problem by restricting the parameters to be positive or imposing a declining structure. Furthermore, unlike the GARCH models which frequently reveal that there is a persistence of shocks to the conditional variance, in exponential ARCH the ln(h t ) is strictly stationary and ergodic.

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Notes 1

ES techniques recognize that security returns are non-stationary (variances changes over time). However, they contradict themselves when used to calculate the VaR. 2 The variance in Riskmetrics is using current period surprise (et ). Hence, they have superior information to the GARCH model. 3 Maximization of the likelihood function is the computational price that is involved in using GARCH. The ES model involves only one parameter whose optimum value can be obtained by selecting that estimate which generates the minimum sum of squared residuals. 4 Hence each of the two series of the conditional (GARCH) variance is restricted to depend on its own past values and the last period’s residual errors to be denoted as surprises. Similarly, the conditional (GARCH) estimates for the covariance (which, among others, determine the sine of the cross-correlation coefficient) is modeled on its own last period’s value and the cross-product of the errors of the two assets. 5 When W represents an investment holding under consideration, Y describes the behavior of this hypothetical portfolio over the past. 6 Markowitz incorporates equation (2.27b) in the objective function of his portfolio selection problem because his aim was to find the optimal vector of weights W. However, if W is known a priori then the portfolio’s (unconditional) volatility can be computed more easily as in equation (2.28b). 7 The terms ‘local market’, ‘national market’, ‘domestic market’, ‘local portfolio’, ‘national portfolio’ refer to the national indices and will be used interchangeably through this study. 8 Due to investment restrictions for foreign investors in the emerging markets and other market misconceptions along with data non-availability, our study is restricted to the developed markets only. 9 In a widely diversified portfolio, which may contain different types of assets, the null hypothesis of non-ARCH may not be rejected even if each asset follows a GARCH process itself. 10 If the null hypothesis had not been rejected, then portfolio volatility could be estimated as a constant. 11 A number of different GARCH parameterizations and lag orders have been tested. Among these conditional variance parameterizations are the GARCH, exponential GARCH, threshold GARCH and GARCH with t-distribution in the likelihood function. We used a number of diagnostic tests, i.e. serial correlation, no further GARCH effect, significant t-statistics. The final choice for the model in equation (2.29) is the unbiasedness in conditional variance estimates as tested by the Pagan–Ullah test which is expressed in equation (2.30) and absence of serial correlation in the residual returns. Non-parametric estimates of conditional mean functions, employed later, support this assumption. 12 Throughout this chapter the term ‘empirical distribution’ refers to the Kernel estimators. 13 The Pagan–Ullah test can also be applied to measure the goodness of fit of a conditional covariance model. This stands on regressing the cross product of the two residual series against a constant and the covariance estimates. The unbiasedness hypothesis requires the constant to be zero and the slope to be one. The uncentered coefficient of determination of the regression tells us the forecasting power of the model. Unfortunately, even with daily observations, for most financial time series the coefficient of determination tends to be very low, pointing to the great difficulty in obtaining good covariance estimates. 14 That is, the average volatility of a diversified over the average of an undiversified portfolio. 15 The forecast of the portfolio variance converges to a constant, (uòac2 )/(1ñañb), which is also the mean of the portfolio’s conditional volatility. 16 A second limitation arises if the (stochastic) model that describes portfolio returns restricts portfolio variance to be constant over time. 17 Historical simulation is better known as ‘bootstrapping’ simulation. 18 Note that the empirical distribution has asymmetric tails and is kurtotic. Our methodology ensures that the degree of asymmetry is consistent with the statistical properties of portfolio returns over time.

Measuring volatility 19 20

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The ARCH(1) case requires that 3a2 \1 for the fourth moment to exist. Thus, the assumption that the conditional density is normally distributed usually does not affect the parameter estimates of an ARCH model, even if it is false, see, for example, Engle and Gonzalez-Rivera (1991).

Further reading Barone-Adesi, G., Bourgoin, F. and Giannopoulos, K. (1998) ‘Don’t look back’, Risk, August. Barone-Adesi, G., Giannopoulos, K. and Vosper, L. (1990) ‘VaR without correlations for non-linear portfolios’, Journal of Futures Markets, 19, 583–602. Berndt, E., Hall, B., Hall, R. and Hausman, J. (1974) ‘Estimation and interference in non-linear structured models’, Annals of Economic and Social Measurement, 3, 653–65. Black, M. (1968) ‘Studies in stock price volatility changes’, Proceedings of the 1976 Business Meeting of the Business and Economic Statistics Section, American Statistical Association, 177–81. Bollerslev, T. (1986) ‘Generalised autoregressive conditional heteroskedasticity’, Journal of Econometrics, 31, 307–28. Bollerslev, T. (1988) ‘On the correlation structure of the generalised autoregressive conditional heteroskedastic process’, Journal of Time Series Analysis, 9, 121–31. Christie, A. (1982) ‘The stochastic behaviour of common stock variance: value, leverage and interest rate effects’, Journal of Financial Economics, 10, 407–32. Diebold, F. and Nerlove, M. (1989) ‘The dynamics of exchange rate volatility: a multivariate latent factor ARCH model’, Journal of Applied Econometrics, 4, 1–21. Engle, R. (1982) ‘Autoregressive conditional heteroskedasticity with estimates of the variance in the UK inflation’, Econometrica, 50, 987–1008. Engle, R. and Bollerslev, T. (1986) ‘Modelling the persistence of conditional variances’, Econometric Reviews, 5, 1–50. Engle, R. and Gonzalez-Rivera, G. (1991) ‘Semiparametric ARCH models’, Journal of Business and Economic Statistics, 9, 345–59. Engle, R., Granger, C. and Kraft, D. (1984) ‘Combining competing forecasts of inflation using a bivariate ARCH model’, Journal of Economic Dynamics and Control, 8, 151–65. Engle, R., Lilien, D. and Robins, R. (1987) ‘Estimating the time varying risk premia in the term structure: the ARCH-M model’, Econometrica, 55, 391–407. Engle, R. and Ng, V. (1991) ‘Measuring and testing the impact of news on volatility’, mimeo, University of California, San Diego. Fama, E. (1965) ‘The behaviour of stock market prices’, Journal of Business, 38, 34–105. Glosten, L., Jagannathan, R. and Runkle, D. (1991) ‘Relationship between the expected value and the volatility of the nominal excess return on stocks’, mimeo, Northwestern University. Joyce, J. and Vogel, R. (1970) ‘The uncertainty in risk: is variance unambiguous?’, Journal of Finance, 25, 127–34. Kroner, K., Kneafsey, K. and Classens, S. (1995) ‘Forecasting volatility in commodity markets’, Journal of Forecasting, 14, 77–95. LeBaron, B. (1988) ‘Stock return nonlinearities: comparing tests and finding structure’, mimeo, University of Wisconsin.

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Mandelbrot, B. (1963) ‘The variation of certain speculative prices’, Journal of Business, 36, 394–419. Nelson, D. (1988) The Time Series Behaviour of Stock Market Volatility Returns, PhD thesis, MIT, Economics Department. Nelson, D. (1990) ‘Conditional heteroskedasticity in asset returns: a new approach’, Econometrica, 59, 347–70. Nelson, D. (1992) ‘Filtering and forecasting with misspecified ARCH models: getting the right variance with the wrong model’, Journal of Econometrics, 52. Pagan, A. and Schwert, R. (1990) ‘Alternative models for conditional stock volatility’, Journal of Econometrics, 45, 267–90. Pagan, A. and Ullah, A. (1988) ‘The econometric analysis of models with risk terms’, Journal of Applied Econometrics, 3, 87–105. Rosenberg, B. (1985) ‘Prediction of common stock betas’, Journal of Portfolio Management, 11, Winter, 5–14. Scheinkman, J. and LeBaron, B. (1989) ‘Non-linear dynamics and stock returns’, Journal of Business, 62, 311–37. Zakoian, J.-M. (1990) ‘Threshold heteroskedastic model’, mimeo, INSEE, Paris.

3

The yield curve P. K. SATISH

Introduction Fundamental to any trading and risk management activity is the ability to value future cash flows of an asset. In modern finance the accepted approach to valuation is the discounted cash flows (DCF) methodology. If C(t) is a cash flow occurring t years from today, according to the DCF model, the value of this cash flow today is V0 óC(t)Z(t) where Z(t) is the present value (PV) factor or discount factor. Therefore, to value any asset the necessary information is the cash flows, their payment dates, and the corresponding discount factors to PV these cash flows. The cash flows and their payment dates can be directly obtained from the contract specification but the discount factor requires the knowledge of the yield curve. In this chapter we will discuss the methodology for building the yield curve for the bond market and swap market from prices and rates quoted in the market. The yield curve plays a central role in the pricing, trading and risk management activities of all financial products ranging from cash instruments to exotic structured derivative products. It is a result of the consensus economic views of the market participants. Since the yield curve reflects information about the microeconomic and macroeconomic variables such as liquidity, anticipated inflation rates, market risk premia and expectations on the overall state of the economy, it provides valuable information to all market participants. The rationale for many trades in the financial market are motivated by a trader’s attempt to monetize their views about the future evolution of the yield curve when they differ from that of the market. In the interest rate derivative market, yield curve is important for calibrating interest rate models such as Black, Derman and Toy (1990), Hull and White (1990) and Heath, Jarrow and Morton (1992) and Brace, Gatarek and Musiela (1997) to market prices. The yield curve is built using liquid market instrument with reliable prices. Therefore, we can identify hedges by shocking the yield curve and evaluating the sensitivity of the position to changes in the yield curve. The time series data of yield curve can be fed into volatility estimation models such as GARCH to compute Value-at-Risk (VaR). Strictly speaking, the yield curve describes the term structure of interest rates in any market, i.e. the relationship between the market yield and maturity of instruments with similar credit risk. The market yield curve can be described by a number of alternative but equivalent ways: discount curve, par-coupon curve, zero-coupon

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or spot curve and forward rate curve. Therefore, given the information on any one, any of the other curves can be derived with no additional information. The discount curve reflects the discount factor applicable at different dates in the future and represents the information about the market in the most primitive fashion. This is the most primitive way to represent the yield curve and is primarily used for valuation of cash flows. An example of discount curve for the German bond market based on the closing prices on 29 October 1998 is shown in Figure 3.1. The par, spot, and forward curves that can be derived from the discount curve is useful for developing yield curve trading ideas.

Figure 3.1 DEM government discount curve.

The par-coupon curve reflects the relationship between the yield on a bond issued at par and maturity of the bond. The zero curve or the spot curve, on the other hand, indicates the yield of a zero coupon bond for different maturity. Finally, we can also construct the forward par curve or the forward rate curve. Both these curves show the future evolution of the interest rates as seen from today’s market yield curve. The forward rate curve shows the anticipated market interest rate for a specific tenor at different points in the future while the forward curve presents the evolution of the entire par curve at a future date. Figure 3.2 shows the par, spot, forward curves German government market on 29 October 1998. For example the data point (20y, 5.04) in the 6m forward par curve tell us that the 20-year par yield 6m from the spot is 5.04%. The data point (20y, 7.13) on the 6m forward rate curve indicates that the 6-month yield 20 years from the spot is 7.13%. For comparison, the 6-month yield and the 20-year par yield on spot date is 3.25% and 4.95% respectively. Since discount factor curve forms the fundamental building block for pricing and trading in both the cash and derivative markets we will begin by focusing on the methodology for constructing the discount curve from market data. Armed with the

The yield curve

77

Figure 3.2

DEM par-, zero-, and forward yield curves.

knowledge of discount curve we will then devote our attention to developing other representation of market yield curve. The process for building the yield curve can be summarized in Figure 3.3.

Bootstrapping swap curve Market participant also refers to the swap curve as the LIBOR curve. The swap market yield curve is built by splicing together the rates from market instruments that represent the most liquid instruments or dominant instruments in their tenors. At the very short end, the yield curve uses the cash deposit rates, where available the International Money Market (IMM) futures contracts are used for intermediate tenors and finally par swap rates are used for longer tenors. A methodology for building the yield curve from these market rates, referred to as bootstrapping or zero coupon stripping, that is widely used in the industry is discussed in this section. The LIBOR curve can be built using the following combinations of market rates: Ω Cash depositòfuturesòswaps Ω Cash depositòswaps The reason for the popularity of the bootstrapping approach is its ability to produce a no-arbitrage yield curve, meaning that the discount factor obtained from bootstrapping can recover market rates that has been used in their construction. The downside to this approach, as will be seen later, is the fact that the forward rate curve obtained from this process is not a smooth curve. While there exists methodologies to obtain smooth forward curves with the help of various fitting

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The Professional’s Handbook of Financial Risk Management

Figure 3.3 Yield curve modeling process.

algorithms they are not always preferred as they may violate the no-arbitrage constraint or have unacceptable behavior in risk calculations.

Notations In describing the bootstrapping methodology we will adopt the following notations for convenience: t0 S (T ): Z (T ): a(t1 , t 2 ): F (T1 , T2 ): P (T1, T2 ): f (T1, T2 ): d (T ):

Spot date Par swap rate quote for tenor T at spot date Zero coupon bond price or discount factor maturing on date T at spot date Accrual factor between date t1 and t 2 in accordance to day count convention of the market (ACT/360, 30/360, 30E/360, ACT/ACT) Forward rate between date T1 and T2 as seen from the yield curve at spot date IMM futures contract price deliverable on date T1 at spot date Futures rate, calculated as 100-P (T1 , T2 ) at spot date Money market cash deposit rates for maturity T at spot date

Extracting discount factors from deposit rates The first part of the yield curve is built using the cash deposit rates quoted in the market. The interest on the deposit rate accrue on a simple interest rate basis and

The yield curve

79

as such is the simplest instrument to use in generating discount curve. It is calculated using the following fundamental relationship in finance: Present valueófuture valueîdiscount factor The present value is the value of the deposit today and the future value is the amount that will be paid out at the maturity of the deposit. Using our notations we can rewrite this equation as 1ó(1òd(T )a(t 0 , T )îZ(T ) or equivalently, Z(T )ó

1 (1òd(T )a(t 0 , T )

(3.1)

The accrual factor, a(t 0 , T ), is calculated according to money market day count basis for the currency. In most currencies it is Actual/360 or Actual/365. For example, consider the deposit rate data for Germany in Table 3.1. Table 3.1 DEM cash deposit rates data Tenor

Bid

Accrual basis

O/N T/N T/N 1M 2M 3M 6M 9M 12M

3.35 3.38 3.38 3.45 3.56 3.55 3.53 3.44 3.47

Actual/360

The discount factor for 1W is 1

7 1ò3.38% 360

ó0.9993

Similarly, using expression (3.1) we can obtain the discount factor for all other dates as well. The results are shown in Table 3.2. These calculations should be performed after adjusting the maturity of cash rates for weekends and holidays where necessary. In the above calculation the spot date is trade date plus two business days as per the convention for the DEM market and the discount factor for the spot date is defined to be 1. If instead of the spot date we define the discount factor for the trade date to be 1.0 then the above discount factor needs to be rebased using the overnight rate and tomorrow next rate. First, we can calculate the overnight discount factor as: 1

7 1ò3.35% 360

ó0.9999

80

The Professional’s Handbook of Financial Risk Management Table 3.2 DEM cash deposit discount factor curve at spot date Tenor Trade date Spot 1W 1M 2M 3M 6M 9M 12M

Maturity

Accrued days

Discount factor

22-Oct-98 26-Oct-98 02-Nov-98 26-Nov-98 28-Dec-98 26-Jan-99 26-Apr-99 26-Jul-99 26-Oct-99

0 7 31 63 92 182 273 365

1.0000 0.9993 0.9970 0.9938 0.9910 0.9824 0.9745 0.9660

Next, we use the tomorrow next rate to calculate the discount factor for the spot date. The tomorrow next rate is a forward rate between trade day plus one business day to trade date plus two business day. Therefore, the discount factor for the spot date is: 1 ó0.9996 0.9999î 3 1ò3.38% 360

The discount factors to trade date can be obtained by multiplying all the discount factors that has been previously calculated to spot date by 0.9996. This is shown in Table 3.3. Table 3.3 DEM cash deposit discount curve at trade date Tenor Trade Date O/N Spot 1W 1M 2M 3M 6M 9M 12M

Maturity

Accrued days

Discount factor

22-Oct-98 23-Oct-98 26-Oct-98 02-Nov-98 26-Nov-98 28-Dec-98 26-Jan-99 26-Apr-99 26-Jul-99 26-Oct-99

0 1 4 11 35 67 96 186 277 369

1.00000000 0.99990695 0.99962539 0.99896885 0.99666447 0.99343628 0.99063810 0.98209875 0.97421146 0.96565188

Extracting discount factors from futures contracts Next we consider the method for extracting the discount factor from the futures contract. The prices for IMM futures contract reflect the effective interest rate for lending or borrowing 3-month LIBOR for a specific time period in the future. The contracts are quoted on a price basis and are available for the months March, June, September and December. The settlement dates for the contracts vary from exchange to exchange. Typically these contracts settle on the third Wednesday of the month

The yield curve

81

and their prices reflect the effective future interest rate for a 3-month period from the settlement date. The relationship between the discount factor and the futures rate is given by the expression below. t0

T1

T2 1 [1òf (T1, T2 )a(T1, T2 )

Z(T2 )óZ(T1)

(3.2)

The futures rate is derived from the price of the futures contract as follows: f (T1, T2 )ó

100ñP(T1,T2 ) 100

Thus, with the knowledge of discount factor for date T1 and the interest rate futures contract that spans time period (T1 ,T2 ) we can obtain the discount factor for date T2 . If the next futures contract span (T2 ,T3 ) then we can reapply expression (3.2) and use for Z(T2 ) the discount factor calculated from the previous contract. In general, Z(Ti )óZ(Tiñ1)

1 (1òf (Tiñ1,Ti )a(Tiñ1,Ti )

An issue that arises during implementation is that any two adjacent futures contract may not always adjoin perfectly. This results in gaps along the settlement dates of the futures contract making the direct application of expression (3.2) difficult. Fortunately, this problem can be overcome. A methodology for dealing with gaps in the futures contract is discussed later. Building on the example earlier, consider the data in Table 3.4 for 3-month Euromark futures contract in LIFFE. The settlement date is the third Wednesday of the contract expiration month. We assume that the end date for the 3-month forward period is the settlement date of the next contract, i.e. ignore existence of any gaps. Table 3.4 DEM futures price data Contract

Price

Implied rate (A/360 basis)

Settle date

End date

Accrued days

DEC98 MAR99 JUN99 SEP99 DEC99 MAR00 JUN00 SEP00 DEC00 MAR01 JUN01 SEP01 DEC01 MAR02 JUN02 SEP02

96.5100 96.7150 96.7500 96.7450 96.6200 96.6600 96.5600 96.4400 96.2350 96.1700 96.0800 95.9750 95.8350 95.7750 95.6950 95.6100

3.4900% 3.2850% 3.2500% 3.2550% 3.3800% 3.3400% 3.4400% 3.5600% 3.7650% 3.8300% 3.9200% 4.0250% 4.1650% 4.2250% 4.3050% 4.3900%

16-Dec-98 17-Mar-99 16-Jun-99 15-Sep-99 15-Dec-99 15-Mar-00 21-Jun-00 20-Sep-00 20-Dec-00 21-Mar-01 20-Jun-01 19-Sep-01 19-Dec-01 20-Mar-02 19-Jun-02 18-Sep-02

17-Mar-99 16-Jun-99 15-Sep-99 15-Dec-99 15-Mar-00 21-Jun-00 20-Sep-00 20-Dec-00 21-Mar-01 20-Jun-01 19-Sep-01 19-Dec-01 20-Mar-02 19-Jun-02 18-Sep-02 18-Dec-02

91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91

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The Professional’s Handbook of Financial Risk Management

The price DEC98 futures contract reflects the interest rate for the 91-day period from 16 December 1998 to 17 March 1999. This can be used to determine the discount factor for 17 March 1999 using expression (3.2). However, to apply expression (3.2) we need the discount factor for 16 December 1998. While there are several approaches to identify the missing discount factor we demonstrate this example by using linear interpolation of the 1-month (26 November 1998) and 2-month (28 December 1998) cash rate. This approach gives us a cash rate of 3.5188% and discount factor for 0.99504 with respect to the spot date. The discount factor for 17 March 1998 is 0.9950

1 91 1ò3.49% 360

ó0.9863

In the absence of any gaps in the futures contract the above discount factor together with the MAR99 contract can be used determine the discount factor for 16 June 1999 and so on until the last contract. The results from these computations are shown in Table 3.5. Table 3.5 DEM discount curve from futures prices Date

Discount factor

26-Oct-98 16-Dec-98

1.00000 0.99504

17-Mar-99 16-Jun-99 15-Sep-99 15-Dec-99 15-Mar-00 21-Jun-00 20-Sep-00 20-Dec-00 21-Mar-01 20-Jun-01 19-Sep-01 19-Dec-01 20-Mar-02 19-Jun-02 18-Sep-02 18-Dec-02

0.98634 0.97822 0.97024 0.96233 0.95417 0.94558 0.93743 0.92907 0.92031 0.91148 0.90254 0.89345 0.88414 0.87480 0.86538 0.85588

Method Spot Interpolated cash rate DEC98 MAR99 JUN99 SEP99 DEC99 MAR00 JUN00 SEP00 DEC00 MAR01 JUN01 SEP01 DEC01 MAR02 JUN02 SEP02

Extracting discount factor from swap rates As we go further away from the spot date we either run out of the futures contract or, as is more often the case, the futures contract become unsuitable due to lack of liquidity. Therefore to generate the yield curve we need to use the next most liquid instrument, i.e. the swap rate. Consider a par swap rate S(t N ) maturing on t N with cash flow dates {t1 , t 2 , . . . t N }.

The yield curve

83

The cash flow dates may have an annual, semi-annual or quarterly frequency. The relationship between the par swap rate and the discount factor is summarized in the following expression: N

1ñZ(t N )óS(t N ) ; a(t iñ1 , t i )Z(t i )

(3.3)

ió1

The left side of the expression represents the PV of the floating payments and the right side the PV of the fixed rate swap payments. Since a par swap rate by definition has zero net present value, the PV of the fixed and floating cash flows must to be equal. This expression can be rearranged to calculate the discount factor associated with the last swap coupon payment: Nñ1

1ñS(t N ) ; a(tiñ1, t i )Z(t i ) Z(t N )ó

ió1

(3.4)

1òa(t Nñ1 , t N )S(t N )

To apply the above expression we need to know the swap rate and discount factor associated with all but the last payment date. If a swap rate is not available then it has to be interpolated. Similarly, if the discount factors on the swap payment dates are not available then they also have to be interpolated. Let us continue with our example of the DEM LIBOR curve. The par swap rates are given in Table 3.6. In this example all swap rates are quoted in the same frequency and day count basis. However, note that this need not be the case; for example, the frequency of the 1–3y swap rate in Australia dollar is quarterly while the rest are semi-annual. First we combine our cash curve and futures curve as shown in Table 3.7. Notice that all cash discount factors beyond 16 December 1998 have been dropped. This is because we opted to build our yield curve using the first futures contract. Even though the cash discount factors are available beyond 16 December 1998 the futures takes precedence over the cash rates. Since we have already generated discount factor until 18 December 2002 the first relevant swap rate is the 5y rate. Before applying expression (3.3) to bootstrap the

Table 3.6 DEM swap rate data Tenor 2Y 3Y 4Y 5Y 6Y 7Y 8Y 9Y 10Y 12Y 15Y 20Y 30Y

Swap Rate

Maturity

3.4600% 3.6000% 3.7600% 3.9100% 4.0500% 4.1800% 4.2900% 4.4100% 4.4900% 4.6750% 4.8600% 5.0750% 5.2900%

26-Oct-00 26-Oct-01 28-Oct-02 27-Oct-03 26-Oct-04 26-Oct-05 26-Oct-06 26-Oct-07 27-Oct-08 26-Oct-10 28-Oct-13 26-Oct-18 26-Oct-28

Frequency/basis Annual, Annual, Annual, Annual, Annual, Annual, Annual, Annual, Annual, Annual, Annual, Annual, Annual,

30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360

84

The Professional’s Handbook of Financial Risk Management Table 3.7 DEM cash plus futures discount factor curve Date

Discount factor

26-Oct-98 27-Oct-98 02-Nov-98 26-Nov-98 16-Dec-98

1.00000 0.99991 0.99934 0.99704 0.99504

17-Mar-99 16-Jun-99 15-Sep-99 15-Dec-99 15-Mar-00 21-Jun-00 20-Sep-00 20-Dec-00 21-Mar-01 20-Jun-01 19-Sep-01 19-Dec-01 20-Mar-02 19-Jun-02 18-Sep-02 18-Dec-02

0.98634 0.97822 0.97024 0.96233 0.95417 0.94558 0.93743 0.92907 0.92031 0.91148 0.90254 0.89345 0.88414 0.87480 0.86538 0.85588

Source Spot Cash Cash Cash Interpolated cash Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures

discount factor for 27 October 2003 we have to interpolate the discount factor for all the prior payment dates from the ‘cash plus futures’ curve we have so far. This is shown in Table 3.8 using exponential interpolation for discount factors.

Table 3.8 DEM 5y swap payment date discount factor from exponential interpolation Cash ﬂow dates

Accrual factor

Discount factor

Method

26-Oct-99 26-Oct-00 26-Oct-01 28-Oct-02 27-Oct-03

1.0000 1.0000 1.0000 1.0056 0.9972

0.96665 0.93412 0.89885 0.86122 ?

Exponential interpolation from cashòfutures curve

Therefore, the discount factor for 27 October 2003 is 1ñ3.91%(1.00î0.9665ò1.00î0.93412ò1.00î0.89885ò1.0056î0.86122) 1ò3.91%î0.9972 ó0.82452

The yield curve

85

This procedure can be continued to derive all the discount factors. Each successive swap rate helps us identify the discount factor associated with the swap’s terminal date using all discount factors we know up to that point. When a swap rate is not available, for example the 11y rate, it has to be interpolated from the other available swap rates. The results are shown in Table 3.9 below, where we apply linear interpolation method for unknown swap rates. In many markets it may be that the most actively quoted swap rates are the annual tenor swaps. However, if these are semi-annual quotes then we may have more discount factors to bootstrap than available swap rates. For example, suppose that we have the six-month discount factor, the 1y semi-annual swap rate and 2y semiannual swap rate. To bootstrap the 2y discount factor we need the 18 month discount factor which is unknown: Spot1 Z0

6m Z 6m

1y Z 1y

18y Z 18M

2y Z 2Y

A possible approach to proceed in building the yield curve is to first interpolate (possibly linear interpolation) the 18-month swap rate from the 1y and 2y swap rate. Next, use the interpolated 18-month swap rate to bootstrap the corresponding discount factor and continue onwards to bootstrap the 2y discount factor. Another alternative is to solve numerically for both the discount factors simultaneously. Let G be an interpolation function for discount factors that takes as inputs the dates and adjacent discount factors to return the discount factor for the interpolation date, that is, Z 18m óG(T18m , Z 1y ,Z 2y ) The 2y equilibrium swap is calculated as S2y ó

1ñZ 2y [aspot,6m Z 6m òa6m,1y Z 1y òa1y,18y Z 18m òa18m,2y Z 2y ]

where a’s are the accrual factor according to the day count basis. Substituting the relationship for the 18m discount factor we get S2y ó

1ñZ 2y [aspot,6m Z 6m òa6m,1y Z 1y òa1y,18y G(T18m , Z 1y , Z 2y )òa18m,2y Z 2y ]

The above expression for the 2y swap rate does not require the 18m discount factor as input. We can then use a numerical algorithm such as Newton–Raphson to determine the discount factor for 2y, Z 2y , that will ensure that the equilibrium 2y swap rate equals the market quote for the 2y swap rate. Finally, putting it all together we have the LIBOR discount factor curve for DEM in Table 3.10. These discount factors can be used to generate the par swap curve, forward rate curve, forward swap rate curve or discount factors for pricing various structured products. The forward rate between any two dates T1 and T2 as seen from the yield curve on spot date t 0 is F (T1 , T2 )ó

Z(T1 ) 1 ñ1 Z(T2 ) a(T1 , T2 )

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The Professional’s Handbook of Financial Risk Management Table 3.9 DEM discount factors from swap rates Tenor 5y 6y 7y 8y 9y 10y 11y 12y 13y 14y 15y 16y 17y 18y 19y 20y 21y 22y 23y 24y 25y 26y 27y 28y 29y 30y

Maturity

Swap rate

Accrual factor

Discount factor

27-Oct-03 26-Oct-04 26-Oct-05 26-Oct-06 26-Oct-07 27-Oct-08 26-Oct-09 26-Oct-10 26-Oct-11 26-Oct-12 28-Oct-13 27-Oct-14 26-Oct-15 26-Oct-16 26-Oct-17 26-Oct-18 28-Oct-19 26-Oct-20 26-Oct-21 26-Oct-22 26-Oct-23 28-Oct-24 27-Oct-25 26-Oct-26 26-Oct-27 26-Oct-28

3.9100% 4.0500% 4.1800% 4.2900% 4.4100% 4.4900% 4.5824% 4.6750% 4.7366% 4.7981% 4.8600% 4.9029% 4.9459% 4.9889% 5.0320% 5.0750% 5.0966% 5.1180% 5.1395% 5.1610% 5.1825% 5.2041% 5.2256% 5.2470% 5.2685% 5.2900%

0.9972 0.9972 1.0000 1.0000 1.0000 1.0028 0.9972 1.0000 1.0000 1.0000 1.0056 0.9972 0.9972 1.0000 1.0000 1.0000 1.0056 0.9944 1.0000 1.0000 1.0000 1.0056 0.9972 0.9972 1.0000 1.0000

0.82452 0.78648 0.74834 0.71121 0.67343 0.63875 0.60373 0.56911 0.53796 0.50760 0.47789 0.45122 0.42543 0.40045 0.37634 0.35309 0.33325 0.31445 0.29634 0.27900 0.26240 0.24642 0.23127 0.21677 0.20287 0.18959

This can be calculated from the discount factor curve after applying suitable interpolation method to identify discount factors not already available. For example, using exponential interpolation we find that the discount factor for 26 April 1999 is 0.98271 and that for 26 October 1999 is 0.96665. The forward rate between 26 April 1999 and 26 October 1999 is

0.98271 ñ1 0.96665

1

ó3.27%

183 360

The 6m forward rate curve for DEM is shown in Table 3.11. Forward rate curves are important for pricing and trading a range of products such as swaps, FRAs, Caps and Floors and a variety of structured notes. Similarly, the equilibrium par swap rate and forward swap rates can be calculated from the discount from S(t s , t sòN )ó

Z(t s )ñZ(t sòN ) N

; a(t sòiñ1 , t sòi )Z(t sòi ) ió1

The yield curve

87 Table 3.10 DEM swap or LIBOR bootstrapped discount factor curve

Cash Date 26-Oct-98 27-Oct-98 02-Nov-98 26-Nov-98 16-Dec-98

Futures

Swaps

Discount factor

Date

Discount factor

Date

Discount factor

1.00000 0.99991 0.99934 0.99704 0.99504

17-Mar-99 16-Jun-99 15-Sep-99 15-Dec-99 15-Mar-00 21-Jun-00 20-Sep-00 20-Dec-00 21-Mar-01 20-Jun-01 19-Sep-01 19-Dec-01 20-Mar-02 19-Jun-02 18-Sep-02 18-Dec-02

0.98634 0.97822 0.97024 0.96233 0.95417 0.94558 0.93743 0.92907 0.92031 0.91148 0.90254 0.89345 0.88414 0.87480 0.86538 0.85588

27-Oct-03 26-Oct-04 26-Oct-05 26-Oct-06 26-Oct-07 27-Oct-08 26-Oct-09 26-Oct-10 26-Oct-11 26-Oct-12 28-Oct-13 27-Oct-14 26-Oct-15 26-Oct-16 26-Oct-17 26-Oct-18 28-Oct-19 26-Oct-20 26-Oct-21 26-Oct-22 26-Oct-23 28-Oct-24 27-Oct-25 26-Oct-26 26-Oct-27 26-Oct-28

0.82452 0.78648 0.74834 0.71121 0.67343 0.63875 0.60373 0.56911 0.53796 0.50760 0.47789 0.45122 0.42543 0.40045 0.37634 0.35309 0.33325 0.31445 0.29634 0.27900 0.26240 0.24642 0.23127 0.21677 0.20287 0.18959

S(t s , t sòN ) is the equilibrium swap rate starting at time t s and ending at time t sòN . If we substitute zero for s in the above expression we get the equilibrium par swap rate. Table 3.12 shows the par swap rates and forward swap rates from our discount curve. For comparison we also provide the market swap rates. Notice that the market swap rate and the equilibrium swap rate computed from the bootstrapped does not match until the 5y swaps. This is due to the fact that we have used the futures contract to build our curve until 18 December 2002. The fact that the equilibrium swap rates are consistently higher than the market swap rates during the first 4 years is not surprising since we have used the futures contract without convexity adjustments (see below).

Curve stitching Cash rates and futures contracts In building the yield curve we need to switch from the use of cash deposit rates at the near end of the curve to the use of futures rates further along the curve. The choice of the splice date when cash deposit rate is dropped and futures rate is picked up is driven by the trader’s preference, which in turn depends on the instruments

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The Professional’s Handbook of Financial Risk Management Table 3.11 DEM 6 month forward rates from discount factor curve Date

Discount factor

Accrual factor

6m forward rate

26-Oct-98 26-Apr-99 26-Oct-99 26-Apr-00 26-Oct-00 26-Apr-01 26-Oct-01 26-Apr-02 26-Oct-02 26-Apr-03 26-Oct-03 26-Apr-04 26-Oct-04 26-Apr-05 26-Oct-05 26-Apr-06 26-Oct-06 26-Apr-07 26-Oct-07 26-Apr-08 26-Oct-08 26-Apr-09

1.00000 0.98271 0.96665 0.95048 0.93412 0.91683 0.89885 0.88035 0.86142 0.84300 0.82463 0.80562 0.78648 0.76749 0.74834 0.72981 0.71121 0.69235 0.67343 0.65605 0.63884 0.62125

0.5083 0.5083 0.5083 0.5056 0.5083 0.5056 0.5083 0.5056 0.5083 0.5083 0.5083 0.5056 0.5083 0.5056 0.5083 0.5056 0.5083 0.5083 0.5083 0.5056

3.27% 3.35% 3.44% 3.73% 3.93% 4.16% 4.32% 4.32% 4.38% 4.64% 4.79% 4.89% 5.03% 5.02% 5.14% 5.39% 5.52% 5.21% 5.30% 5.60%

Table 3.12 DEM equilibrium swap rates from discount factor curve Tenor

Market swap rate

2y 3y 4y 5y 6y 7y 8y 9y 10y

3.4600% 3.6000% 3.7600% 3.9100% 4.0500% 4.1800% 4.2900% 4.4100% 4.4900%

Equilibrium swap 6m forward start rate swap rate 3.4658% 3.6128% 3.7861% 3.9100% 4.0500% 4.1800% 4.2900% 4.4100% 4.4900%

3.5282% 3.7252% 3.8917% 4.0281% 4.1678% 4.2911% 4.4088% 4.5110% 4.5970%

that will be used to hedge the position. However, for the methodology to work it is necessary that the settlement date of the first futures contract (referred to as a ‘stub’) lie before the maturity date of the last cash deposit rate. If both cash deposit rate and futures rates are available during any time period then the futures price takes precedence over cash deposit rates. Once the futures contracts have been identified all discount factors until the last futures contract is calculated using the bootstrapping procedure. To bootstrap the curve using the futures contract, the discount factor corresponding to the first

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futures delivery date (or the ‘stub’) is necessary information. Clearly, the delivery date of the first futures contract selected may not exactly match the maturity date of one of the cash deposit rates used in the construction. Therefore, an important issue in the curve building is the method for identifying discount factor corresponding to the first futures contract or the stub rate. The discount factor for all subsequent dates on the yield curve will be affected by the stub rate. Hence the choice of the method for interpolating the stub rate can have a significant impact on the final yield curve. There are many alternative approaches to tackle this problem, all of which involves either interpolation method or fitting algorithm. We will consider a few based on the example depicted in Figure 3.4.

Figure 3.4 Cash-futures stitching.

The first futures contract settles on date T1 and spans from T1 to T2 . One alternative is to interpolate the discount factor for date T1 with the 3m and 6m discount factor calculated from expression (3.1). The second alternative is to directly interpolate the 3m and 6m cash deposit rates to obtain the stub rate and use this rate to compute the discount factor using expression (3.1). The impact of applying different interpolation method on the stub is presented in Table 3.13. Table 3.13 DEM stub rate from different interpolation methods Data Spot date: 26-Oct-98 1m (26-Nov-99): 3.45% 2m (28-Dec-98): 3.56% Basis: Act/360 Stub: 16-Dec-98

Interpolation method

Discount factor

Cash rate (stub rate)

Linear cash rate Exponential DF Geometric DF Linear DF

0.99504 0.99504 0.99502 0.99502

3.5188% 3.5187% 3.5341% 3.5332%

The exponential interpolation of discount factor and linear interpolation of the cash rate provide similar results. This is not surprising since exponential interpolation of discount factor differs from the linear interpolation of rate in that it performs a linear interpolation on the equivalent continuously compounded yield.

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To see the impact of the stitching method on the forward around the stub date we have shown the 1m forward rate surrounding the stub date in Figure 3.5 using different interpolation methods. Also, notice that the forward rate obtained from the bootstrapping approach is not smooth.

Figure 3.5 Six-month forward rate from different interpolation.

Going back to Figure 3.4, once the 3m and 6m rate has been used for interpolating the discount factor for T1 all cash rates beyond the 3m is dropped. The yield curve point after the 3m is T2 and all subsequent yield curve point follow the futures contract (i.e. 3 months apart). Since the 6m has been dropped, the 6m cash rate interpolated from the constructed yield curve may not match the 6m cash rate that was initially used. This immediately raises two issues. First, what must be the procedure for discounting any cash flow that occurs between T1 and 6m? Second, what is the implication of applying different methods of interpolation of the discount curve on the interpolated value of the 6m cash rate versus the market cash rate. Further, is it possible to ensure that the interpolated 6m cash rate match the market quoted 6m cash rate? If recovering the correct cash rate from the yield curve points is an important criterion then the methodologies discussed earlier would not be appropriate. A possible way to handle this issue is to change the method for obtaining the stub rate by directly solving for it. We can solve for a stub rate such that 6m rate interpolated from the stub discount factor for T1 and discount factor for T2 match the market. In some markets such as the USD the short-term swap dealers and active cash dealers openly quote the stub rate. If so then it is always preferable to use the market-quoted stub rate and avoid any interpolation.

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Futures strip and swap rates To extend the curve beyond the last futures contract we need the swap rates. The required swap rate may be available as input data or may need to be interpolated. Consider the following illustration where the last futures contract ends on TLF . If S(T2 ), the swap rate that follows the futures contract, is available as input then we can apply expression (3.3) to derive the discount factor for date T2 . This is a straightforward case that is likely to occur in currencies such as DEM with annual payment frequency:

t0

S(T1 )

TLF

S(T2 )

The discount factor corresponding to all payment dates except the last will need to be interpolated. In the next scenario depicted below suppose that swap rate S(T2 ) is not available from the market:

t0

S(T1 )

TLF

S(T2 )

S(T3 )

We have two choices. Since we have market swap rate, S(T1 ) and S(T3 ), we could use these rates to interpolate S(T2 ). Alternatively, since we have discount factor until date TLF we can use them to calculate a equilibrium swap rate, [email protected](T1 ), for tenor T1 . The equilibrium swap rate [email protected](T1 ) and market swap rate S(T3 ) can be used to interpolate the missing swap rate S(T2 ). In some circumstances we may have to interpolate swap rates with a different basis and frequency from the market-quoted rates. In these cases we recommend that the market swap rates be adjusted to the same basis and frequency as the rate we are attempting to interpolate. For example, to get a 2.5y semi-annual, 30E/360 swap rate from the 2y and 3y annual, 30E/360 swap rate, the annual rates can be converted to an equivalent semi-annual rate as follows: Ssemi-annual ó2î[(1òSannual )1/2 ñ1]

Handling futures gaps and overlaps In the construction of the discount factor using expression (3.2), we are implicitly assuming that all futures contract are contiguous with no gaps or overlaps. However, from time to time due to holidays it is possible that the contracts do not line up exactly. Consider the illustration below:

T1 , Z 1

T2 , Z 2

[email protected] , Z [email protected]

T3 , Z 3

The first futures contract span from T1 to T2 while the next futures contract span from [email protected] to T3 resulting in a gap. An approach to resolve this issue is as follows. Define G(T, Z a , Z b ) to be a function representing the interpolation method (e.g. exponential)

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for discount factor. We can apply this to interpolate Z 2 as follows: Z [email protected]óG ([email protected] , Z 2 , Z 3 )

(3.4)

From expression (3.2) we also know that Z 3 óZ [email protected]

1 •(( f, Z [email protected] ) [1òf ([email protected] , T3 )a([email protected] , T3 )]

(3.5)

Combining expressions (3.4) and (3.5) we have Z 3 ó(( f, G([email protected] , Z 2 , Z 3 ))

(3.6)

All variables in expression (3.6) are known except Z 3 . This procedure can be applied to find the discount factor Z 3 without the knowledge of Z [email protected] caused by gaps in the futures contract. For example, if we adopt exponential interpolation ([email protected] ñt 0 )

j

([email protected] ñt 0 )

Z [email protected] óG([email protected] , Z 2 , Z 3 )óZ 2 (T2 ñt 0 ) Z (T3 ñt 0 )

(1ñj)

and jó

(T3 ñT2 ) (T3 ñT2 )

Therefore, ([email protected] ñt 0 )

j

([email protected] ñt 0 )

Z 3 óZ 2 (T2 ñt 0 ) Z (T3 ñt 0 )

(1ñj)

1 [1òf ([email protected] , T3 )a([email protected] , T3 )]

This can be solved analytically for Z 3 . Specifically,

ln Z 2

Z 3 óExp

([email protected] ñt 0 ) j (T2 ñt 0 )

1 [1òf ([email protected] , T3 )a([email protected] , T3 )]

(T ñt 0 ) (1ñj) 1ñ [email protected] (T3 ñt 0 )

(3.7)

As an example consider an extreme case where one of the futures price is entirely missing. Suppose that we know the discount factor for 16 June 1999 and the price of SEP99 futures contract. The JUN99 contract is missing. Normally we would have used the JUN99 contract to derive the discount factor for 15 September 1999 and then use the SEP99 contract to obtain the discount factor for 15 December 1999 (Table 3.14). Table 3.14 Contract

Price

Implied rate (A/360 basis)

Settle date

End date

Discount factor

16-Jun-99 15-Sep-99

26-Oct-98 (t0 ) 16-Jun-99 (T2 ) 15-Sep-99 ([email protected] ) 15-Dec-99 (T3 )

1.0000 0.9782 N/A ?

Spot JUN99 SEP99

Missing 96.7450

Missing 3.2550%

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In this example we can apply expression (3.7) to obtain the discount factor for 15 December 1999:

0.8877

ln Z 20.6384

Z 15-Dec-99 óexp

î0.5

1ñ

1 [1ò3.255%î91/360]

0.8877 (1ñ0.5) 1.1370

ó0.96218

Earlier when we had the price for the JUN99 contract the discount factor for 15 December1999 was found to be 0.96233. Solution to overlaps are easier. If the futures contracts overlap (i.e. [email protected] \T2 ) then the interpolation method can be applied to identify the discount factor Z [email protected] corresponding to the start of the next futures contract.

Futures convexity adjustment Both futures and FRA are contracts written on the same underlying rate. At the expiration of the futures contract, the futures rate and the forward rate will both be equal to the then prevailing spot LIBOR rate. However, these two instruments differ fundamentally in the way they are settled. The futures contracts are settled daily whereas the FRAs are settled at maturity. As a result of this difference in the settlement procedure the daily changes in the value of a position in futures contract and that of a position in FRA to anticipated changes in the future LIBOR rate are not similar. The futures contract react linearly to changes in the future LIBOR rate while the FRA reacts non-linearly. This convexity effect creates an asymmetry in the gains/ losses between being long or short in FRA and hedging them with futures contracts. To be more precise, there is an advantage to being consistently short FRA and hedging them with short futures contracts. This is recognized by the market and reflected in the market price of the futures contract. The convexity effect implies that the forward rate obtained from the futures price will be high. Since the futures rate and forward rate converge as we approach the maturity date, the futures rate must drift downwards. Hence while building the LIBOR yield curve it is important that the forward rates implied from the futures price be adjusted (downwards) by the drift. In most markets the drift adjustments tend to be fairly small for futures contracts that expire within one year from the spot date, but can get progressively larger beyond a year. Consider an N futures contract for periods (t i , t iò1 ), ió1, 2, . . . N and (t iò1 ñt i )ó*t. A simple method to calculate the drift adjustments for the kth futures contract is given below:1 k

kk ó ; f (t i , t iò1 )of Z pf (ti ,tiò1) pZ(tiò1) *t ió1

where f (t i , t iò1 ) is the futures rate for the period t i to t iò1 , pZ(tiò1) is the volatility of the zero coupon bond maturing on t iò1 , pf (ti ,tiò1) is the volatility of the forward rate for the corresponding period and of Z is the correlation between the relevant forward rate and zero coupon bond price. The kth forward rate can be calculated from the futures contract as follows. F(t k , t kò1 )óf (t k , t kò1 )òkk

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Since the correlation between the forward rate and the zero coupon price is expected to be negative the convexity adjustment would result in the futures rate being adjusted downwards. We demonstrate the calculations for the convexity adjustment in the table below. Table 3.15

Contract

Date

Futures rate (1)

SPOT DEC98 MAR99 JUN99 SEP99 DEC99 MAR99 JUN00

26-Oct-98 16-Dec-98 17-Mar-99 16-Jun-99 15-Sep-99 15-Dec-99 15-Mar-00 21-Jun-00

3.49% 3.29% 3.25% 3.26% 3.38% 3.34%

Futures Zero rate coupon volatility volatility (2) (3) 5% 12% 14% 18% 20% 15%

0.25% 0.75% 1.25% 1.50% 2.00% 2.25%

Correlation n (4)

*t (5)

ñ0.99458 ñ0.98782 ñ0.97605 ñ0.96468 ñ0.95370 ñ0.94228

0.25 0.25 0.25 0.25 0.25 0.27

Drift Cumula(bp)ó(1) tive drift or Convexity î(2)î(3) convexity adjusted î(4)î(5) bias (bp) futures rate (6) (7) (8)ó(1)ò(7) ñ0.0108 ñ0.0728 ñ0.1383 ñ0.2112 ñ0.3212 ñ0.2850

ñ0.01 ñ0.08 ñ0.22 ñ0.43 ñ0.75 ñ1.04

3.49% 3.28% 3.25% 3.25% 3.37% 3.33%

Typically the convexity bias is less that 1 basis point for contracts settling within a year from the spot. Between 1 year and 2 years the bias may range from 1 basis point to 4 basis point. For contracts settling beyond 2 years the bias may be as high as 20 basis point – an adjustment that can no longer be ignored.

Interpolation The choice of interpolation algorithm plays a significant role in the process of building the yield curve for a number of reasons: First, since the rates for some of the tenors is not available due to lack of liquidity (for example, the 13-year swap rate) these missing rates need to be determined using some form of interpolation algorithm. Second, for the purposes of pricing and trading various instruments one needs the discount factor for any cash flow dates in the future. However, the bootstrapping methodology, by construction, produces the discount factor for specific maturity dates based on the tenor of the interest rates used in the construction process. Therefore, the discount factor for other dates in the future may need to be identified by adopting some interpolation algorithm. Finally, as discussed earlier, the discount factor corresponding to the stub will most of the time require application of interpolation algorithm. Similarly, the futures and swap rates may also need to be joined together with the help of interpolation algorithm. The choice of the interpolation algorithm is driven by the requirement to balance the need to control artificial risk spillage (an important issue for hedging purposes) against the smoothness of the forward curve (an important issue in the pricing of exotic interest rate derivatives). Discount factor interpolation Consider the example described below:

t0

Z 1 , T1

Z s , Ts

Z 2 , T2

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(a) Linear interpolation The linear interpolation of discount factor for date Ts is obtained by fitting a straight line between the two adjacent dates T1 and T2 . According to linear interpolation, the discount factor for date Ts is: Z s óZ 1 ò

Z 2 ñZ 1 (Ts ñT1) T2 ñT1

or Zsó

T2 ñTs T ñT1 Z1ò s Z2 T2 ñT1 T2 ñT1

Linear interpolation of the discount factor is almost never due to the non-linear shape of the discount curve, but the error from applying it is likely to be low in the short end where there are more points in the curve. (b) Geometric (log-linear) Interpolation The geometric or log linear interpolation of discount factor for date Ts is obtained by applying a natural logarithm transformation to the discount factor function and then performing a linear interpolation on the transformed function. To recover the interpolated discount factor we take the exponent of the interpolated value as shown below: ln(Z s )ó

T2 ñTs T ñT1 ln(Z 1)ò s ln(Z 2 ) T2 ñT1 T2 ñT1

or Z sóexp or

T2 ñTs T ñT1 ln(Z 1)ò s ln(Z 2 ) T2 ñT1 T2 ñT1

Z óZ s

1

T2 ñTs T2 ñT1

Z 2

Ts ñT1 T2 ñT1

(c) Exponential interpolation The continuously compounded yield can be calculated from the discount factor as follows: y1 óñ

1 ln(Z 1 ) (T1 ñt 0 )

and y2 óñ

1 ln(Z 2 ) (T2 ñt 0 )

To calculate the exponential interpolated discount factor we first perform a linear interpolation of the continuously compounded yields as follows: ys óy1

(T2 ñTs ) (T ñT1) òy2 s (T2 ñT1) (T2 ñT1)

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Next we can substitute for yield y1 and y2 to obtain: ys óñ

1 1 ln(Z 1 )jñ ln(Z 2 )(1ñj) (T1 ñt 0 ) (T2 ñt 0 )

where jó

(T2 ñTs ) (T2 ñT1)

The exponentially interpolated value for date Ts is Z s óexp(ñys (Ts ñt 0 ) or (Ts ñt 0 )

j

(Ts ñt 0 )

Z s óZ 1(T1 ñt 0 ) Z 2(T2 ñt 0 )

(1ñj)

Interpolation example Consider the problem of finding the discount factor for 26 February 1999 using the data in Table 3.16. Table 3.16 Discount factor interpolation data Days to spot

Discount factor

0 92 123 182

1.00000 0.99101 ? 0.98247

Date 26-Oct-98 26-Jan-99 26-Feb-99 26-Apr-99

Linear interpolation: Z 26-Feb-99 ó

182ñ123 123ñ92 0.99101ò 0.98247 182ñ92 182ñ92

ó0.98806 Geometric interpolation: Z 26-Feb-99 ó0.99101

0.98247

182ñ123 182ñ92

ó0.98805 Exponential interpolation: jó

(182ñ123) (182ñ92)

ó0.6555

123ñ92 182ñ92

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Z s ó0.99101 (92ñ0)

0.6555

(123ñ0)

0.98247 (182ñ0)

(1ñ0.6555)

ó0.988039 (d) Cubic interpolation Let {tót 0 , t1 , t 2 , . . . t n óT} be a vector of yield curve point dates and Zó{Z 0 , Z 1 , Z 2 , . . . Z n } be the corresponding discount factors obtained from the bootstrapping process. Define Z i óa i òbi t i òci t i2 òd i t i3 to be a cubic function defined over the interval [t i , t iò1 ]. A cubic spline function is a number of cubic functions joined together smoothly at a number of knot points. If the yield curve points {t, t1 , t 2 , . . .T } are defined to be knot points, then coefficients of the cubic spline function defined over the interval [t,T ] can be obtained by imposing the following constraints:

Z i óa i òbi t i òci t i2 òd i t i3 2 3 òd i t iò1 Z iò1 óa i òbi t iò1 òci t iò1 bi ò2ci t i ò3d i t i2 óbiò1 ò2ciò1 t i ò3diò1 t i2 2ci ò6d i t i ó2ciò1 ò6diò1 t i

ió0 to nñ1; n constraints ió0 to nñ1; n constraints ió0 to nñ2; nñ2 constraints ió0 to nñ2; nñ2 constraints

The first sets of n constraints imply that the spline function fit the knot points exactly. The second sets of n constraints require that the spline function join perfectly at the knot point. The third and the fourth sets of constraints ensure that the first and second derivatives match at the knot point. We have a 4n coefficient to estimate and 4n-2 equation so far. The two additional constraints are specified in the form of end point constraints. In the case of natural cubic spline these are that the second derivative equals zero at the two end points, i.e. 2ci ò6d i t i ó0

ió0 and n

The spline function has the advantage of providing a very smooth curve. In Figure 3.6 we present the discount factor and 3-month forward rate derived from exponential and cubic interpolation. Although the discount curves in both interpolation seem similar; comparison of the 3-month forward rate provides a clearer picture of the impact of interpolation technique. The cubic spline produces a smoother forward curve. Swap rate interpolation As in the discount curve interpolation, the swap rate for missing tenor can be interpolated using the methods discussed earlier for the discount factor. The exponential or geometric interpolation is not an appropriate choice for swap rate. Of the remaining methods linear interpolation is the most popular. In Figure 3.7 we compare the swap rate interpolated from linear and cubic splines for GBP. The difference between the rate interpolated by linear and cubic spline ranges from ò0.15 bp to ñ0.25 basis points. Compared to the swap rate from linear interpolation, the rate from cubic spline more often higher, particularly between 20y and 30y tenors. Unfortunately, the advantage of the smooth swap rate curve from the cubic spline is overshadowed by the high level of sensitivity exhibited by the

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Figure 3.6 Forward rate and discount factor from cubic spline and exponential interpolation.

Figure 3.7 Linear and cubic spline swap rate interpolation.

method to knot point data. This can give rise to artificial volatility with significant implications for risk calculations. Figure 3.8 shows the changes in the interpolated swap rate (DEM) for all tenors corresponding to a 1 basis point change in the one of the swap tenors. A 1 basis point change in one swap rate can change the interpolated swap rates for all tenors

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irrespective of their maturities! That is, all else being equal, the effects of a small change in one swap rate is not localized. For example, a 1 bp shift in the 2y swap rate results in a ñ0.07 bp shift in the 3.5y swap rate and a 1 bp shift in the 5y swap rate results in a ñ0.13 bp change in the 3.5y swap rate. This is an undesirable property of cubic spline interpolation, and therefore not preferred in the market.

Figure 3.8 Sensitivity of cubic spline interpolation.

Figure 3.9 displays the implications of applying linear interpolation. The interpolation method, while not smooth like the cubic spline, does keep the impact of a small change in any swap rate localized. The effect on swap rates outside the relevant segment is always zero. This property is preferred for hedge calculations.

Figure 3.9 Sensitivity of linear interpolation.

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Government Bond Curve The bond market differs from the swap market in that the instruments vary widely in their coupon levels, payment dates and maturity. While in principle it is possible to follow the swap curve logic and bootstrap the discount factor, this approach is not recommended. Often the motivation for yield curve construction is to identify bonds that are trading rich or cheap by comparing them against the yield curve. Alternatively, one may be attempting to develop a time series data of yield curve for use in econometric modeling of interest rates. Market factors such as liquidity effect and coupon effect introduce noise that makes direct application of market yields unsuitable for empirical modeling. In either application the bootstrapping approach that is oriented towards guaranteeing the recovery of market prices will not satisfy our objective. Therefore the yield curve is built by applying statistical techniques to market data on bond price that obtains a smooth curve. Yield curve models can be distinguished based on those that fit market yield and those that fit prices. Models that fit yields specify a functional form for the yield curve and estimate the coefficient of the functions using market data. The estimation procedure fits the functional form to market data so as to minimize the sum of squared errors between the observed yield and the fitted yield. Such an approach while easy to implement is not theoretically sound. The fundamental deficiency in this approach is that it does not constrain the cash flows occurring on the same date to be discounted at the same rate. Models that fit prices approach the problem by specifying a functional form for the discount factor and estimate the coefficient using statistical methods. Among the models that fit prices there is also a class of models that treat forward rate as the fundamental variable and derive the implied discount function. This discount function is estimated using the market price data. In this section we limit our discussion to the later approach that was pioneered by McCulloch (1971). This approach is well accepted, although there is no agreement among the practitioners on the choice of the functional form for discount factor. There is a large volume of financial literature that describes the many ways in which this can be implemented. The discussions have been limited to a few approaches to provide the reader with an intuition into the methodology. A more comprehensive discussion on this topic can be found in papers listed in the References.

Parametric approaches The dirty price of a bond is simply the present value of its future cash flows. The dirty price of a bond with N coupon payments and no embedded options can be expressed as: N

P(TN )òA(TN )ó ; cN Z(Ti )òFN Z(TN )

(3.8)

ió1

where P(TN ) is the clean price of a bond on spot date t 0 and maturing on TN , A(TN ) is its accrued interest, cN is the coupon payment on date t, Z(Ti ) is the discount factor at date t and FN is the face value or redemption payment of the bond. The process of building the yield curve hinges on identifying the discount factors corresponding to the payment dates. In the swap market we obtained this from bootstrapping the cash, futures and swap rates. In contrast, in the bond market we assume it to be

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one of the many functions available in our library of mathematical function and then estimate the function to fit the data. While implementing this approach two factors must be kept in mind. First, the discount factor function selected to represent the present value factor at different dates in the future must be robust enough to fit any shape for the yield curve. Second, it must satisfy certain reasonable boundary conditions and characteristics. The discount curve must be positive monotonically non-increasing to avoid negative forward rates. Mathematically, we can state these conditions as (a) Z(0)ó1 (b) Z(ê)ó0 (c) Z(Ti )[Z(Tió1 ) Conditions (a) and (b) are boundary conditions on present value factors based upon fundamental finance principles. Condition (c) ensures that the discount curve is strictly downward sloping and thus that the forward rates are positive. A mathematically convenient choice is to represent the discount factor for any date in the future as a linear combination of k basis functions: k

Z(Ti )ó1ò ; a i fi (Ti ) jó1

where fj (t) is the jth basis function and a j is the corresponding coefficient. The basis function can take a number of forms provided they produce sensible discount function. Substituting equation (3.9) into (3.8) we get N

P(TN )òA(TN )ócN ; ió1

k

k

1ò ; a j f (Ti ) òFN 1ò ; a j f (TN ) jó1

jó1

(3.10)

This can be further simplified as k

N

P(TN )òA(TN )ñNcN ñFN ó ; a j cN ; f (Ti )óFN f (TN ) jó1

ió1

(3.11)

Equivalently, k

yN ó ; a j xN

(3.12)

jó1

where yN óP(TN )òA(TN )ñNcN ñFN

(3.13)

N

xN ócN ; f (Ti )òFN f (TN )

(3.14)

ió1

If we have a sample of N bonds the coefficient of the basis function can be estimated using ordinary least squares regression. The estimated discount function can be used to generate the discount factors for various tenors and the yield curves. McCulloch (1971) modeled the basis function as f (T )óT j for jó1, 2, . . . k. This results in the discount function being approximated as a kth degree polynomial. One of the problems with this approach is that it has uniform resolution power. This is

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not a problem if the observations are uniformly distributed across the maturities. Otherwise, it fits well wherever there is greatest concentration of observations and poorly elsewhere. Increasing the order of the polynomial while solving one problem can give rise to another problem of unstable parameters. Another alternative suggested by McCulloch (1971) is to use splines. A polynomial spline is a number of polynomial functions joined together smoothly at a number of knot points. McCulloch (1971,1975) shows the results from applying quadratic spline and cubic spline functions. The basis functions are represented as a family of quadratic or cubic functions that are constrained to be smooth around the knot points. Schaefer (1981) suggested the use of Bernstein polynomial with the constraint that discount factor at time zero is 1. A major limitation of these methods is that the forwards rates derived from the estimated discount factors have undesirable properties at long maturity. Vasicek and Fong (1982) model the discount function as an exponential function and describe an approach that produces asymptotically flat forward curve. This approach is in line with equilibrium interest rate models such as Vasicek (1977) and Hull and White (1990) that show that zero coupon bond price or the discount factor to have exponential form. Rather than modeling the discount curve Nelson and Siegel (1987) directly model the forward rate. They suggest the following functional form for the forward rate:

F(t)ób0 òb1 exp ñ

t òb2 a1

t t exp ñ a1 a1

(3.15)

This implies the following discount curve:

Z(t)óexp ñt b0 ò(b1 òb2 ) 1ñexp ñ

t a1

t a1 ñb2 exp ñ t a1

(3.16)

Coleman, Fisher and Ibbotson (1992) also model the forward rates instead of the discount curve. They propose instantaneous forward rate to be a piecewise constant function. Partitioning the future dates into N segments, {t 0 , t1 , t 2 , . . . t N }, their model define the forward rate in any segment to be t iñ1 \tOt i

F(t)óji

(3.17)

This model implies that the discount factor at date t between t kñ1 and t k is

kñ1

Z(t)óexp ñ j1t 1 ò ; ji (t i ñt iñ1 )ójk (tñt kñ1 ) ió2

(3.18)

The discount curve produced by this model will be continuous but the forward rate curve will not be smooth. Chambers, Carleton and Waldman (1984) propose an exponential polynomial for the discount curve. The exponential polynomial function for discount factor can be written as

k

Z(t)óexp ñ ; a j m j jó1

(3.19)

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They recommend that a polynomial of degree 3 or 4 is sufficient to model the yield curve. Finally, Wiseman (1994) model the forward curve as an exponential function k

F(t)ó ; a j e ñkj t

(3.20)

jó0

Exponential model To see how the statistical approaches can be implemented consider a simplified example where the discount curve is modeled as a linear combination of m basis functions. Each basis function is assumed to be an exponential function. More specifically, define the discount function to be: m

Z(t)ó ; a k (e ñbt )k

(3.21)

kó1

where a and b are unknown coefficients of the function that need to be estimated. Once these parameters are known we can obtain a theoretical discount factor at any future date. These discount factors can be used to determine the par, spot and forward curves. Substituting the condition that discount factor must be 1 at time zero we obtain the following constraint on the a coefficients: m

; ak ó1

(3.22)

kó1

We can rearrange this as mñ1

am ó1ñ ; ak

(3.23)

kó1

Suppose that we have a sample of N bonds. Let P(Ti ), ió1, 2, . . . N, be the market price of the ith bond maturing Ti years from today. If qi is the time when the next coupon will be paid, according to this model the dirty price of this bond can be expressed as: Ti

P(Ti )òA(Ti )ó ; cj Z(t)ò100Z(Ti )òei tóqi Ti

ó ; ci tóqi

m

; a k e ñkbt ò100

kó1

m

(3.24)

; ak e ñkbTi òei

kó1

One reason for specifying the price in terms of discount factors is that price of a bond in linear in discount factor, while it is non-linear in either forward or spot rates. We can simplify equation (3.24) further and write it as: mñ1

Ti

P(Ti )òA(Ti )ó ; a k ci ; e ñkbt ò100e ñkbTi kó1

tóqi

mñ1

ò 1ñ ; ak kó1

Ti

(3.25)

ci ; e ñmbt ò100e ñmbTi òei tóqi

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Rearranging, we get

Ti

P(Ti )òA(Ti )ñ ci ; e ñmbt ò100e ñmbTi Ti

ó ; ak tóqi

tóqi

Ti

(3.26)

Ti

ci ; e ñkbt ò100e ñkbTi ñ ci ; e ñmbt ò100e ñmbTi tóqi

tóqi

òei

or mñ1

yi ó ; ak xi , k òei

(3.27)

kó1

where

Ti

zi , k ó ci ; e ñkbt ò100e ñkbTi tóqi

yi óP(Ti )òA(Ti )ñzi , m

(3.28)

xi , k ózi , k ñzi , m To empirically estimate the discount function we first calculate yi and xi,k for each of the N bonds in our sample. The coefficient of the discount function must be selected such that they price the N bonds correctly or at least with minimum error. If we can set the b to be some sensible value then the a’s can be estimated using the ordinary least squares regression.

y1 x1,1 y2 x2,1 ó · · yN xN,1

x1,2 · · ·

· x1,m · · · · · xN,m

aˆ1 e1 aˆ2 e2 ò · · aˆm eN

(3.29)

The aˆ ’s estimated from the ordinary least squares provide the best fit for the data by minimizing the sum of the square of the errors, &Nió1 ei2 . The estimated values of aˆ and b can be substituted into (3.21) to determine the bond market yield curve. The model is sensitive to the number of basis functions therefore it should be carefully selected so as not to over-fit the data. Also, most of the models discussed are very sensitive to the data. Therefore, it is important to implement screening procedures to identify bonds and exclude any bonds that are outliers. Typically one tends to exclude bonds with unreliable prices or bonds that due to liquidity, coupon or tax reasons is expected to have be unusually rich or cheap. A better fit can also be achieved by iterative least squares. The model can be extend in several ways to obtain a better fit to the market data such as imposing constraint to fit certain data point exactly or assuming that the model is homoscedastic in yields and applying generalized least squares.

Exponential model implementation We now present the results from implementing the exponential model. The price data for a sample of German bond issues maturing less than 10 years and settling on 28 October 1998 is reported in Table 3.17.

The yield curve

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Table 3.17 DEM government bond price data

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Issue

Price

Yield

Accrued

Coupon

Maturity (years)

TOBL5 12/98 BKO3.75 3/99 DBR7 4/99 DBR7 10/99 TOBL7 11/99 BKO4.25 12/99 BKO4 3/0 DBR8.75 5/0 BKO4 6/0 DBR8.75 7/0 DBR9 10/0 OBL 118 OBL 121 OBL 122 OBL 123 OBL 124 THA7.75 10/2 OBL 125 THA7.375 12/2 OBL 126 DBR6.75 7/4 DBR6.5 10/5 DBR6 1/6 DBR6 2/6 DBR6 1/7 DBR6 7/7 DBR5.25 1/8 DBR4.75 7/8

100.21 100.13 101.68 103.41 103.74 100.94 100.81 108.02 100.95 108.81 110.56 104.05 103.41 102.80 102.94 103.06 114.81 104.99 113.78 103.35 114.74 114.85 111.75 111.91 112.05 112.43 107.96 104.81

3.3073 3.3376 3.3073 3.3916 3.3907 3.3851 3.3802 3.3840 3.3825 3.3963 3.3881 3.3920 3.5530 3.5840 3.5977 3.6218 3.6309 3.6480 3.6846 3.6409 3.8243 4.0119 4.0778 4.0768 4.2250 4.2542 4.1860 4.1345

4.3194 2.2813 3.6556 0.1556 6.4750 3.6715 2.4556 3.7917 1.4667 2.3819 0.2000 3.6021 4.4597 3.0750 2.0125 0.8625 0.5813 4.8056 6.6785 3.1250 1.9313 0.2528 4.8833 4.2000 4.9000 1.9000 4.2875 1.5042

5 3.75 7 7 7 4.25 4 8.75 4 8.75 9 5.25 4.75 4.5 4.5 4.5 7.75 5 7.375 4.5 6.75 6.5 6 6 6 6 5.25 4.75

0.1361 0.3917 0.4778 0.9778 1.0750 1.1361 1.3861 1.5667 1.6333 1.7278 1.9778 2.3139 3.0611 3.3167 3.5528 3.8083 3.9250 4.0389 4.0944 4.3056 5.7139 6.9611 7.1861 7.3000 8.1833 8.6833 9.1833 9.6833

Suppose that we choose to model the discount factor with 5 basis functions and let b equal to the yield of DBR4.75 7/2008. The first step is to calculate the zi,j , ió1 to 28, jó1 to 5. An example of the calculation for DBR6.75 7/4 is described below. This bond pays a coupon of 6.75%, matures in 5.139 years and the next coupon is payable in 0.7139 years from the settle date.

5

z21,2 ó 6.75 ; e ñ2î0.0413î(tò0.7139) ò100e ñ2î0.0413î5.7139 tó0

ó93.70 Similarly, we can calculate the zi,j for all the bonds in our sample and the results are shown in Table 3.18. Next we apply equation (3.28) to obtain the data for the ordinary least square regression estimation. This is reported in Table 3.19. Finally we estimate the a’s using ordinary least square regression and use it in equation (3.20) to generate the discount factors and yield curves. The coefficient

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The Professional’s Handbook of Financial Risk Management Table 3.18 DEM government bond zi,j calculation results

Issue TOBL5 12/98 BKO3.75 3/99 DBR7 4/99 DBR7 10/99 TOBL7 11/99 BKO4.25 12/99 BKO4 3/0 DBR8.75 5/0 BKO4 6/0 DBR8.75 7/0 DBR9 10/0 OBL 118 OBL 121 OBL 122 OBL 123 OBL 124 THA7.75 10/2 OBL 125 THA7.375 12/2 OBL 126 DBR6.75 7/4 DBR6.5 10/5 DBR6 1/6 DBR6 2/6 DBR6 1/7 DBR6 7/7 DBR5.25 1/8 DBR4.75 7/8

zi,1

zi,2

zi,3

zi,4

zi,5

104.41 102.08 104.91 102.76 109.33 103.69 102.14 110.48 101.11 109.74 109.09 105.80 105.94 103.90 102.89 101.81 113.09 107.64 118.30 104.18 114.51 113.75 115.70 115.15 116.98 114.58 111.97 105.62

103.82 100.44 102.86 98.69 104.86 99.10 96.61 103.89 94.66 102.51 100.86 96.75 94.41 91.57 89.80 87.92 97.76 92.87 102.50 88.77 93.70 89.48 91.23 90.37 89.92 86.27 83.45 76.72

103.24 98.83 100.84 94.78 100.58 94.73 91.39 97.70 88.63 95.77 93.26 88.50 84.21 80.78 78.45 76.00 84.63 80.31 89.06 75.79 77.03 70.87 72.64 71.62 70.05 65.84 63.28 56.68

102.66 97.24 98.87 91.02 96.49 90.55 86.45 91.89 82.98 89.48 86.25 80.99 75.20 71.34 68.61 65.77 73.37 69.61 77.61 64.85 63.63 56.57 58.47 57.38 55.40 51.01 48.92 42.67

102.09 95.68 96.94 87.42 92.57 86.56 81.78 86.45 77.71 83.61 79.77 74.16 67.24 63.07 60.07 56.98 63.71 60.50 67.85 55.64 52.85 45.54 47.64 46.53 44.54 40.17 38.62 32.80

estimates and the resultant discount factor curve are reported in Tables 3.20 and 3.21, respectively. The discount factor can be used for valuation, rich-cheap analysis or to generate the zero curve and the forward yield curves.

Model review The discount factors produced by the yield curve models are used for marking-tomarket of position and calculation of end-of-day gains/losses. Others bump the input cash and swap rates to the yield curve model to generate a new set of discount factors and revalue positions. This provides traders with an estimate of their exposure to different tenor and hedge ratios to manage risk of their positions. Models such as those of Heath, Jarrow and Morton (1992) and Brace, Gaterak and Musiela (1995) use the forward rates implied from the yield curve as a starting point to simulate the future evolution of the forward rate curve. Spot rate models such as those of Hull and White (1990), Black, Derman and Toy (1990) and Black and Karasinsky (1990) estimate parameters for the model by fitting it to the yield curve data. The model to

The yield curve

107 Table 3.19 DEM government bond xi,j regression data

Issue TOBL5 12/98 BKO3.75 3/99 DBR7 4/99 DBR7 10/99 TOBL7 11/99 BKO4.25 12/99 BKO4 3/0 DBR8.75 5/0 BKO4 6/0 DBR8.75 7/0 DBR9 10/0 OBL 118 OBL 121 OBL 122 OBL 123 OBL 124 THA7.75 10/2 OBL 125 THA7.375 12/2 OBL 126 DBR6.75 7/4 DBR6.5 10/5 DBR6 1/6 DBR6 2/6 DBR6 1/7 DBR6 7/7 DBR5.25 1/8 DBR4.75 7/8

yi

xi,1

xi,2

xi,3

xi,4

2.4427 6.7305 8.3987 16.1479 17.6444 18.0511 21.4836 25.3652 24.7094 27.5772 30.9865 33.4955 40.6304 42.8011 44.8833 46.9444 51.6844 49.2943 52.6131 50.8381 63.8172 69.5667 68.9955 69.5806 72.4097 74.1636 73.6259 73.5156

2.3240 6.4027 7.9702 15.3430 16.7562 17.1321 20.3622 24.0300 23.3980 26.1283 29.3115 31.6457 38.7041 40.8274 42.8227 44.8324 49.3862 47.1380 50.4555 48.5455 61.6541 68.2133 68.0584 68.6234 72.4375 74.4180 73.3496 72.8177

1.7381 4.7630 5.9182 11.2716 12.2852 12.5442 14.8299 17.4390 16.9496 18.8960 21.0826 22.5896 27.1683 28.4955 29.7297 30.9431 34.0537 32.3718 34.6570 33.1304 40.8501 43.9415 43.5888 43.8422 45.3753 46.1074 44.8310 43.9244

1.1555 3.1495 3.9064 7.3615 8.0077 8.1659 9.6033 11.2536 10.9182 12.1524 13.4866 14.3445 16.9745 17.7065 18.3797 19.0232 20.9184 19.8071 21.2112 20.1514 24.1722 25.3377 25.0013 25.0908 25.5126 25.6739 24.6579 23.8769

0.5761 1.5620 1.9339 3.6064 3.9153 3.9876 4.6653 5.4485 5.2768 5.8641 6.4741 6.8368 7.9648 8.2648 8.5376 8.7895 9.6586 9.1107 9.7599 9.2172 10.7782 11.0347 10.8359 10.8533 10.8640 10.8408 10.2981 9.8696

Table 3.20 DEM exponential model coefﬁcient estimations Coefﬁcient b a1 a2 a3 a4 a5

Estimate 4.13% 16.97 ñ77.59 139.55 ñ110.08 32.15

generate the yield curve model is not as complicated as some of the term structure models. However, any small error made while building the yield curve can have a progressively amplified impact on valuation and hedge ratios unless it has been reviewed carefully. We briefly outline some of the issues that must be kept in mind while validating them. First, the yield curve model should be arbitrage free. A quick check for this would

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The Professional’s Handbook of Financial Risk Management Table 3.21 DEM government bond market discount factor curve Time

Discount factor

Par coupon yield

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00

1.0000 0.9668 0.9353 0.9022 0.8665 0.8288 0.7904 0.7529 0.7180 0.6871 0.6613

3.44% 3.40% 3.49% 3.64% 3.80% 3.96% 4.09% 4.17% 4.20% 4.18%

be to verify if the discount factors generated by the yield curve model can produce the same cash and swap rates as those feed into the model. In addition there may be essentially four possible sources of errors – use of inappropriate market rates data, accrual factor calculations, interpolation algorithms, and curve-stitching. The rates used to build the curve for the short-term product will not be the same as the rates used for pricing long-term products. The individual desk primarily determines this so any curve builder model should offer flexibility to the user in selecting the source and the nature of rate data. Simple as it may seem, another common source of error is incorrect holiday calendar and market conventions for day count to calculate the accrual factors. Fortunately the computation of accrual factors is easy to verify. Interpolation algorithms expose the yield curve model to numerical instability. As we have mentioned earlier, some interpolation methods such as the cubic spline may be capable of producing a very smooth curve but performs poorly during computation of the hedge ratio. A preferable attribute for the interpolation method is to have a local impact on yield curve to changes in specific input data rather than affecting the entire curve. There are many systems that offer users a menu when it comes to the interpolation method. While it is good to have such flexibility, in the hands of a user with little understanding of the implications this may be risky. It is best to ensure that the model provides sensible alternatives and eliminate choices that may be considered unsuitable after sufficient research. In the absence of reasonable market data curve stitching can be achieved by interpolating either the stub rate or the discount factor. A linear interpolation can be applied for rates but not if it is a discount factor.

Summary In this chapter we have discussed the methodology to build the market yield curve for the swap market and the bond market. The market yield curve is one of the most important pieces of information required by traders and risk managers to price, trade, mark-to-market and control risk exposure. The yield curve can be described as discount factor curves, par curves, forward curves or zero curves. Since the

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discount factors are the most rudimentary information for valuing any stream of cash flows it is the most natural place to start. Unfortunately discount factors are not directly observable in the market, rather they have to be derived from the marketquoted interest rates and prices of liquid financial instruments. The swap market provides an abundance of par swap rates data for various tenors. This can be applied to extract the discount factors using the bootstrap method. The bootstrap approach produces a discount factor curve that is consistent with the market swap rates satisfying the condition of no-arbitrage condition. When a swap rate or discount factor for a specific date is not available then interpolation methods may need to be applied to determine the value. These methods must be carefully chosen since they can have significant impact on the resultant yield curve, valuations and risk exposure calculations. Linear interpolation for swap rates and exponential interpolation for discount factors is a recommended approach due to their simplicity and favorable performance attributes. In the bond market due to non-uniform price data on various tenors, coupon effect and liquidity factors a statistical method has to be applied to derive the discount factor curves. The objective is not necessarily to derive discount factors that will price every bond to the market exactly. Instead we estimate the parameters of the model that will minimize the sum of squares of pricing errors for the sample of bonds used. In theory many statistical models can be prescribed to fit the discount curve function. We have reviewed a few and provided details on the implementation of the exponential model. An important criterion for these models is that they satisfy certain basic constraints such as discount factor function equal to one on spot date, converge to zero for extremely long tenors, and be a decreasing function with respect to tenors.

Note 1

For an intuitive description of the futures convexity adjustment and calculations using this expression see Burghartt and Hoskins (1996). For other technical approaches to convexity adjustment see interest rate models such as Hull and White (1990) and Heath, Jarrow and Morton (1992).

References Anderson, N., Breedon, F., Deacon, M. and Murphy, G. (1997) Estimating and Interpreting the Yield Curve, John Wiley. Black, F., Derman, E. and Toy, W. (1990) ‘A one-factor model of interest rates and its application to treasury bond options’, Financial Analyst Journal, 46, 33–39. Black, F. and Karasinski, P. (1991) ‘Bond and option pricing when short rates are lognormal’, Financial Analyst Journal, 47, 52–9. Brace, A., Gatarek, D. and Musiela, M. (1997) ‘The market model of interest-rate dynamics’, Mathematical Finance, 7, 127–54. Burghartt, G. and Hoskins, B. (1996) ‘The convexity bias in Eurodollar futures’, in Konishi, A. and Dattatreya, R. (eds), Handbook of Derivative Instruments, Irwin Professional Publishing. Chambers, D., Carleton, W. and Waldman, D. (1984) ‘A new approach to estimation

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of the term structure of interest rates’, Journal of Financial and Quantitative Analysis, 19, 233–52. Coleman, T., Fisher, L. and Ibbotson, R. (1992) ‘Estimating the term structure of interest rates from data that include the prices of coupon bonds’, The Journal of Fixed Income, 85–116. Heath, D., Jarrow, R. and Morton, A. (1992) ‘Bond pricing and the term structure of interest rates: a new methodology for contingent claim valuation’, Econometrica 60, 77–105. Hull, J. and White, A. (1990) ‘Pricing interest rate derivative securities’, Review of Financial Studies, 3, 573–92. Jamshidian, F. (1997) ‘LIBOR and swap market models and measures’, Finance & Stochastics, 1, 261–91. McCulloch, J. H. (1971) ‘Measuring the term structure of interest rates’, Journal of Finance, 44, 19–31. McCulloch, J. H. (1975) ‘The tax-adjusted yield curve’, Journal of Finance, 30, 811–30. Nelson, C. R. and Siegel, A. F. (1987) ‘Parsimonious modeling of yield curves’, Journal of Business, 60, 473–89. Schaefer, S. M. (1981) ‘Measuring a tax-specific term structure of interest rates in the market for British government securities’, The Economic Journal, 91, 415–38. Shea, G. S. (1984) ‘Pitfalls in smoothing interest rate term structure data: equilibrium models and spline approximation’, Journal of Financial and Quantitative Analysis, 19, 253–69. Steeley, J. M. (1991) ‘Estimating the gilt-edged term structure: basis splines and confidence interval’, Journal of Business, Finance and Accounting, 18, 512–29. Vasicek, O. (1977) ‘An equilibrium characterization of the term structure’, Journal of Financial Economics, 5, 177–88. Vasicek, O. and Fong, H. (1982) ‘Term structure modeling using exponential splines’, Journal of Finance, 37, 339–56. Wiseman, J. (1994) European Fixed Income Research, 2nd edn, J. P. Morgan.

4

Choosing appropriate VaR model parameters and risk-measurement methods IAN HAWKINS Risk managers need a quantitative measure of market risk that can be applied to a single business, compared between multiple businesses, or aggregated across multiple businesses. The ‘Value at Risk’ or VaR of a business is a measure of how much money the business might lose over a period of time in the future. VaR has been widely adopted as the primary quantitative measure of market risk within banks and other financial service organizations. This chapter describes how we define VaR; what the major market risks are and how we measure the market risks in a portfolio of transactions; how we use models of market behavior to add up the risks; and how we estimate the parameters of those models. A sensible goal for risk managers is to implement a measure of market risk that conforms to industry best practice, with the proviso that they do so at reasonable cost. We will give two notes of caution. First, VaR is a necessary part of the firm-wide risk management framework, but not – on its own – sufficient to monitor market risk. VaR cannot replace the rich set of trading controls that most businesses accumulate over the years. These trading controls were either introduced to solve risk management problems in their own businesses or were implemented to respond to risk management problems that surfaced in other businesses. Over-reliance on VaR, or any other quantitative measure of risk, is simply an invitation for traders to build up large positions that fall outside the capabilities of the VaR implementation. Second, as with any model, VaR is subject to model risk, implementation risk and information risk. Model risk is the risk that we choose an inappropriate model to describe the real world. The real world is much more complicated than any mathematical model of the real world that we could create to describe it. We can only try to capture the most important features of the real world, as they affect our particular problem – the measurement of market risk – and, as the world changes, try to change our model quickly enough for the model to remain accurate (see Chapter 14).

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Implementation risk is the risk that we didn’t correctly translate our mathematical model into a working computer program – so that even if we do feed in the right numbers, we don’t get the answer from the program that we should. Finally, information risk is the risk that we don’t feed the right numbers into our computer program. VaR calculation is in its infancy, and risk managers have to accept a considerable degree of all three of these risks in their VaR measurement solutions.

Choosing appropriate VaR model parameters We will begin with a definition of VaR. Our VaR definition includes parameters, and we will go on to discuss how to choose each of those parameters in turn.

VaR deﬁnition The VaR of a portfolio of transactions is usually defined as the maximum loss, from an adverse market move, within a given level of confidence, for a given holding period. This is just one definition of risk, albeit one that has gained wide acceptance. Other possibilities are the maximum expected loss over a given holding period (our definition above without the qualifier of a given level of confidence) or the expected loss, over a specified confidence level, for a given holding period. If we used these alternative definitions, we would find it harder to calculate VaR; however, the VaR number would have more relevance. The first alternative definition is what every risk manager really wants to know – ‘How much could we lose tomorrow?’ The second alternative definition is the theoretical cost of an insurance policy that would cover any excess loss, over the standard VaR definition. For now, most practitioners use the standard VaR definition, while the researchers work on alternative VaR definitions and how to calculate VaR when using them (Artzner et al., 1997; Acar and Prieul, 1997; Embrechts et al., 1998; McNeil, 1998). To use our standard VaR definition, we have to choose values for the two parameters in the definition – confidence level and holding period.

Conﬁdence level Let’s look at the picture of our VaR definition shown in Figure 4.1. On the horizontal axis, we have the range of possible changes in the value of our portfolio of transactions. On the vertical axis, we have the probability of those possible changes occurring. The confidence levels commonly used in VaR calculations are 95% or 99%. Suppose we want to use 95%. To find the VaR of the portfolio, we put our finger on the right-hand side of the figure and move the finger left, until 95% of the possible changes are to the right of our finger and 5% of the changes are to the left of it. The number on the horizontal axis, under our finger, is the VaR of the portfolio. Using a 95% confidence interval means that, if our model is accurate, we expect to lose more than the VaR on only 5 days out of a 1001 . The VaR does not tell how much we might actually lose on those 5 days. Using a 99% confidence interval means that we expect to lose more than the VaR on only 1 day out of a 100. Most organizations use a confidence level somewhere between 95% and 99% for their inhouse risk management. The BIS (Bank for International Settlements) requirement for calculation of regulatory capital is a 99% confidence interval (Basel Committee on Banking Supervision, 1996). While the choice of a confidence level is a funda-

Choosing appropriate VaR model parameters and risk-measurement methods

113

Figure 4.1 VaR deﬁnition.

mental risk management statement for the organization, there is one modeling issue to bear in mind. The closer that the confidence level we choose is to 100%, the rarer are the events that lie to the left of our VaR line. That implies that we will have seen those events fewer times in the past, and that it will be harder for us to make accurate predictions about those rare events in the future. The standard VaR definition does not tell us much about the shape of the overall P/L distribution, other than the likelihood of a loss greater than the VaR. The distribution of portfolio change in value shown in Figure 4.2 results in the same VaR as the distribution in Figure 4.1, but obviously there is a greater chance of a loss of more than say, $4.5 million, in Figure 4.2 than in Figure 4.1.

Figure 4.2 VaR deﬁnition (2) – same VaR as Figure 4.1, but different tail risk.

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Holding period The holding period is the length of time, from today, to the horizon date at which we attempt to model the loss of our portfolio of transactions. There is an implicit assumption in VaR calculation that the portfolio is not going to change over the holding period. The first factor in our choice of holding period is the frequency with which new transactions are executed and the impact of the new transactions on the market risk of the portfolio. If those new transactions have a large impact on the market risk of the portfolio, it doesn’t make much sense to use a long holding period because it’s likely that the market risk of the portfolio will change significantly before we even reach the horizon date, and therefore our VaR calculation will be extremely inaccurate. The second factor in our choice of holding period is the frequency with which the data on the market risk of the portfolio can be assembled. We want to be able to look ahead at least to the next date on which we will have the data to create a new report. While banks are usually able to consolidate most of their portfolios at least once a day, for non-financial corporations, monthly or quarterly reporting is the norm. The third factor in our choice of holding period is the length of time it would take to hedge the risk positions of the portfolio at tolerable cost. The faster we try to hedge a position, the more we will move the market bid or offer price against ourselves, and so there is a balance between hedging risk rapidly to avoid further losses, and the cost of hedging. It’s unreasonable to try to estimate our maximum downside using a holding period that is significantly shorter than the time it would take to hedge the position. The ability to hedge a position is different for different instruments and different markets, and is affected by the size of the position. The larger the position is, in relation to the normal size of transactions traded in the market, the larger the impact that hedging that position will have on market prices. The impact of hedging on prices also changes over time. We can account for this third factor in a different way – by setting aside P/L reserves against open market risk positions. For most institutions, reserves are a much more practical way of incorporating liquidity into VaR than actually modeling the liquidation process. In banks, the most common choice of holding period for internal VaR calculations is 1 day. For bank regulatory capital calculations, the BIS specifies a 10-day holding period, but allows banks to calculate their 10-day VaR by multiplying their 1-day VaR by the square root of 10 (about 3.16). If, for example, a bank’s 1-day VaR was $1.5 million, its 10-day VaR would be $4.74 million ($1.5 million *3.16). Using the square root of time to scale VaR from one time horizon to another is valid if market moves are independently distributed over time (i.e. market variables do not revert to the mean, or show autocorrelation). For the purpose of calculating VaR, this assumption is close enough to reality, though we know that most market variables do actually show mean reversion and autocorrelation.

Applicability of VaR If all assets and liabilities are accounted for on a mark-to-market basis, for example financial instruments in a bank trading book or corporate Treasury, then we can use VaR directly. If assets and liabilities are not all marked to market, we can either

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estimate proxy market values of items that are accrual-accounted, and then use VaR, or use alternative measures of risk to VaR – that are derived from a more tradition ALM perspective. Some examples of alternative measures of performance to mark-to-market value are projected earnings, cash flow and cost of funds – giving rise to risk measures such as Earnings at Risk, Cash flow at Risk or Cost of Funds at Risk.

Uses of VaR To place the remainder of the chapter in context, we will briefly discuss some of the uses of VaR measures.

Regulatory market risk capital Much of the current interest in VaR has been driven by the desire of banks to align their regulatory capital with the bank’s perception of economic capital employed in the trading book, and to minimize the amount of capital allocated to their trading books, by the use of internal VaR models rather than the ‘BIS standardized approach’ to calculation of regulatory capital.

Internal capital allocation Given that VaR provides a metric for the economic capital that must be set aside to cover market risk, we can then use the VaR of a business to measure the returns of that business adjusted for the use of risk capital. There are many flavors of riskadjusted returns, and depending on the intended use of the performance measure, we may wish to consider the VaR of the business on a stand-alone basis, or the incremental VaR of the business as part of the whole organization, taking into account any reduction in risk capital due to diversification.

Market risk limits VaR can certainly be used as the basis of a limits system, so that risk-reducing actions are triggered when VaR exceeds a predefined level. In setting VaR limits, we must consider how a market loss typically arises. First we experience an adverse move and realize losses of the order of the VaR. Then over the next few days or weeks we experience more adverse moves and we lose more money. Then we review our position, the position’s mark to market, and the model used to generate the mark to market. Then we implement changes, revising the mark to market by writing down the value of our position, and/or introducing a new model, and/or reducing our risk appetite and beginning the liquidation of our position. We lose multiples of the VaR, then we rethink what we have been doing and take a further hit as we make changes and we pay to liquidate the position. The bill for the lot is more than the sum of the VaR and our liquidity reserves.

Risk measurement methods This section describes what the sources of market risk are, and how we measure them for a portfolio of transactions. First, a definition of market risk: ‘Market risk is

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the potential adverse change in the value of a portfolio of financial instruments due to changes in the levels, or changes in the volatilities of the levels, or changes in the correlations between the levels of market prices.’ 2 Risk comes from the combination of uncertainty and exposure to that uncertainty. There is no risk in holding a stock if we are completely certain about the future path of the stock’s market price, and even if we are uncertain about the future path of a stock’s market price, there is no risk to us if we don’t hold a position in the stock!

Market risk versus credit risk It is difficult to cleanly differentiate market risk from credit risk. Credit risk is the risk of loss when our counterpart in a transaction defaults on (fails to perform) its contractual obligations, due to an inability to pay (as opposed to an unwillingness to pay). The risk of default in a loan with a counterpart will usually be classified as credit risk. The risk of default in holding a counterpart’s bond, will also usually be classified as credit risk. However, the risk that the bond will change in price, because the market’s view of the likelihood of default by the counterpart changes, will usually be classified as a market risk. In most banks, the amount of market risk underwritten by the bank is dwarfed in size by the amount of credit risk underwritten by the bank. While banks’ trading losses attract a great deal of publicity, particularly if the losses involve derivatives, banks typically write off much larger amounts of money against credit losses from non-performing loans and financial guarantees.

General market risk versus speciﬁc risk. When analyzing market risk, we break down the risk of a transaction into two components. The change in the transaction’s value correlated with the behavior of the market as a whole is known as systematic risk, or general market risk. The change in the transaction’s value not correlated with the behavior of the market as a whole is known as idiosyncratic risk, or specific risk. The factors that contribute to specific risk are changes in the perception of the credit quality of the underlying issuer or counterpart to a transaction, as we discussed above, and supply and demand, which we will discuss below. Consider two bonds, with the same issuer, and of similar maturities, say 9 years and 11 years. The bonds have similar systematic risk profiles, as they are both sensitive to interest rates of around 10 years maturity. However, they are not fungible, (can’t be exchanged for each other). Therefore, supply and demand for each individual bond may cause the individual bond’s actual price movements to be significantly different from that expected due to changes in the market as a whole. Suppose we sell the 9-year bond short, and buy the same notional amount of the 11-year bond. Overall, our portfolio of two transactions has small net general market risk, as the exposure of the short 9-year bond position to the general level of interest rates will be largely offset by the exposure of the long 11-year bond position to the general level of interest rates. Still, the portfolio has significant specific risk, as any divergence in the price changes of our two bonds from that expected for the market as a whole will cause significant unexpected changes in portfolio value. The only way to reduce the specific risk of a portfolio is to diversify the portfolio holdings across a large number of different instruments, so that the contribution to changes in portfolio value from each individual instrument are small relative to the total changes in portfolio value.

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VaR typically only measures general market risk, while specific risk is captured in a separate risk measurement.

Sources of market risk Now we will work through the sources of market uncertainty and how we quantify the exposure to that uncertainty.

Price level risk The major sources of market risk are changes in the levels of foreign exchange rates, interest rates, commodity prices and equity prices. We will discuss each of these sources of market risk in turn.

FX rate risk In the most general terms, we can describe the value of a portfolio as its expected future cash flows, discounted back to today. Whenever a portfolio contains cash flows denominated in, or indexed to, a currency other than the base currency of the business, the value of the portfolio is sensitive to changes in the level of foreign exchange rates. There are a number of different ways to represent the FX exposure of a portfolio. Currency pairs A natural way to represent a portfolio of foreign exchange transactions is to reduce the portfolio to a set of equivalent positions in currency pairs: so much EUR-USD at exchange rate 1, so much JPY-USD at exchange rate 2, so much EUR-JPY at exchange rate 3. As even this simple example shows, currency pairs require care in handling positions in crosses (currency pairs in which neither currency is the base currency). Risk point method We can let our portfolio management system do the work, and have the system revalue the portfolio for a defined change in each exchange rate used to mark the portfolio to market, and report the change in value of the portfolio for each change in exchange rate. This process is known as the Risk Point Method, or more informally as ‘bump and grind’ (bump the exchange rate and grind through the portfolio revaluation). As with currency pairs, we need to make sure that exposure in crosses is not double counted, or inconsistent with the treatment of the underlying currency pairs. Cash ﬂow mapping A more atomic representation of foreign exchange exposure is to map each forward cash flow in the portfolio to an equivalent amount of spot cash flow in that currency: so many EUR, so many JPY, so many USD. Reference (risk) currency versus base (accounting) currency Global trading groups often denominate their results in US dollars (reference currency) and consider US dollars as the currency that has no foreign exchange risk.

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When those trading groups belong to business whose base currency is not US dollars, some care is required to make sure that the group is not subject to the risk that a change in the US dollar exchange rate to the base currency will cause unexpected over- or under-performance relative to the budget set in the base currency.

Interest rate risk It is fairly obvious that the level of interest rates affects fixed-income securities, but – as we have seen earlier in this book – the level of interest rates also affects the prices of all futures and forward contracts relative to spot prices. Buying a currency for delivery forward is equivalent to buying the currency spot and lending/borrowing the proceeds of the spot transaction. The interest rates at which the proceeds of the spot transaction are lent or borrowed determine the ‘no-arbitrage’ forward exchange rate relative to the spot exchange rate. Going back to our general statement that the value of a portfolio is the discounted value of its expected cash flows, it follows that a portfolio that has any future cash flows will be subject to some degree of interest rate risk. As with foreign exchange risk, there are several ways we can quantify interest rate exposure. Cash ﬂow mapping We can map each cash flow in the portfolio to a standard set of maturities from today out to, say, 30-years. Each cash flow will lie between two standard maturities. The cash flow can be allocated between the two maturities according to some rule. For instance, we might want the present value of the cash flow, and the sensitivity of the cash flow to a parallel shift in the yield curve, to equal the present value and sensitivity of the two cash flows after the mapping. Duration bucketing Alternatively, we can take a portfolio of bonds and summarize its exposure to interest rates by bucketing the PV01 of each bond position according to the duration of each bond. Risk point method Finally, as with foreign exchange, one can let the portfolio system do the work, and revalue the portfolio for a defined change in each interest rate, rather than each foreign exchange rate. Principal component analysis (PCA) Given one of these measures of exposure we now have to measure the uncertainty in interest rates, so we can apply the uncertainty measure to the exposure measure and obtain a possible change in mark-to-market value. We know that the level and shape of the yield curve changes in a complicated fashion over time – we see the yield curve move up and down, and back and forth between its normal, upwardsloping, shape and flat or inverted (downward-sloping) shapes. One way to capture this uncertainty is to measure the standard deviation of the changes in the yield at each maturity on the yield curve, and the correlation between the changes in the yields at each pair of maturities. Table 4.1 shows the results of analyzing CMT (Constant Maturity Treasury) data from the US Treasury’s H.15 release over the period from 1982 to 1998. Reading down to the bottom of the

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Table 4.1 CMT yield curve standard deviations and correlation matrix Maturity Standard in years deviation 0.25 0.5 1 2 3 5 7 10 20 30

1.26% 1.25% 1.26% 1.26% 1.25% 1.19% 1.14% 1.10% 1.05% 0.99%

Correlation 0.25

0.5

1

2

3

5

7

10

20

30

100% 93% 82% 73% 67% 60% 56% 54% 52% 50%

100% 96% 89% 85% 79% 75% 73% 70% 67%

100% 97% 94% 90% 87% 84% 80% 77%

100% 99% 97% 94% 92% 87% 85%

100% 99% 97% 95% 91% 89%

100% 99% 98% 94% 92%

100% 99% 96% 95%

100% 98% 97%

100% 98%

100%

standard deviation column we can see that the 30-year CMT yield changes by about 99 bps (basis points) per annum. Moving across from the standard deviation of the 30-year CMT to the bottom-left entry of the correlation matrix, we see that the correlation between changes in the 30-year CMT yield and the 3-month CMT yield is about 50%. For most of us, this table is a fairly unwieldy way of capturing the changes in the yield curve. If, as in this example, we use 10 maturity points on the yield curve in each currency that we have to model, then to model two curves, we will need a correlation matrix that has 400 (20î20) values. As we add currencies the size of the correlation matrix will grow very rapidly. For the G7 currencies we would require a correlation matrix with 4900 (70*70) values. There is a standard statistical technique, called principal component analysis, which allows us to approximate the correlation matrix with a much smaller data set. Table 4.2 shows the results of applying a matrix operation called eigenvalue/ eigenvector decomposition to the product of the standard deviation vector and the correlation matrix. The decomposition allows us to extract common factors from the correlation matrix, which describe how the yield curve moves as a whole. Looking Table 4.2 CMT yield curve factors Proportion of variance 86.8% 10.5% 1.8% 0.4% 0.2% 0.1% 0.1% 0.0% 0.0% 0.0%

Factor shocks 0.25

0.5

1

2

3

5

7

10

20

30

0.95% 1.13% 1.21% 1.25% 1.23% 1.16% 1.10% 1.04% 0.96% 0.89% ñ0.79% ñ0.53% ñ0.26% ñ0.03% 0.10% 0.23% 0.30% 0.33% 0.34% 0.34% ñ0.23% ñ0.01% 0.16% 0.19% 0.16% 0.08% 0.00% ñ0.08% ñ0.20% ñ0.24% ñ0.11% 0.10% 0.11% ñ0.01% ñ0.07% ñ0.08% ñ0.04% ñ0.01% 0.05% 0.05% 0.01% ñ0.06% 0.01% 0.05% 0.07% ñ0.03% ñ0.08% ñ0.07% 0.01% 0.08% ñ0.01% 0.03% ñ0.01% ñ0.03% 0.01% 0.01% 0.00% 0.03% ñ0.10% 0.07% 0.02% ñ0.06% 0.06% ñ0.01% ñ0.03% ñ0.01% 0.03% 0.01% ñ0.02% 0.00% 0.00% 0.01% ñ0.01% 0.00% 0.00% 0.00% 0.04% ñ0.05% 0.00% 0.01% 0.00% 0.00% ñ0.02% 0.06% ñ0.05% 0.01% 0.00% 0.00% ñ0.01% 0.01% 0.00% 0.00% ñ0.02% 0.02% 0.02% ñ0.06% 0.02% 0.02% ñ0.01% ñ0.01%

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down the first column of the table, we see that the first three factors explain over 95% of the variance of interest rates! The first three factors have an intuitive interpretation as a shift movement, a tilt movement and a bend movement – we can see this more easily from Figure 4.3, which shows a plot of the yield curve movements by maturity for each factor.

Figure 4.3 Yield curve factors.

In the shift movement, yields in all maturities change in the same direction, though not necessarily by the same amount: if the 3-month yield moves up by 95 bps, the 2-year yield moves up by 125 bps and the 30Y yield moves up by 89 bps. The sizes of the changes are annualized standard deviations. In the bend movement, the short and long maturities change in opposite directions: if the 3-month yield moves down by 79 bps, the 2-year yield moves down by 3 bps (i.e. it’s almost unchanged) and the 30Y yield moves up by 34 bps. In the bend movement, yields in the short and long maturities change in the same direction, while yields in the intermediate maturities change in the opposite direction: if the 3-month yield moves down by 23 bps, the 2year yield moves up by 19 bps and the 30Y yield moves down by 24 bps. As we move to higher factors, the sizes of the yield changes decrease, and the sign of the changes flips more often as we read across the maturities. If we approximate the changes in the yield curve using just the first few factors we significantly reduce the dimensions of the correlation matrix, without giving up a great deal of modeling accuracy – and the factors are, by construction, uncorrelated with each other.

Commodity price risk At first glance, commodity transactions look very much like foreign exchange transactions. However, unlike currencies, almost cost-less electronic ‘book-entry’ transfers of commodities are not the norm. Commodity contracts specify the form and location of the commodity that is to be delivered. For example, a contract to buy copper will specify the metal purity, bar size and shape, and acceptable warehouse locations that the copper may be sent to. Transportation, storage and insurance are significant factors in the pricing of forward contracts. The basic arbitrage relationship for a commodity forward is that

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the cost of buying the spot commodity, borrowing the money, and paying the storage and insurance must be more than or equal to the forward price. The cost of borrowing a commodity is sometimes referred to as convenience yield. Having the spot commodity allows manufacturers to keep their plants running. The convenience yield may be more than the costs of borrowing money, storage and insurance. Unlike in the foreign exchange markets, arbitrage of spot and forward prices in the commodity markets is not necessarily straightforward or even possible: Ω Oil pipelines pump oil only in one direction. Oil refineries crack crude to produce gasoline, and other products, but can’t be put into reverse and turn gasoline back into crude oil. Ω Arbitrage of New York and London gold requires renting armored cars, a Jumbo jet and a gold refinery (to melt down 400 oz good delivery London bars and cast them into 100 oz good delivery Comex bars). Ω Soft commodities spoil: you can’t let sacks of coffee beans sit in a warehouse forever – they rot! The impact on risk measurement of the constraints on arbitrage is that we have to be very careful about aggregating positions: whether across different time horizons, across different delivery locations or across different delivery grades. These mismatches are very significant sources of exposure in commodities, and risk managers should check, particularly if their risk measurement systems were developed for financial instruments, that commodity exposures are not understated by netting of longs and shorts across time, locations or deliverables that conceals significant risks.

Equity price risk Creating a table of standard deviations and correlations is unwieldy for modeling yield curves that are made up of ten or so maturities in each currency. To model equity markets we have to consider hundreds or possibly even thousands of listed companies in each currency. Given that the correlation matrix for even a hundred equities would have 10 000 entries, it is not surprising that factor models are used extensively in modeling equity market returns and risk. We will give a brief overview of some of the modeling alternatives.3 Single-factor models and beta Single-factor models relate the return on an equity to the return on a stock market index: ReturnOnStockóai òbi* ReturnOnIndexòei bi is cov(ReturnOnStock,ReturnOnIndex)/ var(ReturnOnIndex) ei is uncorrelated with the return on the index The return on the stock is split into three components: a random general market return, measured as b multiplied by the market index return, which can’t be diversified, (so you do get paid for assuming the risk); a random idiosyncratic return, e, which can be diversified (so you don’t get paid for assuming the risk); and an expected idiosyncratic return a. This implies that all stocks move up and down together, differing only in the magnitude of their movements relative to the market index, the b, and the magnitude of an idiosyncratic return that is uncorrelated with either the market index return

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or the idiosyncratic return of any other equity. In practice many market participants use b as the primary measure of the market risk of an individual equity and their portfolio as a whole. While this is a very simple and attractive model, a single factor only explains about 35% of the variance of equity returns, compared to the singlefactor yield curve model, which explained almost 90% of the yield curve variance. Multi-factor models/APT The low explanatory power of single-factor models has led researchers to use multifactor models. One obvious approach is to follow the same steps we described for interest rates: calculate the correlation matrix, decompose the matrix into eigenvalues and eigenvectors, and select a subset of the factors to describe equity returns. More commonly, analysts use fundamental factors (such as Price/Earnings ratio, Style[Value, Growth, . . .], Market capitalization and Industry [Banking, Transportation, eCommerce, . . .]), or macro-economic factors (such as the Oil price, the Yield curve slope, Inflation, . . .) to model equity returns. Correlation and concentration risk We can break equity risk management into two tasks: managing the overall market risk exposure to the factor(s) in our model, and managing the concentration of risk in individual equities. This is a lot easier than trying to use the whole variance–covariance matrix, and follows the same logic as using factor models for yield curve analysis. Dividend and stock loan risk As with any forward transaction, the forward price of an equity is determined by the cost of borrowing the two deliverables, in this case the cost of borrowing money, and the cost of borrowing stock. In addition, the forward equity price is affected by any known cash flows (such as expected dividend payments) that will be paid before the forward date. Relative to the repo market for US Treasuries, the stock loan market is considerably less liquid and less transparent. Indexing benchmark risk We are free to define ‘risk’. While banks typically have absolute return on equity (ROE) targets and manage profit or loss relative to a fixed budget, in the asset management business, many participants have a return target that is variable, and related to the return on an index. In the simplest case, the asset manager’s risk is of a shortfall relative to the index the manager tracks, such as the S&P500. In effect this transforms the VaR analysis to a different ‘currency’ – that of the index, and we look at the price risk of a portfolio in units of, say, S&P500s, rather than dollars.

Price volatility risk One obvious effect of a change in price volatility is that it changes the VaR! Option products, and more generally instruments that have convexity in their value with respect to the level of prices, are affected by the volatility of prices. We can handle exposure to the volatility of prices in the same way as exposure to prices. We measure the exposure (or vega, or kappa4) of the portfolio to a change in volatility, we measure the uncertainty in volatility, and we bring the two together in a VaR calculation. In

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much the same way as the price level changes over time, price volatility also changes over time, so we measure the volatility of volatility to capture this uncertainty.

Price correlation risk In the early 1990s there was an explosion in market appetite for structured notes, many of which contained option products whose payoffs depended on more than one underlying price. Enthusiasm for structured notes and more exotic options has cooled, at least in part due to the well-publicized problems of some US corporations and money market investors, the subsequent tightening of corporate risk management standards, and the subsequent tightening of the list of allowable investment products for money market funds. However, there are still many actively traded options that depend on two or more prices – some from the older generation of exotic products, such as options on baskets of currencies or other assets, and some that were developed for the structured note market, such as ‘diff swaps’ and ‘quantos’. Options whose payoffs depend on two or more prices have exposure to the volatility of each of the underlying prices and the correlation between each pair of prices.

Pre-payment variance risk Mortgage-backed-securities (MBS) and Asset-backed-securities (ABS) are similar to callable bonds. The investor in the bond has sold the borrower (ultimately a homeowner with a mortgage, or a consumer with credit card or other debt) the option to pay off their debt early. Like callable debt, the value of the prepayment option depends directly on the level and shape of the yield curve, and the level and shape of the volatility curve. For instance, when rates fall, homeowners refinance and the MBS prepays principal back to the investor that the MBS investor has to reinvest at a lower rate than the MBS coupon. MBS prepayment risk can be hedged at a macro level by buying receivers swaptions or CMT floors, struck below the money: the rise in the value of the option offsets the fall in the MBS price. Unlike callable debt, the value of the prepayment option is also indirectly affected by the yield curve, and by other factors, which may not be present in the yield curve data. First, people move! Mortgages (except GNMA mortgages) are not usually assumable by the new buyer. Therefore to move, the homeowner may pay down a mortgage even if it is uneconomic to do so. The housing market is seasonal – most people contract to buy their houses in spring and close in summer. The overall state of the economy is also important: in a downturn, people lose their jobs. Rather than submit to a new credit check, a homeowner may not refinance, even if it would be economic to do so. So, in addition to the yield curve factors, a prepayment model will include an econometric model of the impact of the factors that analysts believe determine prepayment speeds: pool coupon relative to market mortgage rates, pool size, pool seasoning/burn-out: past yield curve and prepayment history, geographic composition of the pool, and seasonality. A quick comparison of Street models on the Bloomberg shows a wide range of projected speeds for any given pool! Predicting future prepayments is still as much an art as a science. The MBS market is both large and mature. Figure 4.4 shows the relative risk and return of the plain vanilla pass-through securities, and two types of derived securities that assign the cash flows of a pass-through security in different ways – stable

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Figure 4.4 Relative risk of different types of MBS (after Cheyette, Journal of Portfolio Management, Fall 1996).

tranches, which provide some degree of protection from prepayment risk to some tranche holders at the expense of increased prepayment risk for other tranche holders, and IOs/POs which separate the principal and interest payments of a passthrough security. Under the general heading of prepayment risk we should also mention that there are other contracts that may suffer prepayment or early termination due to external factors other than market prices. Once prepayment or early termination occurs, the contracts may lose value. For example, synthetic guaranteed investment contracts (‘GIC wraps’), which provide for the return of principal on a pension plan investment, have a legal maturity date, but typically also have provisions for compensation of the plan sponsor if a significant number of employees withdraw from the pension plan early, and their investments have lost money. Exogenous events that are difficult to hedge against may trigger withdrawals from the plan: layoffs following a merger or acquisition, declining prospects for the particular company causing employees to move on, and so on.

Portfolio response to market changes Following our brief catalogue of market risks, we now look at how the portfolio changes in value in response to changes in market prices, and how we can summarize those changes in value.

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Linear versus non-linear change in value Suppose we draw a graph of the change in value of the portfolio for a change in a market variable. If it’s a straight line, the change in value is linear in that variable. If it’s curved, the change in value is non-linear in that variable. Figure 4.5 shows a linear risk on the left and a non-linear risk on the right.

Figure 4.5 Linear versus non-linear change in value.

If change in value of the portfolio is completely linear in a price, we can summarize the change in value with a single number, the delta, or first derivative of the change in value with respect to the price. If the change in value is not a straight line, we can use higher derivatives to summarize the change in value. The second derivative is known as the gamma of the portfolio. There is no standard terminology for the third derivative on. In practice, if the change in value of the portfolio can’t be captured accurately with one or two derivatives then we simply store a table of the change in value for the range of the price we are considering! Using two or more derivatives to approximate the change in value of a variable is known as a Taylor series expansion of the value of the variable. In our case, if we wanted to estimate the change in portfolio value given the delta and gamma, we would use the following formula: Change in portfolio valueóDELTA ¥ (change in price)ò1/2 GAMMA ¥ (change in price)2 If the formula looks familiar, it may be because one common application of a Taylor series expansion in finance is the use of modified duration and convexity to estimate the change in value of a bond for a change in yield. If that doesn’t ring a bell, then perhaps the equation for a parabola ( yóaxòbx 2), from your high school math class, does.

Discontinuous change in value Both the graphs in Figure 4.5 could be drawn with a smooth line. Some products, such as digital options, give risk to jumps, or discontinuities, in the graph of portfolio change in value, as we show in Figure 4.6. Discontinuous changes in value are difficult to capture accurately with Taylor series.

Path-dependent change in value Risk has a time dimension. We measure the change in the value of the portfolio over time. The potential change in value, estimated today, between today and a future

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Figure 4.6 Discontinuous change in value.

date, may depend on what happens in the intervening period of time, not just on the state of the world at the future date. This can be because of the payoff of the product (for example, a barrier option) or the actions of the trader (for example, a stop loss order). Let’s take the case of a stop loss order. Suppose a trader buys an equity for 100 USD. The trader enters a stop loss order to sell at 90 USD. Suppose at the end of the time period we are considering the equity is trading at 115 USD. Before we can determine the value of the trader’s portfolio, we must first find out whether the equity traded at 90 USD or below after the trader bought it. If so, the trader would have been stopped out, selling the position at 90 USD (more probably a little below 90 USD); would have lost 10 USD on the trade and would currently have no position. If not, the trader would have a mark-to-market gain on the position of 15 USD, and would still be long. Taylor series don’t help us at all with this type of behavior. When is a Taylor series inaccurate? Ω Large moves: if the portfolio change in value is not accurately captured by one or two derivatives,5 then the larger the change in price over which we estimate the change in value, the larger the slippage between the Taylor series estimate and the true portfolio change in value. For a large change in price, we have to change the inputs to our valuation model, recalculate the portfolio value, and take the difference from the initial value. Ω Significant cross-partials: our Taylor series example assumed that the changes in value of the portfolio for each different risk factor that affects the value of the portfolio are independent. If not, then we have to add terms to the Taylor series expansion to capture these cross-partial sensitivities. For example, if our vega changes with the level of market prices, then we need to add the derivative of vega with respect to prices to our Taylor series expansion to make accurate estimates of the change in portfolio value.

Risk reports Risk managers spend a great deal of time surveying risk reports. It is important that risk managers understand exactly what the numbers they are looking at mean. As we have discussed above, exposures are usually captured by a measure of the derivative of the change in value of a portfolio to a given price or other risk factor.

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The derivative may be calculated as a true derivative, obtained by differentiating the valuation formula with respect to the risk factor, or as a numerical derivative, calculated by changing the value of the risk factor and rerunning the valuation to see how much the value changes. Numerical derivatives can be calculated by just moving the risk factor one way from the initial value (a one-sided difference), or preferably, by moving the risk factor up and down (a central difference). In some cases, the differences between true, one-sided numerical, and central numerical derivatives are significant. Bucket reports versus factor reports A bucket report takes a property of the trade (say, notional amount), or the output of a valuation model (say, delta), and allocates the property to one or two buckets according to a trade parameter, such as maturity. For example, the sensitivity of a bond to a parallel shift in the yield curve of 1 bp, might be bucketed by the maturity of the bond. A factor report bumps model inputs and shows the change in the present value of the portfolio for each change in the model input. For example, the sensitivity of a portfolio of FRAs and swaps to a 1 bp change in each input to the swap curve (money market rates, Eurodollar futures prices, US Treasuries and swap spreads). Both types of report are useful, but risk managers need to be certain what they are looking at! When tracking down the sources of an exposure, we often work from a factor report (say, sensitivity of an options portfolio to changes in the volatility smile) to a bucket report (say, option notional by strike and maturity) to a transaction list (transactions by strike and maturity).

Hidden exposures The exposures that are not captured at all by the risk-monitoring systems are the ones that are most likely to lose risk managers their jobs. Part of the art of risk management is deciding what exposures to monitor and aggregate through the organization, and what exposures to omit from this process. Risk managers must establish criteria to determine when exposures must be included in the monitoring process, and must establish a regular review to monitor whether exposures meet the criteria. We will point out a few exposures, which are often not monitored, but may in fact be significant: Ω Swaps portfolios: the basis between Eurodollar money market rates, and the rates implied by Eurodollar futures; and the basis between 3M Libor and other floating rate frequencies (1M Libor, 6M Libor and 12M Libor). Ω Options portfolios: the volatility smile. Ω Long-dated option portfolios: interest rates, and borrowing costs for the underlying (i.e. repo rates for bond options, dividend and stock loan rates for equity options, metal borrowing rates for bullion options, . . .) Positions that are not entered in the risk monitoring systems at all are a major source of problems. Typically, new products are first valued and risk managed in spreadsheets, before inclusion in the production systems. One approach is to require that even spreadsheet systems adhere to a firm-wide risk-reporting interface and that spreadsheet systems hand off the exposures of the positions they contain to the

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production systems. A second alternative is to limit the maximum exposure that may be assumed while the positions are managed in spreadsheets. Risk managers have to balance the potential for loss due to inadequate monitoring with the potential loss of revenue from turning away transactions simply because the transactions would have to be managed in spreadsheets (or some other non-production system). In general, trading portfolios have gross exposures that are much larger than the net exposures. Risk managers have to take a hard look at the process by which exposures are netted against each other, to ensure that significant risks are not being masked by the netting process.

Market parameter estimation As we will see below, risk-aggregation methods rely on assumptions about the distributions of market prices and estimates of the volatility and correlation of market prices. These assumptions quantify the uncertainty in market prices. When we attempt to model the market, we are modeling human not physical behavior (because market prices are set by the interaction of traders). Human behavior isn’t always rational, or consistent, and changes over time. While we can apply techniques from the physical sciences to modeling the market, we have to remember that our calculations may be made completely inaccurate by changes in human behavior. The good news is that we are not trying to build valuation models for all the products in our portfolio, we are just trying to get some measure of the risk of those products over a relatively short time horizon. The bad news is that we are trying to model rare events – what happens in the tails of the probability distribution. By definition, we have a lot less information about past rare events than about past common events.

Choice of distribution To begin, we have to choose a probability distribution for changes in market prices – usually either the normal or the log-normal distribution. We also usually assume that the changes in one period are independent of the changes in the previous period, and finally we assume that the properties of the probability distribution are constant over time. There is a large body of research on non-parametric estimation of the distributions of market prices. Non-parametric methods are techniques for extracting the real-world probability distribution from large quantities of observed data, without making many assumptions about the actual distribution beforehand. The research typically shows that market prices are best described by complex mixtures of many different distributions that have changing properties over time (Ait-Sahalia, 1996, 1997; Wilmot et al., 1995). That said, we use the normal distribution, not because it’s a great fit to the data or the market, but because we can scale variance over time easily (which means we can translate VaR to a different time horizon easily), because we can calculate the variance of linear sums of normal variables easily (which means we can add risks easily), and because we can calculate the moments of functions of normal variables easily (which means we can approximate the behavior of some other distributions easily)!

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Figure 4.7 Normally and log-normally distributed variables.

As its name suggests, the log-normal distribution is closely related to the normal distribution. Figure 4.7 shows the familiar ‘bell curve’ shape of the probability density of a normally distributed variable with a mean of 100 and a standard deviation of 40. When we use the normal distribution to describe prices, we assume that up-anddown moves of a certain absolute size are equally likely. If the moves are large enough, the price can become negative, which is unrealistic if we are describing a stock price or a bond price. To avoid this problem, we can use the log-normal distribution. The log-normal distribution assumes that proportional moves in the stock price are equally likely.6 Looking at Figure 4.7 we see that, compared to the normal distribution, for the log-normal distribution the range of lower prices is compressed, and the range of higher prices is enlarged. In the middle of the price range the two distributions are very similar. One other note about the log-normal distribution – market participants usually refer to the standard deviation of a lognormal variable as the variable’s volatility. Volatility is typically quoted as a percentage change per annum. To investigate the actual distribution of rates we can use standardized variables. To standardize a variable we take each of the original values in turn, subtracting the average value of the whole data set and dividing through by the standard deviation of the whole data set: Standard variable valueó(original variable valueñaverage of original variable)/ standard deviation of original variable After standardization the mean of the standard variable is 0, and the standard deviation of the new variable is 1. If we want to compare different data sets, it is much easier to see the differences between the data sets if they all have the same mean and standard deviation to start with. We can do the comparisons by looking at probability density plots. Figure 4.8 shows the frequency of observations of market changes for four years

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Figure 4.8 Empirical FX data compared with the normal and log-normal distributions.

of actual foreign exchange rate data (DEM–USD, JPY–USD and GBP–USD), overlaid on the normal distribution. On the left-hand side of the figure the absolute rate changes are plotted, while on the right-hand side the changes in the logarithms of the rate are plotted. If foreign exchange rates were normally distributed, the colored points on the left-hand side of the figure would plot on top of the smooth black line representing the normal distribution. If foreign exchange rates were log-normally distributed, the colored points in the right-hand side of the figure would plot on top of the normal distribution. In fact, it’s quite hard to tell the difference between the two figures: neither the normal nor the log-normal distribution does a great job of matching the actual data. Both distributions fail to predict the frequency of large moves in the actual data – the ‘fat tails’ or leptokurtosis of most financial variables. For our purposes, either distribution assumption will do. We recommend using whatever distribution makes the calculations easiest, or is most politically acceptable to the organization! We could use other alternatives to capture the ‘fat tails’ in the actual data: such as the T-distribution; the distribution implied from the option volatility smile; or a mixture of two normal distributions (one regular, one fat). These alternatives are almost certainly not worth the additional effort required to implement them, compared to the simpler approach of using the results from the normal or log-normal distribution and scaling them (i.e. multiplying them by a fudge factor so the results fit the tails of the actual data better). Remember that we are really only concerned with accurately modeling the tails of the distribution at our chosen level of confidence, not the whole distribution.

Volatility and correlation estimation Having chosen a distribution, we have to estimate the parameters of the distribution. In modeling the uncertainty of market prices, we usually focus on estimating the standard deviations (or volatilities) of market prices, and the correlations of pairs of market prices. We assume the first moment of the distribution is zero (which is

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reasonable in most cases for a short time horizon), and we ignore higher moments than the second moment, the standard deviation. One consequence of using standard deviation as a measure of uncertainty is that we don’t take into account asymmetry in the distribution of market variables. Skewness in the price distribution may increase or decrease risk depending on the sign of the skewness and whether our exposure is long or short. Later, when we discuss risk aggregation methods, we’ll see that some of the methods do account for the higher moments of the price distribution.

Historical estimates One obvious starting point is to estimate the parameters of the distribution from historical market data, and then apply those parameters to a forward-looking analysis of VaR (i.e. we assume that past market behavior can tell us something about the future). Observation period and weighting First, we have to decide how much of the historical market data we wish to consider for our estimate of the parameters of the distribution. Let’s suppose we want to use 2 months’ of daily data. One way to achieve this is to plug 2 months’ data into a standard deviation calculation. Implicitly, what we are doing is giving equal weight to the recent data and the data from two months ago. Alternatively, we may choose to give recent observations more weight in the standard deviation calculation than data from the past. If recent observations include larger moves than most of the data, then the standard deviation estimate will be higher, and if recent observations include smaller moves than most of the data, the standard deviation estimate will be lower. These effects on our standard deviation estimate have some intuitive appeal. If we use 2 months of equally weighted data, the weighted-average maturity of the data is 1 month. To maintain the same average maturity while giving more weight to more recent data, we have to sample data from more than 2 months. We can demonstrate this with a concrete example using the exponential weighting scheme. In exponential weighting, the weight of each value in the data, working back from today, is equal to the previous value’s weight multiplied by a decay factor. So if the decay factor is 0.97, the weight for today’s value in the standard deviation calculation is 0.970 ó1, the weight of yesterday’s value in the standard deviation is 0.971 ó0.97, the weight of previous day’s value in the standard deviation is 0.972 ó0.9409, and so on. Figure 4.9 shows a graph of the cumulative weight of n days, values, working back from today, and Table 4.3 shows the value of n for cumulative weights of 25%, 50%, 75% and 99%. Twenty-two business days (about 1 calendar month) contribute 50% of the weight of the data, so the weighted-average maturity of the data is 1 month. In contrast to the equally weighted data, where we needed 2 months of data, now we need 151 business days or over 7 months of data to calculate our standard deviation, given that we are prepared to cut off data that would contribute less than 1% to the total weight. Exponential weighting schemes are very popular, but while they use more data for the standard deviation calculation than unweighted schemes, it’s important to note that they effectively sample much less data – a quarter of the weight is contributed by the first nine observations, and half the weight by 22 observations. Our decay factor of 0.97 is towards the high end of the range of values used, and lower decay factors sample even less data.

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Figure 4.9 Cumulative weight of n observations for a decay factor of 0.97.

Table 4.3 Cumulative weight for a given number of observations. Weight 25% 50% 75% 99%

Observations 9 22 45 151

GARCH estimates GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) models of standard deviation can be thought of as a more complicated version of the weighting schemes described above, where the weighting factor is determined by the data itself. In a GARCH model the standard deviation today depends on the standard deviation yesterday, and the size of the change in market prices yesterday. The model tells us how a large move in prices today affects the likelihood of there being another large move in prices tomorrow.

Estimates implied from market prices Readers familiar with option valuation will know that the formulae used to value options take the uncertainty in market prices as input. Conversely, given the price of an option, we can imply the uncertainty in market prices that the trader used to value the option. If we believe that option traders are better at predicting future uncertainty in prices than estimates from historical data, we can use parameters

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implied from the market prices of options in our VaR models. One drawback of this approach is that factors other than trader’s volatility estimates, such as market liquidity and the balance of market supply and demand, may be reflected in market prices for options.

Research on different estimation procedures Hendricks (1996) studies the performance of equally and exponentially weighted estimators of volatility for a number of different sample sizes in two different VaR methods. His results indicate that there is very little to choose between the different estimators. Boudoukh et al. (1997) study the efficiency of several different weighting schemes for volatility estimation. The ‘winner’ is non-parametric multivariate density estimation (MDE). MDE puts high weight on observations that occur under conditions similar to the current date. Naturally this requires an appropriate choice of state variables to describe the market conditions. The authors use yield curve level and slope when studying Treasury bill yield volatility. MDE does not seem to represent a huge forecasting improvement given the increased complexity of the estimation method but it is interesting that we can formalize the concept of using only representative data for parameter estimation. Naive use of a delta normal approach requires estimating and handling very large covariance matrices. Alexander and Leigh (1997) advocate a divide-and-conquer strategy to volatility and correlation estimation: break down the risk factors into a sets of highly correlated factors; then perform principal components analysis to create a set of orthogonal risk factors; then estimate variances of the orthogonal factors and covariances of the principal components. Alexander and Leigh also conclude from backtesting that there is little to choose between the regulatory-year equally weighted model and GARCH(1,1), while the RiskMetricsTM exponentially weighted estimator performs less well.

BIS quantitative requirements The BIS requires a minimum weighted average maturity of the historical data used to estimate volatility of 6 months, which corresponds to a historical observation period of at least 1 year for equally weighted data. The volatility data must be updated at least quarterly: and more often if market conditions warrant it. Much of the literature on volatility estimation recommends much shorter observation periods7 but these are probably more appropriate for volatility traders (i.e. option book runners) than risk managers. When we use a short observation period, a couple of weeks of quiet markets will significantly reduce our volatility estimate. It is hard to see that the market has really become a less risky place, just because it’s been quiet for a while. Another advantage of using a relatively long observation period, and revising volatilities infrequently, is that the units of risk don’t change from day to day – just the position. This makes it easier for traders and risk managers to understand why their VaR has changed.

Beta estimation Looking back at our equation for the return on a stock, we see that it is the equation of a straight line, where b represents the slope of the line we would plot through the

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scatter graph of stock returns plotted on the y-axis, against market returns plotted on the x-axis. Most of the literature on beta estimation comes from investment analysis, where regression analysis of stock returns on market returns is performed using estimation periods that are extremely long (decades) compared to our risk analysis horizon. While betas show a tendency to revert to towards 1, the impact of such reversion over the risk analysis horizon is probably negligible. One criticism of historical estimates of beta is that they do not respond quickly to changes in the operating environment – or capital structure – of the firm. One alternative is for the risk manager to estimate beta from regression of stock returns on accounting measures such as earnings variability, leverage, and dividend payout, or to monitor these accounting measures as an ‘early-warning system’ for changes, not yet reflected in historical prices, that may impact the stock’s beta in the future.

Yield curve estimation We discussed yield curve construction and interpolation in Chapter 3. Risk managers must have detailed knowledge of yield curve construction and interpolation to assess model risk in the portfolio mark to market, to understand the information in exposure reports and to be able to test the information’s integrity. However, for risk aggregation, the construction and interpolation methods are of secondary importance, as is the choice of whether risk exposures are reported for cash yields, zero coupon yields or forward yields. As we will emphasize later, VaR measurement is not very accurate, so we shouldn’t spend huge resources trying to make the VaR very precise. Suppose we collect risk exposures by perturbing cash instrument yields, but we have estimated market uncertainty analysing zero coupon yields. It’s acceptable for us to use the two somewhat inconsistent sets of information together in a VaR calculation – as long as we understand what we have done, and have estimated the error introduced by what we have done.

Risk-aggregation methods We have described how we measure our exposure to market uncertainty and how we estimate the uncertainty itself, and now we will describe the different ways we calculate the risks and add up the risks to get a VaR number.

Factor push VaR As its name implies, in this method we simply push each market price in the direction that produces the maximum adverse impact on the portfolio for that market price. The desired confidence level and horizon determine the amount the price is pushed.

Conﬁdence level and standard deviations Let’s assume we are using the 99% confidence level mandated by the BIS. We need to translate that confidence level into a number of standard deviations by which we will push our risk factor.

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Figure 4.10 Cumulative normal probability density.

Figure 4.10 shows the cumulative probability density for the normal distribution as a function of the number of standard deviations. The graph was plotted in Microsoft ExcelTM, using the cumulative probability density function, NORMSDIST (NumberOfStandardDeviations). Using this function, or reading from the graph, we can see that one standard deviation corresponds to a confidence level of 84.1%, and two standard deviations correspond to a confidence level of 97.7%. Working back the other way, we can use the ExcelTM NORMSINV(ConfidenceLevel) function to tell us how many standard deviations correspond to a particular confidence level. For example, a 95% confidence level corresponds to 1.64 standard deviations, and a 99% confidence level to 2.33 standard deviations.

Growth of uncertainty over time Now we know that for a 99% confidence interval, we have to push the risk factor 2.33 standard deviations. However, we also have to scale our standard deviation to the appropriate horizon period for the VaR measurement. Let’s assume we are using a 1-day horizon. For a normally distributed variable, uncertainty grows with the square root of time. Figure 4.11 shows the growth in uncertainty over time of an interest rate with a standard deviation of 100 bps per annum,8 for three different confidence levels (84.1% or one standard deviation, 95% or 1.64 standard deviations, 99% or 2.33 standard deviations). Table 4.4 shows a table of the same information. Standard deviations are usually quoted on an annual basis. We usually assume that all the changes in market prices occur on business days – the days when all the markets and exchanges are open and trading can occur. There are approximately 250 business days in the year.9 To convert an annual (250-day) standard deviation to a 1-day standard deviation we multiply by Y(1/250), which is approximately 1/16.

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Figure 4.11 Growth in uncertainty over time.

Table 4.4 Growth of uncertainty over time (annual standard deviation 1%) Horizon Tenor

Days

Conﬁdence 84.10% STDs 1.00

95.00% 1.64

99.00% 2.33

1D 2W 1M 3M 6M 9M 1Y

1 10 21 62.5 125 187.5 250

0.06% 0.20% 0.29% 0.50% 0.71% 0.87% 1.00%

0.10% 0.33% 0.48% 0.82% 1.16% 1.42% 1.64%

0.15% 0.47% 0.67% 1.16% 1.64% 2.01% 2.33%

So, if we wanted to convert an interest rate standard deviation of about 100 bps per annum to a daily basis, we would divide 100 by 16 and get about 6 bps per day. So, we can calculate our interest rate factor push as a standard deviation of 6 bps per day, multiplied by the number of standard deviations for a 99% confidence interval of 2.33, to get a factor push of around 14 bps. Once we know how to calculate the size of the push, we can push each market variable to its worst value, calculate the impact on the portfolio value, and add each of the individual results for each factor up to obtain our VaR: FactorPushVaRóSum of ABS(SingleFactorVaR) for all risk factors, where SingleFactorVaRóExposure*MaximumLikelyAdverseMove MaximumLikelyAdverseMoveóNumberOfStandardDeviations*StandardDeviation* YHorizon, and ABS(x) means the absolute value of x

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The drawback of this approach is that it does not take into account the correlations between different risk factors. Factor push will usually overestimate VaR10 because it does not take into account any diversification of risk. In the real world, risk factors are not all perfectly correlated, so they will not all move, at the same time, in the worst possible direction, by the same number of standard deviations.

FX example Suppose that we bought 1 million DEM and sold 73.6 million JPY for spot settlement. Suppose that the daily standard deviations of the absolute exchange rates are 0.00417 for USD–DEM and 0.0000729 for USD–JPY (note that both the standard deviations were calculated on the exchange rate expressed as USD per unit of foreign currency). Then the 95% confidence interval, 1-day single factor VaRs are: 1 000 000*1.64*0.00417*Y1ó6863 USD for the DEM position ñ73 594,191*1.64*0.0000729*Y1óñ8821 USD for the JPY position The total VaR using the Factor Push methodology is the sum of the absolute values of the single factor VaRs, or 6863 USD plus 8821 USD, which equals 15 683 USD. As we mentioned previously, this total VaR takes no account of the fact that USD–DEM and USD–JPY foreign exchange rates are correlated, and so a long position in one currency and a short position in the other currency will have much less risk than the sum of the two exposures.

Bond example Suppose on 22 May 1998 we have a liability equivalent in duration terms to $100 million of the current 10-year US Treasury notes. Rather than simply buying 10year notes to match our liability, we see value in the 2-year note and 30-year bond, and so we buy an amount of 2-year notes and 30-year bonds that costs the same amount to purchase as, and matches the exposure of, the 10-year notes. Table 4.5 shows the portfolio holdings and duration, while Table 4.6 shows the exposure to each yield curve factor, using the data from Table 4.2 for the annualized change in yield at each maturity, for each factor. Table 4.5 Example bond portfolio’s holdings and duration Holding (millions) Liability: 10-year note Assets: 2-year note 30-year bond

100 44 54.25

Coupon

Maturity

Price

Yield

Modiﬁed duration

v01

5 5/8

5/15/08

99-29

5.641

7.56

75 483

5 5/8 6 1/8

4/30/00 11/15/18

100-01 103-00

5.605 5.909

1.81 13.79

7 948 77 060

Total

85 008

To convert the net exposure numbers in each factor to 95% confidence level, 1-day VaRs, we multiply by 1.64 standard deviations to get a move corresponding to 95% confidence and divide by 16 to convert the annual yield curve changes to daily changes. Note that the net exposure to the first factor is essentially zero. This is

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Change in market value Factor 1 Shift

Factor 2 Tilt

Factor 3 Bend

Liability: 10-year note 100

104

33

ñ8

(7 850 242) (2 490 942)

603 865

Assets: 2-year note 30-year bond

125 89

ñ3 34

19 ñ24

(933 497) 23 844 (6 858 310) (2 620 028)

(151 012) 1 849 432

Total

(7 851 807) (2 596 184)

1 698 420

44 54.25

Net exposure Single factor VaR

1 565 162

105 242 10 916

(1 094 556) (113 530)

because we choose the amounts of the 2-year note and 30-year bond to make this so.11 Our total factor push VaR is simply the sum of the absolute values of the single factor VaRs: 162ò10 916ò113 530ó124 608 USD.

Covariance approach Now we can move on to VaR methods that do, either explicitly or implicitly, take into account the correlation between risk factors.

Delta-normal VaR The standard RiskMetricsTM methodology measures positions by reducing all transactions to cash flow maps. The volatility of the returns of these cash flows is assumed to be normal, i.e. the cash flows each follow a log-normal random walk. The change in the value of the cash flow is then approximated as the product of the cash flow and the return (i.e. using the first term of a Taylor series expansion of the change in value of a log-normal random variable, e x ). Cash flow mapping can be quite laborious and does not extend to other risks beyond price and interest rate sensitivities. The Delta-normal methodology is a slightly more general flavor of the standard RiskMetrics methodology, which considers risk factors rather than cash flow maps as a measure of exposure. The risk factors usually correspond to standard trading system sensitivity outputs (price risk, vega risk, yield curve risk), are assumed to follow a multivariate normal distribution and are all first derivatives. Therefore, the portfolio change in value is linear in the risk factors and the position in each factor and the math for VaR calculation looks identical to that for the RiskMetrics approach, even though the assumptions are rather different. Single risk factor delta-normal VaR Delta-normal VaR for a single risk factor is calculated the same way as for the factor push method.

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Multiple risk factor delta-normal VaR How do we add multiple single factor VaRs, taking correlation into account? For the two risk factor case: VaR Total óY(VaR 21 òVaR 22 ò2*o12*VaR 1*VaR 2), where o12 is the correlation between the first and second risk factors. For the three risk factor case: VaR Total óY(VaR 21 òVaR 22 òVaR 23 ò2*o12*VaR 1*VaR 2 ò2*o13*VaR 1*VaR 3 ò2*o32* VaR 3*VaR 2), For n risk factors: VaR Total óY(&i &j oij*VaR i*VaR j )12 As the number of risk factors increases, the long hand calculation gets cumbersome, so we switch to using matrix notation. In matrix form, the calculation is much more compact: VaR Total óY(V*C*V T ) where: V is the row matrix of n single-factor VaRs, one for each risk factor C is the n by n correlation matrix between each risk factor and T denotes the matrix Transpose operation ExcelTM has the MMULT() formula for matrix multiplication, and the TRANSPOSE() formula to transpose a matrix, so – given the input data – we can calculate VaR in a single cell, using the formula above.13 The essence of what we are doing when we use this formula is adding two or more quantities that have a magnitude and a direction, i.e. adding vectors. Suppose we have two exposures, one with a VaR of 300 USD, and one with a VaR of 400 USD. Figure 4.12 illustrates adding the two VaRs for three different correlation coefficients. A correlation coefficient of 1 (perfect correlation of the risk factors) implies that the VaR vectors point in the same direction, and that we can just perform simple addition to get the total VaR of 300ò400ó700 USD. A correlation coefficient of 0 (no correlation of the risk factors) implies that the VaR vectors point at right

Figure 4.12 VaR calculation as vector addition.

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angles to each other, and that we can use Pythagoras’s theorem to calculate the length of the hypotenuse, which corresponds to our total VaR. The total VaR is the square root of the sum of the squares of 300 and 400, which gives a total VaR of Y(3002 ò4002)ó500 USD. A correlation coefficient of ñ1 (perfect inverse correlation of the risk factors) implies that the VaR vectors point in the opposite direction, and we can just perform simple subtraction to get the total VaR of 300 – 400óñ100 USD. We can repeat the same exercise, of adding two VaRs for three different correlation coefficients, using the matrix math that we introduced on the previous page. Equations (4.1)– (4.3) show the results of working through the matrix math in each case. Perfectly correlated risk factors VaRó

ó

(300 400)

1 1 1 1

300 400

(300.1ò400.1 300.1ò400.1)

300 400

ó (700.300ò700.400)

(4.1)

ó (210 000ò280 000) ó (490 000) ó700 Completely uncorrelated risk factors VaRó

ó

(300 400)

1 0 0 1

300 400

(300.1ò400.0 300.0ò400.1)

ó (300.300ò400.400)

(4.2)

ó (90 000ò160 000) ó (250 000) ó500 Perfectly inversely correlated risk factors VaRó

(300 400)

1 ñ1 ñ1 1

300 400

300 400

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ó

(300.1ò400.ñ1 300.ñ1ò400.1)

141

300 400

ó (ñ100.300ò100.400)

(4.3)

ó (ñ30 000ò40 000) ó (10 000) ó100 Naturally in real examples the correlations will not be 1,0 or ñ1, but the calculations flow through the matrix mathematics in exactly the same way. Now we can recalculate the total VaR for our FX and bond examples, this time using the deltanormal approach to incorporate the impact of correlation. FX example The correlation between USD–DEM and USD–JPY for our sample data is 0.063. Equation (4.4) shows that the total VaR for the delta-normal approach is 7235 USD – more than the single-factor VaR for the DEM exposure but actually less than the single-factor VaR for the JPY exposure, and about half the factor push VaR.

VaRó

ó

(6863 ñ8821)

1 0.603 0.603 1

6863 ñ8821

(6863.1òñ8821.0.603 6863.0.603òñ8821.1)

ó (1543.6863òñ4682.ñ8921)

6863 ñ8821

(4.4)

ó (10 589 609ò41 768 122) ó 52 357 731) ó 7235 Bond example By construction, our yield curve factors are completely uncorrelated. Equation (4.5) shows that the total VaR for the delta-normal approach is 114 053 USD – more than any of the single-factor VaRs, and less than total factor push VaR.

VaRó

1 0 0

(162 10 916 ñ113 530) 0 1 0 0 0 1

162 10 916

ñ113 530

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ó

(162.1ò10 916.0òñ113 530.0 162.0ò10 916.1òñ113 530.0

162.0ò10 916.0òñ113 530.1)

162 10 916

ñ113 530

ó (162.162ò10 916.10 916òñ113 530.ñ113 530)

(4.5)

ó (26 244ò119 159 056ò12 889 060 900) ó (13 008 246 200) ó114 053 The bond example shows that there are indirect benefits from the infrastructure that we used to calculate our VaR. Using yield curve factors for our exposure analysis also helps identify the yield curve views implied by the hedge strategy, namely that we have a significant exposure to a bend in the yield curve. In general, a VaR system can be programmed to calculate the implied views of a portfolio, and the best hedge for a portfolio. Assuming that the exposure of a position can be captured entirely by first derivatives is inappropriate for portfolios containing significant quantities of options. The following sections describe various ways to improve on this assumption.

Delta-gamma VaR There are two different VaR methodologies that are called ‘delta-gamma VaR’. In both cases, the portfolio sensitivity is described by first and second derivatives with respect to risk factors. Tom Wilson (1996) works directly with normally distributed risk factors and a second-order Taylor series expansion of the portfolio’s change in value. He proposes three different solution techniques to calculate VaR, two of which require numerical searches. The third method is an analytic solution that is relatively straightforward, and which we will describe here. The gamma of a set of N risk factors can be represented by an NîN matrix, known as the Hessian. The matrix diagonal is composed of second derivatives – what most people understand by gamma. The offdiagonal or cross-terms describe the sensitivities of the portfolio to joint changes in a pair of risk factors. For example, a yield curve moves together with a change in volatility. Wilson transforms the risk factors to orthogonal risk factors. The transformed gamma matrix has no cross-terms – the impact of the transformed risk factors on the portfolio change in value is independent – so the sum of the worstcase change in value for each transformed risk factor will also be the worst-case risk for the portfolio. Wilson then calculates an adjusted delta that gives the same worstcase change in value for the market move corresponding to the confidence level as the original delta and the original gamma.

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Figure 4.13 Delta-gamma method.

Figure 4.13 shows the portfolio change in value for a single transformed risk factor as a black line. The delta of the portfolio is the grey line, the tangent to the black line at the origin of the figure, which can be projected out to the appropriate number of standard deviations to calculate a delta-normal VaR. The adjusted delta of the portfolio is the dotted line, which can be used to calculate the delta-gamma VaR. The adjusted delta is a straight line from the origin to the worst-case change in value for the appropriate number of standard deviations,14 where the straight line crosses the curve representing the actual portfolio change in value. Given this picture, we can infer that the delta-gamma VaR is correct only for a specified confidence interval and cannot be rescaled to a different confidence interval like a delta-normal VaR number. The adjusted delta will typically be different for a long and a short position in the same object. An ad-hoc version of this approach can be applied to untransformed risk factors – provided the cross-terms in the gamma matrix are small. To make things even simpler, we can require the systems generating delta information for risk measurement to do so by perturbing market rates by an amount close to the move implied by the confidence interval and then feed this number into our delta-normal VaR calculation. RiskMetrics15 takes a very different approach to extending the delta-normal framework. The risk factor delta and gamma are used to calculate the first four moments of the portfolio’s return distribution. A function of the normal distribution is chosen to match these moments. The percentile for the normal distribution can then be transformed to the percentile for the actual return distribution. If this sounds very complicated, think of the way we calculate the impact of a 99th percentile/2.33 standard deviation move in a log-normally distributed variable. We multiply the

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volatility by 2.33 to get the change in the normal variable, and then multiply the spot price by e change to get the up move and divide by e change to get the down move. This is essentially the same process. (Hull and White (1998) propose using the same approach for a slightly different problem.) Now let’s see how we can improve on the risk factor distribution and portfolio change in value assumptions we have used in delta-normal and delta-gamma VaR.

Historical simulation VaR So far we have assumed our risk factors are either normally or log-normally distributed. As we saw in our plots of foreign exchange rates, the distribution of real market data is not that close to either a normal or a log-normal distribution. In fact, some market data (electricity prices are a good example) has a distribution that is completely unlike either a normal or log-normal distribution. Suppose that instead of modeling the market data, we just ‘replayed the tape’ of past market moves? This process is called historical simulation. While the problems of modeling and estimating parameters for the risk factors are eliminated by historical simulation, we are obviously sensitive to whether the historic time series data we use is representative of the market, and captures the features we want – whether the features are fat tails, skewness, non-stationary volatilities or the presence of extreme events. We can break historical simulation down into three steps: generating a set of historical changes in our risk factors, calculating the change in portfolio value for each historical change, and calculating the VaR. Let’s assume that we are going to measure the changes in the risk factors over the same period as our VaR horizon – typically 1 day. While we said that we were eliminating modeling, we do have to decide whether to store the changes as absolute or percentage changes. If the overall level of a risk factor has changed significantly over the period sampled for the simulation, then we will have some sensitivity to the choice.16 Naturally, the absence of a historical time series for a risk factor you want to include in your analysis is a problem! For instance, volatility time series for OTC (over-the-counter) options are difficult to obtain (we usually have to go cap in hand to our option brokers) and entry into a new market for an instrument that has not been traded for long requires some method of ‘back-filling’ the missing data for the period prior to the start of trading. Next, we have to calculate the change in portfolio value. Starting from today’s market data, we apply one period’s historical changes to get new values for all our market data. We can then calculate the change in portfolio value by completely revaluing the whole portfolio, by using the sensitivities to each risk factor (delta, gamma, vega, . . .) in a Taylor series or by interpolating into precalculated sensitivity tables (a half-way house between full revaluation and risk factors). We then repeat the process applying the next period’s changes to today’s market data. Full revaluation addresses the problem of using only local measures of risk, but requires a huge calculation resource relative to using factor sensitivities.17 The space required for storing all the historical data may also be significant, but note that the time series takes up less data than the covariance matrix if the number of risk factors is more than twice the number of observations in the sample (Benson and Zangari, 1997). Finally, we have to calculate the VaR. One attractive feature of historical simulation

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is that we have the whole distribution of the portfolio’s change in value. We can either just look up the required percentile in the table of the simulation results, or we can model the distribution of the portfolio change and infer the change in value at the appropriate confidence interval from the distribution’s properties (Zangari, 1997; Butler and Schachter, 1996). Using the whole distribution uses information from all the observations to make inference about the tails, which may be an advantage for a small sample, and also allows us to project the behavior of the portfolio for changes that are larger than any changes that have been seen in our historic data.

FX example Suppose we repeat the VaR calculation for our DEM–JPY position, this time using the actual changes in FX rates and their impact on the portfolio value. For each day, and each currency, we multiply the change in FX rate by the cash flow in the currency. Then, we look up the nth smallest or largest change in value, using the ExcelTM SMALL() and LARGE() functions, where n is the total number of observations multiplied by (1-confidence interval). Table 4.7 FX example of historical simulation VaR versus analytic VaR Conﬁdence

Method

DEM

JPY

DEM–JPY

95%

AnalyticVaR SimulationVaR SimulationVaR/AnalyticVaR

6 863 6 740 98.2%

(8 821) (8 736) 99.0%

7 196 (7 162) 99.5%

99%

AnalyticVaR SimulationVaR SimulationVaR/AnalyticVaR

9 706 12 696 130.8%

(12 476) (16 077) 128.9%

10 178 (12 186) 119.7%

Table 4.7 shows the results of the analytic and historical simulation calculations. The simulation VaR is less than the analytic VaR for 95% confidence and below, and greater than the analytic VaR for 99% confidence and above. If we had shown results for long and short positions, they would not be equal.18

Monte Carlo simulation VaR Monte Carlo VaR replaces the first step of historical simulation VaR: generating a set of historic changes in our risk factors. Monte Carlo VaR uses a model, fed by a set of random variables, to generate complete paths for all risk factor changes from today to the VaR horizon date. Each simulation path provides all the market data required for revaluing the whole portfolio. For a barrier FX option, each simulation path would provide the final foreign exchange rate, the final foreign exchange rate volatility, and the path of exchange rates and interest rates. We could then determine whether the option had been ‘knocked-out’ at its barrier between today and the horizon date, and the value of the option at the horizon date if it survived. The portfolio values (one for each path) can then be used to infer the VaR as described for historical simulation. Creating a model for the joint evolution of all the risk factors that affect a bank’s portfolio is a massive undertaking that is almost certainly a hopeless task for any

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institution that does not already have similar technology tried and tested in the front office for portfolio valuation and risk analysis.19 This approach is also at least as computationally intensive as historical simulation with full revaluation, if not more so. Both Monte Carlo simulation and historical simulation suffer from the fact that the VaR requires a large number of simulations or paths before the value converges towards a single number. The potential errors in VaR due to convergence decrease with the square of the number of Monte Carlo paths – so we have to run four times as many paths to cut the size of the error by a factor of 2. While, in principle, Monte Carlo simulation can address both the simplifying assumptions we had to make for other methods in modeling the market and in representing the portfolio, it is naive to expect that most implementations will actually achieve these goals. Monte Carlo is used much more frequently as a research tool than as part of the production platform in financial applications, except possibly for mortgage-backed securities (MBS).

Current practice We have already discussed the assumptions behind VaR. As with any model, we must understand the sensitivity of our VaR model to the quality of its inputs. In a perfect world we would also have implemented more than one model and have reconciled the difference between the models’ results. In practice, this usually only happens as we refine our current model and try to understand the impact of each round of changes from old to new. Beder (1995) shows a range of VaR calculations of 14 times for the same portfolio using a range of models – although the example is a little artificial as it includes calculations based on two different time horizons. In a more recent regulatory survey of Australian banks, Gizycki and Hereford (1998) report an even larger range (more than 21 times) of VaR values, though they note that ‘crude, but conservative’ assumptions cause outliers at the high end of the range. Gizycki and Hereford also report the frequency with which the various approaches are being used: 12 Delta-Normal Variance–Covariance, 5 Historical Simulation, 3 Monte Carlo, 1 Delta-Normal Variance–Covariance and Historical Simulation. The current best practice in the industry is historical simulation, using factor sensitivities, while participants are moving towards historical simulation, using full revaluation, or Monte Carlo. Note that most implementations study the terminal probabilities of events, not barrier probabilities. Consider the possibility of the loss event happening at any time over the next 24 hours rather than the probability of the event happening when observed at a single time, after 24 hours have passed. Naturally, the probability of exceeding a certain loss level at any time over the next 24 hours is higher than the probability of exceeding a certain loss level at the end of 24 hours. This problem in handling time is similar to the problem of using a small number of terms in the Taylor series expansion of a portfolio’s P/L function. Both have the effect of masking large potential losses inside the measurement boundaries. The BIS regulatory multiplier (Stahl, 1997; Hendricks and Hirtle, 1998) takes the VaR number we first calculated and multiplies it by at least three – and more if the regulator deems necessary – to arrive at the required regulatory capital. Even though this goes a long way to addressing the modeling uncertainties in VaR, we would still not recommend VaR as a measure of downside on its own. Best practice requires

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that we establish market risk reserves (Group of Thirty, 1993) and model risk reserves (Beder, 1995). Model risk reserves should include coverage for potential losses that relate to risk factors that are not captured by the modeling process and/or the VaR process. Whether such reserves should be included in VaR is open to debate.20

Robust VaR Just how robust is VaR? In most financial applications we choose fairly simple models and then abuse the input data outside the model to fit the market. We also build a set of rules about when the model output is likely to be invalid. VaR is no different. As an example, consider the Black–Scholes–Merton (BSM) option pricing model: one way we abuse the model is by varying the volatility according to the strike. We then add a rule not to sell very low delta options at the model value because even with a steep volatility smile we just can’t get the model to charge enough to make it worth our while to sell these options. A second BSM analogy is the modeling of stochastic volatility by averaging two BSM values, one calculated using market volatility plus a perturbation, and one using market volatility minus a perturbation, rather than building a more complicated model which allows volatility to change from its initial value over time. Given the uncertainties in the input parameters (with respect to position, liquidation strategy, time horizon and market model) and the potential mis-specification of the model itself, we can estimate the uncertainty in the VaR. This can either be done formally, to be quoted on our risk reports whenever the VaR value is quoted, or informally, to determine when we should flag the VaR value because it is extremely sensitive to the input parameters or to the model itself. Here is a simple analysis of errors for single risk factor VaR. Single Risk factor VaR is given by Exposure*NumberOfStandardDeviations*StandardDeviation*YHorizon. If the exposure is off by 15% and the standard deviation is off by 10% then relative error of VaR is 15ò10ó25%! Note that this error estimate excludes the problems of the model itself. The size of the error estimate does not indicate that VaR is meaningless – just that we should exercise some caution in interpreting the values that our models produce.

Speciﬁc risk The concept of specific risk is fairly simple. For any instrument or portfolio of instruments for which we have modeled the general market risk, we can determine a residual risk that is the difference between the actual change in value and that explained by our model of general market risk. Incorporating specific risk in VaR is a current industry focus, but in practice, most participants use the BIS regulatory framework to calculate specific risk, and that is what we describe below.

Interest rate speciﬁc risk model The BIS specific risk charge is intended to ‘protect against adverse movement in the price of an individual security owing to factors relating to the individual issuer’. The charge is applied to the gross positions in trading book instruments – banks can

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only offset matched positions in the identical issue – weighted by the factors in Table 4.8. Table 4.8 BIS speciﬁc risk charges Issuer category Government Qualifying issuers: e.g. public sector entities, multilateral development banks and OECD banks

Other

Weighting factor 0% 3.125%

12.5% 20% 100%

Capital charge 0% 0.25% residual term to ﬁnal maturity \6M

1.0% residual term to ﬁnal maturity 6–24M 1.6% residual term to ﬁnal maturity 24Mò 8%

Interest rate and currency swaps, FRAs, forward foreign exchange contracts and interest rate futures are not subject to a specific risk charge. Futures contracts where the underlying is a debt security, are subject to charge according to the credit risk of the issuer.

Equity-speciﬁc risk model The BIS specific risk charge for equities is 8% of gross equity positions, unless the portfolio is ‘liquid and well-diversified’ according to the criteria of the national authorities, in which case the charge is 4%. The charge for equity index futures, forwards and options is 2%.

Concentration risk Diversification is one of the cornerstones of risk management. Just as professional gamblers limit their stakes on any one hand to a small fraction of their net worth, so they will not be ruined by a run of bad luck, so professional risk-taking enterprises must limit the concentration of their exposures to prevent any one event having a significant impact on their capital base. Concentrations may arise in a particular market, industry, region, tenor or trading strategy. Unfortunately, there is a natural tension between pursuit of an institution’s core competencies and comparative advantages into profitable market segments or niches, which produces concentration, and the desire for diversification of revenues and exposures.

Conclusion We can easily criticize the flaws in the VaR models implemented at our institutions, but the simplicity of the assumptions behind our VaR implementations is actually an asset that facilitates education of both senior and junior personnel in the organization, and helps us retain intuition about the models and their outputs. In fact, VaR models perform surprisingly well, given their simplicity. Creating the modeling, data, systems and intellectual infrastructure for firm-wide quantitative

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risk management is a huge undertaking. Successful implementation of even a simple VaR model is a considerable achievement and an ongoing challenge in the face of continually changing markets and products.

Acknowledgements Thanks to Lev Borodovsky, Randi Hawkins, Yong Li, Marc Lore, Christophe Rouvinez, Rob Samuel and Paul Vogt for encouragement, helpful suggestions and/or reviewing earlier drafts. The remaining mistakes are mine. Please email any questions or comments to [email protected] This chapter represents my personal opinions, and is supplied without any warranty of any kind.

Notes 1

Rather than saying ‘lose’, we should really say ‘see a change in value below the expected change of ’. 2 Here, and throughout this chapter, when we use price in this general sense, we take price to mean a price, or an interest rate or an index. 3 The classic reference on this topic is Elton and Gruber (1995). 4 Option traders like using Greek letters for exposure measures. Vega isn’t a Greek letter, but is does begin with a ‘V’, which is easy to remember for a measure of volatility exposure. Classically trained option traders use Kappa instead. 5 For instance, when the portfolio contains significant positions in options that are about to expire, or significant positions in exotic options. 6 Stricly speaking, changes in the logarithm of the price are normally distributed. 7 The Riskmetrics Group recommends a estimator based on daily observations with a decay factor of 0.94, and also provides a regulatory data set with monthly observations and a decay factor of 0.97 to meet the BIS requirements. Their data is updated daily. 8 To keep the examples simple, we have used absolute standard deviations for market variables throughout the examples. A percentage volatility can be converted to an absolute standard deviation by multiplying the volatility by the level of the market variable. For example, if interest rates are 5%, or 500 bps, and volatility is 20% per year, then the absolute standard deviation of interest rates is 500*0.2ó100 bps per year. 9 Take 365 calendar days, multiply by 5/7 to eliminate the weekends, and subtract 10 or so public holidays to get about 250 business days. 10 However, in some cases, factor push can underestimate VaR. 11 Note also that, because our factor model tells us that notes and bonds of different maturities experience yield curve changes of different amounts, the 2-year note and 30-year bond assets do not have the same duration and dollar sensitivity to an 01 bp shift (v01) as the 10year liability. Duration and v01 measure sensitivity to a parallel shift in the yield curve. 12 As a reference for the standard deviation of functions of random variables see Hogg and Craig (1978). 13 This is not intended to be an (unpaid!) advertisement for ExcelTM. ExcelTM simply happens to be the spreadsheet used by the authors, and we thought it would be helpful to provide some specific guidance on how to implement these calculations. 14 The worst-case change in value may occur for an up or a down move in the risk factor – in this case it’s for a down move. 15 RiskMetrics Technical Document, 4th edition, pp. 130–133 at http://www.riskmetrics.com/ rm/pubs/techdoc.html

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16

Consider the extreme case of a stock that has declined in price from $200 per share to $20 per share. If we chose to store absolute price changes, then we might have to try to apply a large decline from early in the time series, say $30, that is larger than today’s starting price for the stock! 17 In late 1998, most businesses could quite happily run programs to calculate their deltanormal VaR, delta-gamma VaR or historical simulation VaR using risk factor exposures on a single personal computer in under an hour. Historical simulation VaR using full revaluation would require many processors (more than 10, less than 100) working together (whether over a network or in a single multi-processor computer), to calculate historical simulation VaR using full revaluation within one night. In dollar terms, the PC hardware costs on the order of 5000 USD, while the multi-processor hardware costs on the order of 500 000 USD. 18 Assuming the antithetic variable variance reduction technique was not used. 19 The aphorism that springs to mind is ‘beautiful, but useless’. 20 Remember that VaR measures uncertainty in the portfolio P/L, and reserves are there to cover potential losses. Certain changes in the P/L or actual losses, even if not captured by the models used for revaluation, should be included in the mark to market of the portfolio as adjustments to P/L.

References Acar, E. and Prieve, D. (1927) ‘Expected minimum loss of financial returns’, Derivatives Week, 22 September. Ait-Sahalia, Y. (1996) ‘Testing continuous time models of the spot rate’, Review of Financial Studies, 2, No. 9, 385–426. Ait-Sahalia, Y. (1997) ‘Do interest rates really follow continuous-time Markov diffusions?’ Working Paper, Graduate School of Business, University of Chicago. Alexander, C. (1997) ‘Splicing methods for VaR’, Derivatives Week, June. Alexander, C. and Leigh, J. (1997) ‘On the covariance matrices used in VaR models’, Journal of Derivatives, Spring, 50–62. Artzner, P. et al. (1997) ‘Thinking coherently’, Risk, 10, No. 11. Basel Committee on Banking Supervision (1996) Amendment to the Capital Accord to incorporate market risks, January. http://www.bis.org/publ/bcbs24.pdf Beder, T. S. (1995) ‘VaR: seductive but dangerous’, Financial Analysts Journal, 51, No. 5, 12–24, September/October or at http://www.cmra.com/ (registration required). Beder, T. S. (1995) Derivatives: The Realities of Marking to Model, Capital Market Risk Advisors at http://www.cmra.com/ Benson, P. and Zangari, P. (1997) ‘A general approach to calculating VaR without volatilities and correlations’, RiskMetrics Monitor, Second Quarter. Boudoukh, J., Richardson, M. and Whitelaw, R. (1997) ‘Investigation of a class of volatility estimators’, Journal of Derivatives, Spring. Butler, J. and Schachter, B. (1996) ‘Improving Value-at-Risk estimates by combining kernel estimation with historic simulation’, OCC Report, May. Elton, E. J. and Gruber, M. J. (1995) Modern Portfolio Theory and Investment Analysis, Wiley, New York. Embrechs, P. et al. (1998) ‘Living on the edge’, Risk, January. Gizycki, M. and Hereford, N. (1998) ‘Differences of opinion’, Asia Risk, 42–7, August. Group of Thirty (1993) Derivatives: Practices and Principles, Recommendations 2 and 3, Global Derivatives Study Group, Washington, DC.

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Hendricks, D. (1996) ‘Evaluation of VaR models using historical data’, Federal Reserve Bank of New York Economic Policy Review, April, or http://www.ny.frb.org/ rmaghome/econ_pol/496end.pdf Hendricks, D. and Hirtle, B. (1998) ‘Market risk capital’, Derivatives Week, 6 April. Hogg, R. and Craig, A. (1978) Introduction to Mathematical Statistics, Macmillan, New York. Hull, J. and White, A. (1998) ‘Value-at-Risk when daily changes in market variables are not normally distributed’, Journal of Derivatives, Spring. McNeil, A. (1998) ‘History repeating’, Risk, January. Stahl, G. (1997) ‘Three cheers’, Risk, May. Wilmot, P. et al. (1995) ‘Spot-on modelling’, Risk, November. Wilson, T. (1996) ‘Calculating risk capital’, in Alexander, C. (ed.), The Handbok of Risk Management, Wiley, New York. Zangari, P. (1997) ‘Streamlining the market risk measurement process’, RiskMetrics Monitor, First Quarter.

Part 2

Market risk, credit risk and operational risk

5

Yield curve risk factors: domestic and global contexts WESLEY PHOA

Introduction: handling multiple risk factors Methodological introduction Traditional interest rate risk management focuses on duration and duration management. In other words, it assumes that only parallel yield curve shifts are important. In practice, of course, non-parallel shifts in the yield curve often occur, and represent a significant source of risk. What is the most efficient way to manage non-parallel interest rate risk? This chapter is mainly devoted to an exposition of principal component analysis, a statistical technique that attempts to provide a foundation for measuring non-parallel yield curve risk, by identifying the ‘most important’ kinds of yield curve shift that occur empirically. The analysis turns out to be remarkably successful. It gives a clear justification for the use of duration as the primary measure of interest rate risk, and it also suggests how one may design ‘optimal’ measures of non-parallel risk. Principal component analysis is a popular tool, not only in theoretical studies but also in practical risk management applications. We discuss such applications at the end of the chapter. However, it is first important to understand that principal component analysis has limitations, and should not be applied blindly. In particular, it is important to distinguish between results that are economically meaningful and those that are statistical artefacts without economic significance. There are two ways to determine whether the results of a statistical analysis are meaningful. The first is to see whether they are consistent with theoretical results; the Appendix gives a sketch of this approach. The second is simply to carry out as much exploratory data analysis as possible, with different data sets and different historical time periods, to screen out those findings which are really robust. This chapter contains many examples. In presenting the results, our exposition will rely mainly on graphs rather than tables and statistics. This is not because rigorous statistical criteria are unnecessary – in fact, they are very important. However, in the exploratory phase of any empirical study it is critical to get a good feel for the results first, since statistics can easily

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mislead. The initial goal is to gain insight; and visual presentation of the results can convey the important findings most clearly, in a non-technical form. It is strongly suggested that, after finishing this chapter, readers should experiment with the data themselves. Extensive hands-on experience is the only way to avoid the pitfalls inherent in any empirical analysis.

Non-parallel risk, duration bucketing and partial durations Before discussing principal component analysis, we briefly review some more primitive approaches to measuring non-parallel risk. These have by no means been superseded: later in the chapter we will discuss precisely what role they continue to play in risk management. The easiest approach is to group securities into maturity buckets. This is a very simple way of estimating exposure to movements at the short, medium and long ends of the yield curve. But it is not very accurate: for example, it ignores the fact that a bond with a higher coupon intuitively has more exposure to movements in the short end of the curve than a lower coupon bond with the same maturity. Next, one could group securities into duration buckets. This approach is somewhat more accurate because, for example, it distinguishes properly between bonds with different coupons. But it is still not entirely accurate because it does not recognize that the different individual cash flows of a single security are affected in different ways by a non-parallel yield curve shift. Next, one could group security cash flows into duration buckets. That is, one uses a finer-grained unit of analysis: the cash flow, rather than the security. This makes the results much more precise. However, bucketed duration exposures have no direct interpretation in terms of changes in some reference set of yields (i.e. a shift in some reference yield curve), and can thus be tricky to interpret. More seriously, as individual cash flows shorten they will move across bucket duration boundaries, causing discontinuous changes in bucket exposures which can make risk management awkward. Alternatively, one could measure partial durations. That is, one directly measures how the value of a portfolio changes when a single reference yield is shifted, leaving the other reference yields unchanged; note that doing this at the security level and at the cashflow level gives the same results. There are many different ways to define partial durations: one can use different varieties of reference yield (e.g. par, zero coupon, forward rate), one can choose different sets of reference maturities, one can specify the size of the perturbation, and one can adopt different methods of interpolating the perturbed yield curve between the reference maturities. The most popular partial durations are the key rate durations defined in Ho (1992). Fixing a set of reference maturities, these are defined as follows: for a given reference maturity T, the T-year key rate duration of a portfolio is the percentage change in its value when one shifts the T-year zero coupon yield by 100 bp, leaving the other reference zero coupon yields fixed, and linearly interpolating the perturbed zero coupon curve between adjacent reference maturities (often referred to as a ‘tent’ shift). Figure 5.1 shows some examples of key rate durations. All the above approaches must be used with caution when dealing with optionembedded securities such as callable bonds or mortgage pools, whose cashflow timing will vary with the level of interest rates. Option-embedded bonds are discussed in detail elsewhere in this book.

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Figure 5.1 Key rate durations of non-callable Treasury bonds.

Limitations of key rate duration analysis Key rate durations are a popular and powerful tool for managing non-parallel risk, so it is important to understand their shortcomings. First, key rate durations can be unintuitive. This is partly because ‘tent’ shifts do not occur in isolation, and in fact have no economic meaning in themselves. Thus, using key rate durations requires some experience and familiarization. Second, correlations between shifts at different reference maturities are ignored. That is, the analysis treats shifts at different points in the yield curve as independent, whereas different yield curve points tend to move in correlated ways. It is clearly important to take these correlations into account when measuring risk, but the key rate duration methodology does not suggest a way to do so. Third, the key rate duration computation is based on perturbing a theoretical zero coupon curve rather than observed yields on coupon bonds, and is therefore sensitive to the precise method used to strip (e.g.) a par yield curve. This introduces some arbitrariness into the results, and more significantly makes them hard to interpret in terms of observed yield curve shifts. Thus swap dealers (for example) often look at partial durations computed by directly perturbing the swap curve (a par curve) rather than perturbing a zero coupon curve. Fourth, key rate durations for mortgage-backed securities must be interpreted with special care. Key rate durations closely associated with specific reference maturities which drive the prepayment model can appear anomalous; for example, if the mortgage refinancing rate is estimated using a projected 10-year Treasury yield, 10-year key rate durations on MBS will frequently be negative. This is correct according to the definition, but in this situation one must be careful in constructing MBS hedging strategies using key rate durations. Fifth, key rate durations are unwieldy. There are too many separate interest rate

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risk measures. This leads to practical difficulties in monitoring risk, and inefficiencies in hedging risk. One would rather focus mainly on what is ‘most important’. To summarize: while key rate durations are a powerful risk management tool, it is worth looking for a more sophisticated approach to analyzing non-parallel risk that will yield deeper insights, and that will provide a basis for more efficient risk management methodologies.

Principal component analysis Deﬁnition and examples from US Treasury market As often occurs in finance, an analogy with physical systems suggests an approach. Observed shifts in the yield curve may seem complex and somewhat chaotic. In principle, it might seem that any point on the yield curve can move independently in a random fashion. However, it turns out that most of the observed fluctuation in yields can be explained by more systematic yield shifts: that is, bond yields moving ‘together’, in a correlated fashion, but perhaps in several different ways. Thus, one should not focus on fluctuations at individual points on the yield curve, but on shifts that apply to the yield curve as a whole. It is possible to identify these systematic shifts by an appropriate statistical analysis; as often occurs in finance, one can apply techniques inspired by the study of physical systems. The following concrete example, taken from Jennings and McKeown (1992), may be helpful. Consider a plank with one end fixed to a wall. Whenever the plank is knocked, it will vibrate. Furthermore, when it vibrates it does not deform in a completely random way, but has only a few ‘vibration modes’ corresponding to its natural frequencies. These vibration modes have different degrees of importance, with one mode – a simple back-and-forth motion – dominating the others: see Figure 5.2. One can derive these vibration modes mathematically, if one knows the precise physical characteristics of the plank. But one should also be able to determine them empirically by observing the plank. To do this, one attaches motion sensors at different points on the plank, to track the motion of these points through time. One will find that the observed disturbances at each point are correlated. It is possible to extract the vibration modes, and their relative importance, from the correlation matrix. In fact, the vibration modes correspond to the eigenvalues of the matrix: in other words, the eigenvectors, plotted in graphical form, will turn out to look exactly as in Figure 5.2. The relative importance of each vibration mode is measured by the size of the corresponding eigenvectors. Let us recall the definitions. Let A be a matrix. We say that v is an eigenvector of A, with corresponding eigenvalue j, if A . vójv. The eigenvalues of a matrix must be mutually orthogonal, i.e. ‘independent’. Note that eigenvectors are only defined up to a scalar multiple, but that eigenvalues are uniquely defined. Suppose A is a correlation matrix, e.g. derived from some time series of data; then it must be symmetric and also positive definite (i.e. v . A . v[0 for all vectors v). One can show that all the eigenvalues of such a matrix must be real and positive. In this case it makes sense to compare their relative sizes, and to regard them as ‘weights’ which measure the importance of the corresponding eigenvectors. For a physical system such as the cantilever, the interpretation is as follows. The

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Figure 5.2 Vibration modes of the cantilever.

eigenvectors describe the independent vibration modes: each eigenvector has one component for each sensor, and the component is a (positive or negative) real number which describes the relative displacement of that sensor under the given vibration mode. The corresponding eigenvalue measures how much of the observed motion of the plank can be attributed to that specific vibration mode. This suggests that we can analyze yield curve shifts analogously, as follows. Fix a set of reference maturities for which reasonably long time series of, say, daily yields are available: each reference maturity on the yield curve is the analog of a motion sensor on the plank. Construct the time series of daily changes in yield at each reference maturity, and compute the correlation matrix. Next, compute the eigenvectors and eigenvalues of this matrix. The eigenvectors can then be interpreted as independent ‘fundamental yield curve shifts’, analogous to vibration modes; in other words, the actual change in the yield curve on any particular day may be regarded as a combination of different, independent, fundamental yield curve shifts. The relative sizes of the eigenvalues tells us which fundamental yield curve shifts tend to dominate.

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Daily changes 1 0 ñ2 5 0

1 1 ñ2 4 0

1 2 ñ2 3 1

1 3 ñ2 2 0

Correlation matrix 1.00 0.97 0.83 0.59

0.97 1.00 0.92 0.77

0.83 0.92 1.00 0.90

0.59 0.77 0.90 1.00

Eigenvalues and eigenvectors [A] [B] [C] [D]

0.000 0.037 0.462 3.501

(0%) (1%) (11%) (88%)

0.607 ñ0.155 ñ0.610 0.486

ñ0.762 ñ0.263 ñ0.274 0.524

0.000 0.827 0.207 0.522

0.225 ñ0.471 0.715 0.465

For a toy example, see Table 5.1. The imaginary data set consists of five days of observed daily yield changes at four unnamed reference maturities; for example, on days 1 and 3 a perfectly parallel shift occurred. The correlation matrix shows that yield shifts at different maturity points are quite correlated. Inspecting the eigenvalues and eigenvectors shows that, at least according to principal component analysis, there is a dominant yield curve shift, eigenvector (D), which represents an almost parallel shift: each maturity point moves by about 0.5. The second most important eigenvector (C) seems to represent a slope shift or ‘yield curve tilt’. The third eigenvector (B) seems to appear because of the inclusion of day 5 in the data set. Note that the results might not perfectly reflect one’s intuition. First, the dominant shift (D) is not perfectly parallel, even though two perfectly parallel shifts were included in the data set. Second, the shift that occurred on day 2 is regarded as a combination of a parallel shift (D) and a slope shift (C), not a slope shift alone; shift (C) has almost the same shape as the observed shift on day 2, but it has been ‘translated’ so that shifts of type (C) are uncorrelated with shifts of type (D). Third, eigenvector (A) seems to have no interpretation. Finally, the weight attached to (D) seems very high – this is because the actual shifts on all five days are regarded as having a parallel component, as we just noted. A technical point: in theory, one could use the covariance matrix rather than the correlation matrix in the analysis. However using the correlation matrix is preferable when observed correlations are more stable than observed covariances – which is usually the case in financial data where volatilities are quite unstable. (For further discussion, see Buhler and Zimmermann, 1996.) In the example of Table 5.1, very similar results are obtained using the covariance matrix. Table 5.2 shows the result of a principal component analysis carried out on actual US Treasury bond yield data from 1993 to 1998. In this case the dominant shift is a virtually parallel shift, which explains over 90% of observed fluctuations in bond yields. The second most important shift is a slope shift or tilt in which short yields fall and long yields rise (or vice versa). The third shift is a kind of curvature shift, in

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Table 5.2 Principal component analysis of US Treasury yields, 1993–8

0.3% 0.3% 0.2% 0.4% 0.6% 1.1% 5.5% 91.7%

1 year

2 year

3 year

5 year

7 year

10 year

20 year

30 year

0.00 0.00 0.01 ñ0.05 0.21 0.70 ñ0.59 0.33

0.05 ñ0.08 ñ0.05 ñ0.37 ñ0.71 ñ0.30 ñ0.37 0.35

ñ0.20 0.49 ñ0.10 0.65 0.03 ñ0.32 ñ0.23 0.36

0.31 ñ0.69 0.25 0.27 0.28 ñ0.30 ñ0.06 0.36

ñ0.63 0.06 0.30 ñ0.45 0.35 ñ0.19 0.14 0.36

0.50 0.27 ñ0.52 ñ0.34 0.34 ñ0.12 0.20 0.36

0.32 0.30 0.59 0.08 ñ0.27 0.28 0.44 0.35

ñ0.35 ñ0.34 ñ0.48 0.22 ñ0.26 0.32 0.45 0.35

Historical bond yield data provided by the Federal Reserve Board.

which short and long yields rise while mid-range yields fall (or vice versa); the remaining eigenvectors have no meaningful interpretation and are statistically insignificant. Note that meaningful results will only be obtained if a consistent set of yields is used: in this case, constant maturity Treasury yields regarded as a proxy for a Treasury par yield curve. Yields on physical bonds should not be used, since the population of bonds both ages and changes composition over time. The analysis here has been carried out using CMT yields reported by the US Federal Reserve Bank. An alternative is to use a dataset consisting of historical swap rates, which are par yields by definition. The results of the analysis turn out to be very similar.

Meaningfulness of factors: dependence on dataset It is extremely tempting to conclude that (a) the analysis has determined that there are exactly three important kinds of yield curve shift, (b) that it has identified them precisely, and (c) that it has precisely quantified their relative importance. But we should not draw these conclusions without looking more carefully at the data. This means exploring datasets drawn from different historical time periods, from different sets of maturities, and from different countries. Risk management should only rely on those results which turn out to be robust. Figure 5.3 shows a positive finding. Analyzing other 5-year historical periods, going back to 1963, we see that the overall results are quite consistent. In each case the major yield curve shifts turn out to be parallel, slope and curvature shifts; and estimates of the relative importance of each kind of shift are reasonably stable over time, although parallel shifts appear to have become more dominant since the late 1970s. Figures 5.4(a) and (b) show that some of the results remain consistent when examined in more detail: the estimated form of both the parallel shift and the slope shift are very similar in different historical periods. Note that in illustrating each kind of yield curve shift, we have carried out some normalization to make comparisons easier: for example, estimated slope shifts are normalized so that the 10-year yield moves 100 bp relative to the 1-year yield, which remains fixed. See below for further discussion of this point. However, Figure 5.4(c) does tentatively indicate that the form of the curvature shift has varied over time – a first piece of evidence that results on the curvature shift may be less robust than those on the parallel and slope shifts.

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Figure 5.3 Relative importance of principal components, 1963–98.

Figure 5.5 shows the effect of including 3- and 6-month Treasury bill yields in the 1993–8 dataset. The major yield curve shifts are still identified as parallel, slope and curvature shifts. However, an analysis based on the dataset including T-bills attaches somewhat less importance to parallel shifts, and somewhat more importance to slope and curvature shifts. Thus, while the estimates of relative importance remain qualitatively significant, they should not be regarded as quantitatively precise. Figures 5.6(a) and (b) show that the inclusion of T-bill yields in the dataset makes almost no difference to the estimated form of both the parallel and slope shifts. However, Figure 5.6(c) shows that the form of the curvature shift is totally different. Omitting T-bills, the change in curvature occurs at the 3–5-year part of the curve; including T-bills, it occurs at the 1-year part of the curve. There seem to be some additional dynamics associated with yields on short term instruments, which become clear once parallel and slope shifts are factored out; this matter is discussed further in Phoa (1998a,b). The overall conclusions are that parallel and slope shifts are unambiguously the most important kinds of yield curve shift that occur, with parallel shifts being dominant; that the forms of these parallel and slope shifts can be estimated fairly precisely and quite robustly; but that the existence and form of a third, ‘curvature’ shift are more problematic, with the results being very dependent on the dataset used in the analysis. Since the very form of a curvature shift is uncertain, and specifying it precisely requires making a subjective judgment about which dataset is ‘most relevant’, the curvature shift is of more limited use in risk management. The low weight attached to the curvature factor also suggests that it may be less important than other (conjectural) phenomena which might somehow have been missed by the analysis. The possibility that the analysis has failed to detect some important yield curve risk factors, which potentially outweigh curvature risk, is discussed further below. International bond yield data are analyzed in the next section. The results are

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Figure 5.4 1963–98.

163

Shapes of (a) ‘parallel’ shift, 1963–98, (b) ‘slope’ shift, 1963–98, (c) ‘curvature’ shift,

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Figure 5.5 Relative importance of principal components, with/without T-bills.

broadly consistent, but also provide further grounds for caution. The Appendix provides some theoretical corroboration for the positive findings. We have glossed over one slightly awkward point. The fundamental yield curve shifts estimated by a principal component analysis – in particular, the first two principal components representing parallel and slope shifts – are, by definition, uncorrelated. But normalizing a ‘slope shift’ so that the 1-year yield remains fixed introduces a possible correlation. This kind of normalization is convenient both for data analysis, as above, and for practical applications; but it does mean that one then has to estimate the correlation between parallel shifts and normalized slope shifts. This is not difficult in principle, but, as shown in Phoa (1998a,b), this correlation is time-varying and indeed exhibits secular drift. This corresponds to the fact that, while the estimated (non-normalized) slope shifts for different historical periods have almost identical shapes, they have different ‘pivot points’. The issue of correlation risk is discussed further below.

Correlation structure and other limitations of the approach It is now tempting to concentrate entirely on parallel and slope shifts. This approach forms the basis of most useful two factor interest rate models: see Brown and Schaefer (1995). However, it is important to understand what is being lost when one focuses only on two kinds of yield curve shift. First, there is the question of whether empirical correlations are respected. Figure 5.7(a) shows, graphically, the empirical correlations between daily Treasury yield shifts at different maturity points. It indicates that, as one moves to adjacent maturities, the correlations fall away rather sharply. In other words, even adjacent yields quite often shift in uncorrelated ways. Figure 5.7(b) shows the correlations which would have been observed if only parallel and slope shifts had taken place. These slope away much more gently as one moves to adjacent maturities: uncorrelated shifts in adjacent yields do not occur. This observation is due to Rebonato and Cooper (1996), who prove that the correlation structure implied by a two-factor model must always take this form.

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Figure 5.6 (a) Estimated ‘parallel’ shift, with/without T-bills, (b) estimated ‘slope’ shift, with/without T-bills, (c) estimated ‘curvature’ shift, with/without T-bills.

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Figure 5.7 (a) Empirical Treasury yield correlations, (b) theoretical Treasury yield correlations, twofactor model.

What this shows is that, even though the weights attached to the ‘other’ eigenvectors seemed very small, discarding these other eigenvectors radically changes the correlation structure. Whether or not this matters in practice will depend on the specific application. Second, there is the related question of the time horizon of risk. Unexplained yield shifts at specific maturities may be unimportant if they quickly ‘correct’; but this will clearly depend on the investor’s time horizon. If some idiosyncratic yield shift occurs, which has not been anticipated by one’s risk methodology, this may be disastrous for a hedge fund running a highly leveraged trading book with a time horizon of

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hours or days; but an investment manager with a time horizon of months or quarters, who is confident that the phenomenon is transitory and who can afford to wait for it to reverse itself, might not care as much.

Figure 5.8 Actual Treasury yield versus yield predicted by two-factor model.

This is illustrated in Figure 5.8. It compares the observed 10-year Treasury yield from 1953 to 1996 to the yield which would have been predicted by a model in which parallel and slope risk fully determine (via arbitrage pricing theory) the yields of all Treasury bonds. The actual yield often deviates significantly from the theoretical yield, as yield changes unrelated to parallel and slope shifts frequently occurred. But deviations appear to mean revert to zero over periods of around a few months to a year; this can be justified more rigorously by an analysis of autocorrelations. Thus, these deviations matter over short time frames, but perhaps not over long time frames. See Phoa (1998a,b) for further details. Third, there is the question of effects due to market inhomogeneity. In identifying patterns of yield shifts by maturity, principal component analysis implicitly assumes that the only relevant difference between different reference yields is maturity, and that the market is homogeneous in every other way. If it is not – for example, if there are differences in liquidity between different instruments which, in some circumstances, lead to fluctuations in relative yields – then this assumption may not be sound. The US Treasury market in 1998 provided a very vivid example. Yields of onthe-run Treasuries exhibited sharp fluctuations relative to off-the-run yields, with ‘liquidity spreads’ varying from 5 bp to 25 bp. Furthermore, different on-the-run issues were affected in different ways in different times. A principal component analysis based on constant maturity Treasury yields would have missed this source of risk entirely; and in fact, even given yield data on the entire population of Treasury bonds, it would have been extremely difficult to design a similar analysis which would have been capable of identifying and measuring some systematic ‘liquidity

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spread shift’. In this case, risk management for a Treasury book based on principal component analysis needs to be supplemented with other methods. Fourth, there is the possibility that an important risk factor has been ignored. For example, suppose there is an additional kind of fundamental yield curve shift, in which 30- to 100-year bond yields move relative to shorter bond yields. This would not be identified by a principal component analysis, for the simple reason that this maturity range is represented by only one point in the set of reference maturities. Even if the 30-year yield displayed idiosyncratic movements – which it arguably does – the analysis would not identify these as statistically significant. The conjectured ‘long end’ risk factor would only emerge if data on other longer maturities were included; but no such data exists for Treasury bonds. An additional kind of ‘yield curve risk’, which could not be detected at all by an analysis of CMT yields, is the varying yield spread between liquid and illiquid issues as mentioned above. This was a major factor in the US Treasury market in 1998; in fact, from an empirical point of view, fluctuations at the long end of the curve and fluctuations in the spread between on- and off-the-run Treasuries were, in that market, more important sources of risk than curvature shifts – and different methods were required to measure and control the risk arising from these sources. To summarize, a great deal more care is required when using principal component analysis in a financial, rather than physical, setting. One should always remember that the rigorous justifications provided by the differential equations of physics are missing in financial markets, and that seemingly analogous arguments such as those presented in the Appendix are much more heuristic. The proper comparison is with biology or social science rather than physics or engineering.

International bonds Principal component analysis for international markets All our analysis so far has used US data. Are the results applicable to international markets? To answer this question, we analyze daily historical bond yield data for a range of developed countries, drawn from the historical period 1986–96. In broad terms, the results carry over. In almost every case, the fundamental yield curve shifts identified by the analysis are a parallel shift, a slope shift and some kind of curvature shift. Moreover, as shown in Figure 5.9, the relative importance of these different yield curve shifts is very similar in different countries – although there is some evidence that parallel shifts are slightly less dominant, and slope shifts are slightly more important, in Europe and Japan than in USD bloc countries. It is slightly worrying that Switzerland appears to be an exception: the previous results simply do not hold, at least for the dataset used. This proves that one cannot simply take the results for granted; they must be verified for each individual country. For example, one should not assume that yield curve risk measures developed for use in the US bond market are equally applicable to some emerging market. Figure 5.10(a) shows the estimated form of a parallel shift in different countries. Apart from Switzerland, the results are extremely similar. In other words, duration is an equally valid risk measure in different countries. Figure 5.10(b) shows the estimated form of a slope shift in different countries; in this case, estimated slope shifts have been normalized so that the 3-year yield

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Figure 5.9 Relative importance of principal components in various countries. (Historical bond yield data provided by Deutsche Bank Securities).

remains fixed and the 10-year yield moves by 100 bp. Unlike the parallel shift, there is some evidence that the slope shift takes different forms in different countries; this is consistent with the findings reported in Brown and Schaefer (1995). For risk management applications it is thus prudent to estimate the form of the slope shift separately for each country rather than, for example, simply using the US slope shift. Note that parallel/slope correlation also varies between countries, as well as over time. Estimated curvature shifts are not shown, but they are quite different for different countries. Also, breaking the data into subperiods, the form of the curvature shift typically varies over time as it did with the US data. This is further evidence that there is no stable ‘curvature shift’ which can reliably be used to define an additional measure of non-parallel risk.

Co-movements in international bond yields So far we have only used principal component analysis to look at data within a single country, to identify patterns of co-movement between yields at different maturities. We derived the very useful result that two major kinds of co-movement explain most variations in bond yields. It is also possible to analyze data across countries, to identify patterns of comovements between bond yields in different countries. For example, one could carry out a principal component analysis of daily changes in 10-year bond yields for various countries. Can any useful conclusions be drawn? The answer is yes, but the results are significantly weaker. Figure 5.9 shows the dominant principal component identified from three separate datasets: 1970–79, 1980–89 and 1990–98. As one might hope, this dominant shift is a kind of ‘parallel shift’, i.e. a simultaneous shift in bond yields, with the same direction and magnitude, in each country. In other words, the notion of ‘global duration’ seems to make sense:

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Figure 5.10 Shape of (a) ‘parallel’ shift and (b) ‘slope’ shift in different countries.

the aggregate duration of a global bond portfolio is a meaningful risk measure, which measures the portfolio’s sensitivity to an empirically identifiable global risk factor. However, there are three important caveats. First, the ‘global parallel shift’ is not as dominant as the term structure parallel shift identified earlier. In the 1990s, it explained only 54% of variation in global bond yields; in the 1970s, it explained only 29%. In other words, while duration captures most of the interest rate risk of a domestic bond portfolio, ‘global duration’ captures only half, or less, of the interest rate risk of a global bond portfolio: see Figure 5.11.

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Figure 5.11 Dominant principal component, global 10-year bond yields.

Second, the shift in bond yields is not perfectly equal in different countries. It seems to be lower for countries like Japan and Switzerland, perhaps because bond yields have tended to be lower in those countries. Third, the ‘global parallel shift’ is not universal: not every country need be included. For example, it seems as if Australian and French bond yields did not move in step with other countries’ bond yields in the 1970s, and did so only partially in the 1980s. Thus, the relevance of a global parallel shift to each specific country has to be assessed separately. Apart from the global parallel shift, the other eigenvectors are not consistently meaningful. For example, there is some evidence of a ‘USD bloc shift’ in which US, Canadian, Australian and NZ bond yields move while other bond yields remain fixed, but this result is far from robust. To summarize, principal component analysis provides some guidelines for global interest rate risk management, but it does not simplify matters as much as it did for yield curve risk. The presence of currency risk is a further complication; we return to this topic below.

Correlations: between markets, between yield and volatility Recall that principal component analysis uses a single correlation matrix to identify dominant patterns of yield shifts. The results imply something about the correlations themselves: for instance, the existence of a global parallel shift that explains around 50% of variance in global bond yields suggests that correlations should, on average, be positive. However, in global markets, correlations are notoriously time-varying: see Figure 5.12. In fact, short-term correlations between 10-year bond yields in different coun-

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Figure 5.12 Historical 12-month correlations between 10-year bond yields.

tries are significantly less stable than correlations between yields at different maturities within a single country. This means that, at least for short time horizons, one must be especially cautious in using the results of principal component analysis to manage a global bond position. We now discuss a somewhat unrelated issue: the relationship between yield and volatility, which has been missing from our analysis so far. Principal component analysis estimates the form of the dominant yield curve shifts, namely parallel and slope shifts. It says nothing useful about the size of these shifts, i.e. about parallel and slope volatility. These can be estimated instantaneously, using historical or implied volatilities. But for stress testing and scenario analysis, one needs an additional piece of information: whether there is a relationship between volatility and (say) the outright level of the yield curve. For example, when stress testing a trading book under a ò100 bp scenario, should one also change one’s volatility assumption? It is difficult to answer this question either theoretically or empirically. For example, most common term structure models assume that basis point (parallel) volatility is either independent of the yield level, or proportional to the yield level; but these assumptions are made for technical convenience, rather that being driven by the data. Here are some empirical results. Figures 5.13(a)–(c) plot 12-month historical volatilities, expressed as a percentage of the absolute yield level, against the average yield level itself. If basis point volatility were always proportional to the yield level, these graphs would be horizontal lines; if basis point volatility were constant, these graphs would be hyperbolic. Neither seems to be the case. The Japanese dataset suggests that when yields are under around 6–7%, the graph is hyperbolic. All three datasets suggest that when yields are in the 7–10% range, the graph is horizontal. And the US dataset suggests that when yields are over 10%, the graph actually slopes upward: when yields rise,

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Figure 5.13 (a) US, (b) Japan and (c) Germany yield/volatility relationships.

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volatility rises more than proportionately. But in every case, the results are confused by the presence of volatility spikes. The conclusion is that, when stress testing a portfolio, it is safest to assume that when yields fall, basis point volatility need not fall; but when yields rise, basis point volatility will also rise. Better yet, one should run different volatility scenarios as well as interest rate scenarios.

Practical implications Risk management for a leveraged trading desk This section draws some practical conclusions from the above analysis, and briefly sketches some suggestions about risk measurement and risk management policy; more detailed proposals may be found elsewhere in this book. Since parallel and slope shifts are the dominant yield curve risk factors, it makes sense to focus on measures of parallel and slope risk; to structure limits in terms of maximum parallel and slope risk rather than more rigid limits for each point of the yield curve; and to design flexible hedging strategies based on matching parallel and slope risk. If the desk as a whole takes proprietary interest rate risk positions, it is most efficient to specify these in terms of target exposures to parallel and slope risk, and leave it to individual traders to structure their exposures using specific instruments. Rapid stress testing and Value-at-Risk estimates may be computed under the simplifying assumption that only parallel and slope risk exist. This approach is not meant to replace a standard VaR calculation using a covariance matrix for a whole set of reference maturities, but to supplement it. A simplified example of such a VaR calculation appears in Table 5.3, which summarizes both the procedure and the results. It compares the Value-at-Risk of three positions, each with a net market value of $100 million: a long portfolio consisting of a single position in a 10-year par bond; a steepener portfolio consisting of a long position in a 2-year bond and a short position in a 10-year bond with offsetting durations, i.e. offsetting exposures to parallel risk; and a butterfly portfolio consisting of long/short positions in cash and 2-, 5- and 10-year bonds with zero net exposure to both parallel and slope risk. For simplicity, the analysis assumes a ‘total volatility’ of bond yields of about 100 bp p.a., which is broadly realistic for the US market. The long portfolio is extremely risky compared to the other two portfolios; this reflects the fact that most of the observed variance in bond yields comes from parallel shifts, to which the other two portfolios are immunized. Also, the butterfly portfolio appears to have almost negligible risk: by this calculation, hedging both parallel and slope risk removes over 99% of the risk. However, it must be remembered that the procedure assumes that the first three principal components are the only sources of risk. This calculation was oversimplified in several ways: for example, in practice the volatilities would be estimated more carefully, and risk computations would probably be carried out on a cash flow-by-cash flow basis. But the basic idea remains straightforward. Because the calculation can be carried out rapidly, it is easy to vary assumptions about volatility/yield relationships and about correlations, giving additional insight into the risk profile of the portfolio. Of course, the calculation is

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Table 5.3 Simpliﬁed Value-at-Risk calculation using principal components Deﬁnitions di vi pi VaRi VaR

‘duration’ relative to factor i variance of factor i bp volatility of factor i Value-at-Risk due to factor i

ë duration . (factor i shift) ë factor weight óv1/2 i ëpi . di . T

aggregate Value-at-Risk

ó ; VaR2i

Long portfolio

1/2

i

$100m

10-year par bond

Steepener portfolio

$131m ñ$31m

2-year par bond 10-year par bond

Butterﬂy portfolio

$64m $100m ñ$93m $29m

cash 2-year par bond 5-year par bond 10-year par bond

Calculations Assume 100 bp p.a. ‘total volatility’, factors and factor weights as in Table 5.2. Ignore all but the ﬁrst three factors (those shown in Figure 5.4). Parallel Long

Steepener

Butterﬂy

10yr durn Total durn Risk (VaR)

7.79 7.79 5.75%

Slope

Curvature

1 s.d. risk

Daily VaR

1.50 1.50 0.27%

ñ0.92 ñ0.92 ñ0.07%

5.95%

$376 030

2yr durn 10yr durn Total durn Risk (VaR)

2.39 ñ2.39 0.00 0.00

ñ0.87 ñ0.46 ñ1.33 ñ0.24

ñ0.72 0.28 ñ0.44 ñ0.04

0.28%

$17 485

Cash durn 2yr durn 5yr durn 10yr durn Total durn Risk (VaR)

0.00 1.83 ñ4.08 2.25 0.00 0.00%

0.00 ñ0.67 0.24 0.43 0.00 0.00%

0.00 ñ0.55 1.20 ñ0.26 0.38 0.03%

0.03%

$1 954

approximate, and in practice large exposures at specific maturities should not be ignored. That would tend to understate the risk of butterfly trades, for example. However, it is important to recognize that a naive approach to measuring risk, which ignores the information about co-movements revealed by a principal component analysis, will tend to overstate the risk of a butterfly position; in fact, in some circumstances a butterfly position is no riskier than, say, an exposure to the spread between on- and off-the-run Treasuries. In other words, the analysis helps risk managers gain some sense of perspective when comparing the relative importance of different sources of risk. Risk management for a global bond book is harder. The results of the analysis are mainly negative: they suggest that the most prudent course is to manage each country exposure separately. For Value-at-Risk calculations, the existence of a ‘global parallel shift’ suggests an alternative way to estimate risk, by breaking it into two

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components: (a) risk arising from a global shift in bond yields, and (b) countryspecific risk relative to the global component. This approach has some important advantages over the standard calculation, which uses a covariance matrix indexed by country. First, the results are less sensitive to the covariances, which are far from stable. Second, it is easier to add new countries to the analysis. Third, it is easier to incorporate an assumption that changes in yields have a heavy-tailed (non-Gaussian) distribution, which is particularly useful when dealing with emerging markets. Again, the method is not proposed as a replacement for standard VaR calculations, but as a supplement.

Total return management and benchmark choice For an unleveraged total return manager, many of the proposals are similar. It is again efficient to focus mainly on parallel and slope risk when setting interest rate risk limits, implementing an interest rate view, or designing hedging strategies. This greatly simplifies interest rate risk management, freeing up the portfolio manager’s time to focus on monitoring other forms of risk, on assessing relative value, and on carrying out more detailed scenario analysis. Many analytics software vendors, such as CMS, provide measures of slope risk. Investment managers should ensure that such a measure satisfies two basic criteria. First, it should be consistent with the results of a principal component analysis: a measure of slope risk based on an unrealistic slope shift is meaningless. Second, it should be easy to run, and the results should be easy to interpret: otherwise, it will rarely be used, and slope risk will not be monitored effectively. The above comments on risk management of global bond positions apply equally well in the present context. However, there is an additional complication. Global bond investors tend to have some performance benchmark, but it is most unclear how an ‘optimal’ benchmark should be constructed, and how risk should be measured against it. For example, some US investors simply use a US domestic index as a benchmark; many use a currency unhedged global benchmark. (Incidentally, the weights of a global benchmark are typically determined by issuance volumes. This is somewhat arbitrary: it means that a country’s weight in the index depends on its fiscal policy and on the precise way public sector borrowing is funded. Mason has suggested using GDP weights; this tends to lower the risk of the benchmark.) Figures 5.14(a)–(c) may be helpful. They show the risk/return profile, in USD terms, of a US domestic bond index; currency unhedged and hedged global indexes; and the full range of post hoc efficient currency unhedged and hedged portfolios. Results are displayed separately for the 1970s, 1980s and 1990s datasets. The first observation is that the US domestic index has a completely different (and inferior) risk/return profile to any of the global portfolios. It is not an appropriate benchmark. The second observation is that hedged and unhedged portfolios behave in completely different ways. In the 1970s, hedged portfolios were unambiguously superior; in the 1980s, hedged and unhedged portfolios behaved almost like two different asset types; and in the 1990s, hedged and unhedged portfolios seemed to lie on a continuous risk/return scale, with hedged portfolios at the less risky end. If a benchmark is intended to be conservative, a currency hedged benchmark is clearly appropriate. What, then, is a suitable global benchmark? None of the post hoc efficient portfolios will do, since the composition of efficient portfolios is extremely unstable over time – essentially because both returns and covariances are unstable. The most plausible

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Figure 5.14 Global bond efﬁcient frontier and hedged index: (a) 1970s, (b) 1980s and (c) 1990s. (Historical bond and FX data provided by Deutsche Bank Securities).

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candidate is the currency hedged global index. It has a stable composition, has relatively low risk, and is consistently close to the efficient frontier. Once a benchmark is selected, principal component analysis may be applied as follows. First, it identifies countries which may be regarded as particularly risk relative to the benchmark; in the 1970s and 1980s this would have included Australia and France (see Figure 5.11). Note that this kind of result is more easily read off from the analysis than by direct inspection of the correlations. Second, it helps managers translate country-specific views into strategies. That it, by estimating the proportion of yield shifts attributable to a global parallel shift (around 50% in the 1990s) it allows managers will a bullish or bearish view on a specific country to determine an appropriate degree of overweighting. Third, it assists managers who choose to maintain open currency risk. A more extensive analysis can be used to identify ‘currency blocs’ (whose membership may vary over time) and to estimate co-movements between exchange rates and bond yields. However, all such results must be used with great caution.

Asset/liability management and the use of risk buckets For asset/liability managers, the recommendations are again quite similar. One should focus on immunizing parallel risk (duration) and slope risk. If these two risk factors are well matched, then from an economic point of view the assets are an effective hedge for the liabilities. Key rate durations are a useful way to measure exposure to individual points on the yield curve; but it is probably unnecessary to match all the key rate durations of assets and liabilities precisely. However, one does need to treat both the short and the very long end of the yield curve separately. Regarding the long end of the yield curve, it is necessary to ensure that really longdated liabilities are matched by similarly long-dated assets. For example, one does not want to be hedging 30-year liabilities with 10-year assets, which would be permitted if one focused only on parallel and slope risk. Thus, it is desirable to ensure that 10-year to 30-year key rate durations are reasonably well matched. Regarding the short end of the yield curve, two problems arise. First, for maturities less than about 18–24 months – roughly coinciding with the liquid part of the Eurodollar futures strip – idiosyncratic fluctuations at the short end of the curve introduce risks additional to parallel and slope risk. It is safest to identify and hedge these separately, either using duration bucketing or partial durations. Second, for maturities less than about 12 months, it is desirably to match actual cashflows and not just risks. That is, one needs to generate detailed cashflow forecasts rather than simply matching interest rate risk measures. To summarize, an efficient asset/liability management policy might be described as follows: from 0–12 months, match cash flows in detail; from 12–24 months, match partial durations or duration buckets in detail; from 2–15 years, match parallel and slope risk only; beyond 15 years, ensure that partial durations are roughly matched too. Finally, one must not forget optionality. If the assets have very different option characteristics from the liabilities – which may easily occur when callable bonds or mortgage-backed securities are held – then it is not sufficient to match interest rate exposure in the current yield curve environment. One must also ensure that risks are matched under different interest rate and volatility scenarios. Optionality is treated in detail elsewhere in this book. In conclusion: principal component analysis suggests a simple and attractive

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solution to the problem of efficiently managing non-parallel yield curve risk. It is easy to understand, fairly easy to implement, and various off-the-shelf implementations are available. However, there are quite a few subtleties and pitfalls involved. Therefore, risk managers should not rush to implement policies, or to adopt vendor systems, without first deepening their own insight through experimentation and reflection.

Appendix: Economic factors driving the curve Macroeconomic explanation of parallel and slope risk This appendix presents some theoretical explanations for why (a) parallel and slope shifts are the dominant kinds of yield curve shift that occur, (b) curvature shifts are observed but tend to be both transitory and inconsistent in form, and (c) the behavior of the short end of the yield curve is quite idiosyncratic. The theoretical analysis helps to ascertain which empirical findings are really robust and can be relied upon: that is, an empirical result is regarded as reliable if it has a reasonable theoretical explanation. For reasons of space, the arguments are merely sketched. We first explain why parallel and slope shifts emerge naturally from a macroeconomic analysis of interest rate expectations. For simplicity, we use an entirely standard linear macroeconomic model, shown in Table 5A.1; see Frankel (1995) for details. Table 5A.1 A macroeconomic model of interest rate expectations Model deﬁnitions: i short-term nominal interest rate ne expected long-term inﬂation rate re expected long-term real interest rate y log of output y¯ log of normal or potential output m log of the money supply p log of the price level c, {, j, o constant model parameters (elasticities) Model assumptions: The output gap is related to the current real interest rate through investment demand: yñy¯ óñc(iñneñr e ) Real money demand depends positively on income and negatively on the interest rate: mñpó{ yñji Price changes are determined by excess demand and expected long-term inﬂation: dp óo( yñy¯ )òne dt Theorem (Frankel, 1995): The expected rate of change of the interest rate is given by: oc di óñd(iñne ñr e ), where dó dt {còj

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The model is used in the following way. Bond yields are determined by market participants’ expectations about future short-term interest rates. These in turn are determined by their expectations about the future path of the economy: output, prices and the money supply. It is assumed that market participants form these expectations in a manner consistent with the macroeconomic model. Now, the model implies that the short-term interest rate must evolve in a certain fixed way; thus, market expectations must, ‘in equilibrium’, take a very simple form. To be precise, it follows from the theorem stated in Table 5A.1 that if i 0 is the current short-term nominal interest rate, i t is the currently expected future interest rate at some future time t, and i ê is the long-term expected future interest rate, then rational interest rate expectations must take the following form in equilibrium: i t ói ê ò(i 0 ñi ê )eñ dt In this context, a slope shift corresponds to a change in either i ê or i 0 , while a parallel shift corresponds to a simultaneous change in both. Figure 5A.1 shows, schematically, the structure of interest rate expectations as determined by the model. The expected future interest rate at some future time is equal to the expected future rate of inflation, plus the expected future real rate. (At the short end, some distortion is possible, of which more below.)

Figure 5A.1 Schematic breakdown of interest rate expectations.

In this setting, yield curve shifts occur when market participants revise their expectations about future interest rates – that is, about future inflation and output growth. A parallel shift occurs when both short- and long-term expectations change at once, by the same amount. A slope shift occurs when short-term expectations change but long-term expectations remain the same, or vice versa. Why are parallel shifts so dominant? The model allows us to formalize the following simple explanation: in financial markets, changes in long-term expectations are

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primarily driven by short-term events, which, of course, also drive changes in shortterm expectations. For a detailed discussion of this point, see Keynes (1936). Why is the form of a slope shift relatively stable over time, but somewhat different in different countries? In this setting, the shape taken by a slope shift is determined by d, and thus by the elasticity parameters c,{ j, o of the model. These parameters depend in turn on the flexibility of the economy and its institutional framework – which may vary from country to country – but not on the economic cycle, or on the current values of economic variables. So d should be reasonably stable. Finally, observe that there is nothing in the model which ensures that parallel and slope shifts should be uncorrelated. In fact, using the most natural definition of ‘slope shift’, there will almost certainly be a correlation – but the value of the correlation coefficient is determined by how short-term events affect market estimates of the different model variables, not by anything in the underlying model itself. So the model does not give us much insight into correlation risk.

Volatility shocks and curvature risk We have seen that, while principal component analysis seems to identify curvature shifts as a source of non-parallel risk, on closer inspection the results are somewhat inconsistent. That is, unlike parallel and slope shifts, curvature shifts do not seem to take a consistent form, making it difficult to design a corresponding risk measure. The main reason for this is that ‘curvature shifts’ can occur for a variety of quite different reasons. A change in mid-range yields can occur because (a) market volatility expectations have changed, (b) the ‘term premium’ for interest rate risk has changed, (c) market segmentation has caused a temporary supply/demand imbalance at specific maturities, or (d) a change in the structure of the economy has caused a change in the value d above. We briefly discuss each of these reasons, but readers will need to consult the References for further details. Regarding (a): The yield curve is determined by forward short-term interest rates, but these are not completely determined by expected future short-term interest rates; forward rates have two additional components. First, forward rates display a downward ‘convexity bias’, which varies with the square of maturity. Second, forward rates display an upward ‘term premium’, or risk premium for interest rate risk, which (empirically) rises at most linearly with maturity. The size of both components obviously depends on expected volatility as well as maturity. A change in the market’s expectations about future interest rate volatility causes a curvature shift for the following reason. A rise in expected volatility will not affect short maturity yields since both the convexity bias and the term premium are negligible. Yields at intermediate maturities will rise, since the term premium dominates the convexity bias at these maturities; but yields at sufficiently long maturities will fall, since the convexity bias eventually dominates. The situation is illustrated in Figure 5A.2. The precise form taken by the curvature shift will depend on the empirical forms of the convexity bias and the term premium, neither of which are especially stable. Regarding (b): The term premium itself, as a function of maturity, may change. In theory, if market participants expect interest rates to follow a random walk, the term premium should be a linear function of maturity; if they expect interest rates to range trade, or mean revert, the term premium should be sub-linear (this seems to be observed in practice). Thus, curvature shifts might occur when market participants revise their expectations about the nature of the dynamics of interest rates,

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Figure 5A.2 Curvature shift arising from changing volatility expectations.

perhaps because of a shift in the monetary policy regime. Unfortunately, effects like this are nearly impossible to measure precisely. Regarding (c): Such manifestations of market ineffiency do occur, even in the US market. They do not assume a consistent form, but can occur anywhere on the yield curve. Note that, while a yield curve distortion caused by a short-term supply/ demand imbalance may have a big impact on a leveraged trading book, it might not matter so much to a typical mutual fund or asset/liability manager. Regarding (d): It is highly unlikely that short-term changes in d occur, although it is plausible that this parameter may drift over a secular time scale. There is little justification for using ‘sensitivity to changes in d’ as a measure of curvature risk. Curvature risk is clearly a complex issue, and it may be dangerous to attempt to summarize it using a single stylized ‘curvature shift’. It is more appropriate to use detailed risk measures such as key rate durations to manage exposure to specific sections of the yield curve.

The short end and monetary policy distortions The dynamics of short maturity money market yields is more complex and idiosyncratic than that of longer maturity bond yields. We have already seen a hint of this in Figure 5.6(c), which shows that including T-bill yields in the dataset radically changes the results of a principal component analysis; the third eigenvector represents, not a ‘curvature shift’ affecting 3–5 year maturities, but a ‘hump shift’ affecting maturities around 1 year. This is confirmed by more careful studies. As with curvature shifts, hump shifts might be caused by changes in the term premium. But there is also an economic explanation for this kind of yield curve shift: it is based on the observation that market expectations about the path of interest rates in the near future can be much more complex than longer term expectations.

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For example, market participants may believe that monetary policy is ‘too tight’ and can make detailed forecasts about when it may be eased. Near-term expected future interest rates will not assume the simple form predicted by the macroeconomic model of Figure 5.4 if investors believe that monetary policy is ‘out of equilibrium’. This kind of bias in expectations can create a hump or bowl at the short end of the yield curve, and is illustrated schematically in Figure 5A.1. One would not expect a ‘hump factor’ to take a stable form, since the precise form of expectations, and hence of changes in expectations, will depend both on how monetary policy is currently being run and on specific circumstances. Thus, one should not feed money market yields to a principal component analysis and expect it to derive a reliable ‘hump shift’ for use in risk management. For further discussion and analysis, see Phoa (1998a,b). The overall conclusion is that when managing interest rate risk at the short end of the yield curve, measures of parallel and slope risk must be supplemented by more detailed exposure measures. Similarly, reliable hedging strategies cannot be based simply on matching parallel and slope risk, but must make use of a wider range of instruments such as a whole strip of Eurodollar futures contracts.

Acknowledgements The research reported here was carried out while the author was employed at Capital Management Sciences. The author has attempted to incorporate several useful suggestions provided by an anonymous reviewer.

References The following brief list of references is provided merely as a starting point for further reading, which might be structured as follows. For general background on matrix algebra and matrix computations, both Jennings and McKeown (1992) and the classic Press et al. (1992) are useful, though there are a multitude of alternatives. On principal components analysis, Litterman and Scheinkman (1991) and Garbade (1996) are still well worth reading, perhaps supplemented by Phoa (1998a,b) which contain further practical discussion. This should be followed with Buhler and Zimmermann (1996) and Hiraki et al. (1996) which make use of additional statistical techniques not discussed in the present chapter. However, at this point it is probably more important to gain hands-on experience with the techniques and, especially, the data. Published results should not be accepted unquestioningly, even those reported here! For numerical experimentation, a package such as Numerical Python or MATLABTM is recommended; attempting to write one’s own routines for computing eigenvectors is emphatically not recommended. Finally, historical bond data for various countries may be obtained from central banking authorities, often via the World Wide Web.1 Brown, R. and Schaefer, S. (1995) ‘Interest rate volatility and the shape of the term structure’, in Howison, S., Kelly, F. and Wilmott, P. (eds), Mathematical Models in Finance, Chapman and Hall.

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Buhler, A. and Zimmermann, H. (1996) ‘A statistical analysis of the term structure of interest rates in Switzerland and Germany’, Journal of Fixed Income, December. Frankel, J. (1995) Financial Markets and Monetary Policy, MIT Press. Garbade, K. (1996) Fixed Income Analytics, MIT Press. Hiraki, T., Shiraishi, N. and Takezawa, N. (1996) ‘Cointegration, common factors and the term structure of Yen offshore interest rates’, Journal of Fixed Income, December. Ho, T. (1992) ‘Key rate durations: measures of interest rate risk’, Journal of Fixed Income, September. Jennings, A. and McKeown, J. (1992) Matrix Computation (2nd edn), Wiley. Keynes, J. M. (1936) The General Theory of Employment, Interest and Money, Macmillan. Litterman, R. and Scheinkman, J. (1991) ‘Common factors affecting bond returns’, Journal Fixed Income, June. Phoa, W. (1998a) Advanced Fixed Income Analytics, Frank J. Fabozzi Associates. Phoa, W. (1998b) Foundations of Bond Market Mathematics, CMS Research Report. Press, W., Teukolsky, S., Vetterling, W. and Flannery, B. (1992) Numerical Recipes in C: The Art of Scientific Computing (2nd edn), Cambridge University Press. Rebonato, R., and Cooper, I. (1996) ‘The limitations of simple two-factor interest rate models’, Journal Financial Engineering, March.

Note 1

The International datasets used here were provided by Sean Carmody and Richard Mason of Deutsche Bank Securities. The author would also like to thank them for many useful discussions.

6

Implementation of a Value-at-Risk system ALVIN KURUC

Introduction In this chapter, we discuss the implementation of a value-at-risk (VaR) system. The focus will be on the practical nuts and bolts of implementing a VaR system in software, as opposed to a critical review of the financial methodology. We have therefore taken as our primary example a relatively simple financial methodology, a first-order variance/covariance approach. The prototype of this methodology is the basic RiskMetricsT M methodology developed by J. P. Morgan [MR96].1 Perhaps the main challenge in implementing a VaR system is in coming up with a systematic way to express the risk of a bewilderingly diverse set of financial instruments in terms of a relatively small set of risk factors. This is both a financial-engineering and a system-implementation challenge. The body of this chapter will focus on some of the system-implementation issues. Some of the financial-engineering issues are discussed in the appendices.

Overview of VaR methodologies VaR is distinguished from other risk-management techniques in that it attempts to provide an explicit probabilistic description of future changes in portfolio value. It requires that we estimate the probability distribution of the value of a financial portfolio at some specific date in the future, termed the target date. VaR at the 1ña confidence level is determined by the a percentile of this probability distribution. Obviously, this estimate must be based on information that is known today, which we term the anchor date. Most procedures for estimating VaR are based on the concept that a given financial portfolio can be valued in terms of a relatively small of factors, which we term risk factors. These can be prices of traded instruments that are directly observed in the market or derived quantities that are computed from such prices. One then constructs a probabilistic model for the risk factors and derives the probability distribution of the portfolio value as a consequence. To establish a VaR methodology along these lines, we need to 1 Define the risk factors. 2 Establish a probabilistic model for the evolution of these risk factors.

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3 Determine the parameters of the model from statistical data on the risk factors. 4 Establish computational procedures for obtaining the distribution of the portfolio value from the distribution of the risk factors. For example, the RiskMetrics methodology fits into this framework as follows: 1 The risk factors consist of FX rates, zero-coupon discount factors for specific maturities, spot and forward commodity prices, and equity indices. 2 Returns on the risk factors are modeled as being jointly normal with zero means. 3 The probability distribution of the risk factors is characterized by a covariance matrix, which is estimated from historical time series and provided in the RiskMetrics datasets. 4 The portfolio value is approximated by its first-order Taylor-series expansion in the risk factors. Under this approximation, the portfolio value is normally distributed under the assumed model for the risk factors.

Deﬁning the risk factors The risk factors should contain all market factors for which one wishes to assess risk. This will depend upon the nature of one’s portfolio and what data is available. Important variables are typically foreign-exchange (FX) and interest rates, commodity and equity prices, and implied volatilities for the above. In many cases, the market factors will consist of derived quantities, e.g. fixed maturity points on the yield curve and implied volatilities, rather than directly observed market prices. Implied volatilities for interest rates are somewhat problematic since a number of different mathematical models are used and these models can be inconsistent with one another. It should be noted that the risk factors for risk management might differ from those used for pricing. For example, an off-the-run Treasury bond might be marked to market based on a market-quoted price, but be valued for risk-management purposes from a curve built from on-the-run bonds in order to reduce the number of risk factors that need to be modeled. A key requirement is that it should be possible to value one’s portfolio in terms of the risk factors, at least approximately. More precisely, it should be possible to assess the change in value of the portfolio that corresponds to a given change in the risk factors. For example, one may capture the general interest-rate sensitivity of a corporate bond, but not model the changes in value due to changes in the creditworthiness of the issuer. Another consideration is analytical and computational convenience. For example, suppose one has a portfolio of interest-rate derivatives. Then the risk factors must include variables that describe the term structure of interest rates. However, the term structure of interest rates can be described in numerous equivalent ways, e.g. par rates, zero-coupon discount rates, zero-coupon discount factors, etc. The choice will be dictated by the ease with which the variables can be realistically modeled and further computations can be supported.

Probabilistic model for risk factors The basic dichotomy here is between parametric and non-parametric models. In parametric models, the probability distribution of the risk factors is assumed to be of a specific functional form, e.g. jointly normal, with parameters estimated from historical time series. In non-parametric models, the probability distribution of

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changes in the risk factors is taken to be precisely the distribution that was observed empirically over some interval of time in the past. The non-parametric approach is what is generally known as historical VaR. Parametric models have the following advantages: 1 The relevant information from historical time series is encapsulated in a relatively small number of parameters, facilitating its storage and transmission. 2 Since the distribution of the market data is assumed to be of a specific functional form, it may be possible to derive an analytic form for the distribution of the value of the portfolio. An analytic solution can significantly reduce computation time. 3 It is possible to get good estimates of the parameters even if the individual time series have gaps, due to holidays, technical problems, etc. 4 In the case of RiskMetrics, the necessary data is available for free. Parametric models have the following disadvantages: 1 Real market data is, at best, imperfectly described by the commonly used approximating distributions. For example, empirical distributions of log returns have skewed fat tails and it is intuitively implausible to model volatilities as being jointly normal with their underlyings. 2 Since one does not have to worry about model assumptions, non-parametric models are more flexible, i.e. it is easy to add new variables. 3 In the opinion of some, non-parametric models are more intuitive.

Data for probabilistic model For non-parametric models, the data consists of historical time-series of risk factors. For parametric models, the parameters are usually estimated from historical time series of these variables, e.g. by computing a sample covariance matrix. Collection, cleaning, and processing of historical time-series can be an expensive proposition. The opportunity to avoid this task has fueled the popularity of the RiskMetrics datasets.

Computing the distribution of the portfolio value If the risk factors are modeled non-parametrically, i.e. for historical VaR, the time series of changes in the risk factors are applied one by one to the current values of the risk factors and the portfolio revalued under each scenario. The distribution of portfolio values is given simply by the resulting histogram. A similar approach can be used for parametric models, replacing the historical perturbations by pseudorandom ones drawn from the parametric model. This is termed Monte Carlo VaR. Alternatively, if one makes certain simplifying assumptions, one can compute the distribution of portfolio values analytically. For example, this is done in the RiskMetrics methodology. The benefit of analytic methodologies is that their computational burden is much lower. In addition, analytic methods may be extended to give additional insight into the risk profile of a portfolio.

Variance/covariance methodology for VaR We will take as our primary example the variance/covariance methodology, specifically RiskMetrics. In this section, we provide a brief overview of this methodology.

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Fundamental assets For the purposes of this exposition, it will be convenient to take certain asset prices as risk factors. We will term these fundamental assets. In what follows, we will see that this choice leads to an elegant formalism for the subsequent problem of estimating the distribution of portfolio value. We propose using three types of fundamental assets: 1 Spot foreign-exchange (FX) rates. We fix a base currency. All other currencies will be termed foreign currencies. We express spot prices of foreign currencies as the value of one unit of foreign currency in units of base currency. For example, if the base currency were USD, the value of JPY would be in the neighborhood of USD 0.008. 2 Spot asset prices. These prices are expressed in the currency unit that is most natural and convenient, with that currency unit being specified as part of the price. We term this currency the native currency for the asset. For example, shares in Toyota would be expressed in JPY. Commodity prices would generally be expressed in USD, but could be expressed in other currencies if more convenient. 3 Discount factors. Discount factors for a given asset, maturity, and credit quality are simply defined as the ratio of the value of that asset for forward delivery at the given maturity by a counterparty of a given credit quality to the value of that asset for spot delivery. The most common example is discount factors for currencies, but similar discount factors may be defined for other assets such as commodities and equities. Thus, for example, we express the value of copper for forward delivery as the spot price of copper times a discount factor relative to spot delivery. In the abstract, there is no essential difference between FX rates and asset prices. However, we distinguish between them for two reasons. First, while at any given time we work with a fixed base currency, we need to be able to change this base currency and FX rates need to be treated specially during a change of base currency. Second, it is useful to separate out the FX and asset price components of an asset that is denominated in a foreign currency.

Statistical model for fundamental assets Single fundamental assets The essential assumption behind RiskMetrics is that short-term, e.g. daily, changes in market prices of the fundamental assets can be approximated by a zero-mean normal distribution. Let vi (t) denote the present market value of the ith fundamental asset at time t. Define the relative return on this asset over the time interval *t by ri (t)•[vi (tò*t)ñvi (t)]/vi (t). The relative return is modeled by a zero-mean normal distribution. We will term the standard deviation of this distribution the volatility, and denote it by mi (t). Under this model, the absolute return vi (tò*t)ñvi (t) is normally distributed with zero mean and standard deviation pi (t)óvi (t)mi (t). Multiple fundamental assets The power of the RiskMetrics approach comes from modeling the changes in the prices of the fundamental assets by a joint normal distribution which takes into account the correlations of the asset prices as well as their volatilities. This makes it possible to quantify the risk-reduction effect of portfolio diversification.

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Suppose there are m fundamental assets and define the relative-return vector r•[r1 , r2 , . . . , rm ]T (T here denotes vector transpose). The relative-return vector is modeled as having a zero-mean multivariate normal distribution with covariance matrix $.

Statistical data The covariance matrix is usually constructed from historical volatility and correlation data. In the particular case of the RiskMetrics methodology, the data needed to construct the covariance matrix is provided in the RiskMetrics datasets, which are published over the Internet free of charge. RiskMetrics datasets provide a volatility vector (t) and a correlation matrix R to describe the distribution of r. The covariance matrix is given in terms of the volatility vector and correlation matrix by $óT ¥ R where ¥ denotes element-by-element matrix multiplication.

Distribution of portfolio value Primary assets The fundamental assets are spot positions in FX relative to base currency, spot positions in base- and foreign-currency-denominated assets, and forward positions in base currency. We define a primary asset as a spot or forward position in FX or in another base- or foreign-currency-denominated asset expressed in base currency. We will approximate the sensitivity of any primary asset to changes in the fundamental assets by constructing an approximating portfolio of fundamental assets. The process of going from a given position to the approximating portfolio is termed mapping. The rules are simple. A primary asset with present value v has the following exposures in fundamental assets: 1 Spot and forward positions in non-currency assets have an exposure to the corresponding spot asset price numerically equal to its present value. 2 Forward positions have an exposure to the discount factor for the currency that the position is denominated in numerically equal to its present value. 3 Foreign-currency positions and positions in assets denominated in foreign currencies have a sensitivity to FX rates for that foreign currency numerically equal to its present value. Example Suppose the base currency is USD. A USD-denominated zero-coupon bond paying USD 1 000 000 in one year that is worth USD 950 000 today has an exposure of USD 950 000 to the 1-year USD discount factor. A GBP-denominated zero-coupon bond paying GBP 1 000 000 in one year that is worth USD 1 500 000 today has an exposure of USD 1 500 000 to both the 1-year GBP discount factor and the GBP/USD exchange rate. A position in the FTSE 100 that is worth USD 2 000 000 today has an exposure of USD 2 000 000 to both the FTSE 100 index and the GBP/USD exchange rate. Example Suppose the current discount factor for the 1-year USD-denominated zero-coupon

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bond is 0.95, and the current daily volatility of this discount factor is 0.01. Then the present value of the bond with face value USD 1 000 000 is USD 950 000 and the standard deviation of the change in its value is USD 9500. Portfolios of primary assets Gathering together the sum of the portfolio exposures into a total exposure vector v•[vi , v2 , . . . , vm ]T, the model assumed for the fundamental assets implies that the absolute return of the portfolio, i.e. m

m

; vi (tò*t )ñvi (t)ó ; vi (t)ri (t) ió1

ió1

has a zero-mean normal distribution. The distribution is completely characterized by its variance, p2 óvT $v. Example Consider a portfolio consisting of fixed cashflows in amounts USD 1 000 000 and USD 2 000 000 arriving in 1 and 2 years, respectively. Suppose the present value of 1 dollar paid 1 and 2 years from now is 0.95 and 0.89, respectively. The presentvalue vector is then vó[950 000 1 780 000]T Suppose the standard deviations of daily relative returns in 1- and 2-year discount factors are 0.01 and 0.0125, respectively, and the correlation of these returns is 0.8. The covariance matrix of the relative returns is then given by ó

ó

0.012

0.8 · 0.01 · 0.00125

0.8 · 0.01 · 0.00125

0.01252

0.0001

0.0001

0.0001

0.00015625

The variance of the valuation function is given by pó[950 000 1 780 000]

0.0001

0.0001

0.0001

0.00015625

950 000

1 780 000

ó0.9025î108 ò3.3820î108 ò4.9506î108 ó9.2351î108 and the standard deviation of the portfolio is USD 30 389. Value at risk Given that the change in portfolio value is modeled as a zero-mean normal distribution with standard deviation p, we can easily compute the probability of sustaining a loss of any given size. The probability that the return is less than the a percentile point of this distribution is, by definition, a. For example, the 5th percentile of the normal distribution is Bñ$1.645p, so the probability of sustaining a loss of greater than 1.645p is 5%. In other words, at the 95% confidence level, the VaR is 1.645p.

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The 95% confidence level is, of course, arbitrary and can be modified by the user to fit the requirements of a particular application. For example, VaR at the 99% confidence level is B2.326p. Example The 1-year zero-coupon bond from the above examples has a daily standard deviation of USD 9500. Its daily VaR at the 95% and 99% confidence levels is thus USD 15 627.50 and USD 22 097.00, respectively.

Asset-ﬂow mapping Interpolation of maturity points Earlier we outlined the VaR calculation for assets whose prices could be expressed in terms of fundamental asset prices. Practical considerations limit the number of fundamental assets that can be included as risk factors. In particular, it is not feasible to include discount factors for every possible maturity. In this section, we look at approximating assets not included in the set of fundamental assets by linear combinations of fundamental assets. As a concrete example, consider the mapping of future cashflows. The fundamental asset set will contain discount factors for a limited number of maturities. For example, RiskMetrics datasets cover zero-coupon bond prices for bonds maturing at 2, 3, 4, 5, 7, 9, 10, 15, 20, and 30 years. To compute VaR for a real coupon-bearing bond, it is necessary to express the principal and coupon payments maturities in terms of these vertices. An obvious approach is to apportion exposures summing to the present value of each cashflow to the nearest maturity or maturities in the fundamental asset set. For example, a payment of USD 1 000 000 occurring in 6 years might have a present value of USD 700 000. This exposure might be apportioned to exposures to the 5- and 7-year discount factors totaling USD 700 000. In the example given in the preceding paragraph, the condition that the exposures at the 5- and 7-year points sum to 700 000 is obviously insufficient to determine these exposures. An obvious approach would be to divide these exposures based on a simple linear interpolation. RiskMetrics suggests a more elaborate approach in which, for example, the volatility of the 6-year discount factor would be estimated by linear interpolation of the volatilities of the 5- and 7-year discount factors. Exposures to the 5- and 7-year factors would then be apportioned such that the volatility obtained from the VaR calculation agreed with the interpolated 6-year volatility. We refer the interested reader to Morgan and Reuters (1996) for details.

Summary of mapping procedure At this point, it will be useful to summarize the mapping procedure for asset flows in a systematic way. We want to map a spot or forward position in an asset. This position is characterized by an asset identifier, a credit quality, and a maturity. The first step is to compute the present value (PV) of the position. To do so, we need the following information: 1 The current market price of the asset in its native currency.

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2 If the native currency of the asset is not the base currency, we need the current FX rate for the native currency. 3 If the asset is for forward delivery, we need the current discount factor for the asset for delivery at the given maturity by a counterparty of the given credit quality. To perform the PV calculation in a systemic manner, we require the following data structures: 1 We need a table, which we term the asset-price table, which maps the identifying string for each asset to a current price in terms of a native currency and amount. An example is given in Table 6.1. Table 6.1 Example of an asset-price table Asset Copper British Airways

Currency

Price

USD GBP

0.64 4.60

2 We need a table, which we term the FX table, which maps the identifying string for each currency to its value in base currency. An example is given in Table 6.2. Table 6.2 Example of an FX table with USD as base currency Currency USD GBP JPY

Value 1.0 1.6 0.008

3 We need an object, which we term a discounting term structure (DTS), that expresses the value of assets for forward delivery in terms of their value for spot delivery. DTS are specified by an asset-credit quality pair, e.g. USD-Treasury or Copper-Comex. The essential function of the DTS is to provide a discount factor for any given maturity. A simple implementation of a DTS could be based on an ordered list of maturities and discount factors. Discount factors for dates not on the list would be computed by log-linear interpolation. An example is given in Table 6.3.

Table 6.3 Example DTS for Copper-Comex Maturity (years) 0.0 0.25 0.50

Discount factor 1.0 0.98 0.96

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The steps in the PV calculation are then as follows: 1 Based on the asset identifier, look up the asset price in the asset price table. 2 If the price in the asset price table is denominated in a foreign currency, convert the asset price to base currency using the FX rate stored in the FX table. 3 If the asset is for forward delivery, discount its price according to its maturity using the DTS for the asset and credit quality. Example Consider a long 3-month-forward position of 100 000 lb of copper with a base currency of USD. From Table 6.1, the spot value of this amount of copper is USD 64 000. From Table 6.2, the equivalent value in base currency is USD 64 000. From Table 6.3, we see that this value should be discounted by a factor of 0.98 for forward delivery, giving a PV of USD 62 720. Having computed the PV, the next step is to assign exposures to the fundamental assets. We term the result an exposure vector. To facilitate this calculation, it is useful to introduce another object, the volatility term structure (VTS). The data for this object is an ordered, with respect to maturity, sequence of volatilities for discount factors for a given asset and credit quality. Thus, for example, we might have a VTS for USD-Treasury or GBP-LIBOR. The most common example is discount factors for currencies, but similar discount factors may be defined for other assets such as commodities and equities. An example is given in Table 6.4. Table 6.4 Example VTS for Copper-Comex Term (years) 0.0 0.25 0.5

Volatility 0.0 0.01 0.0125

The steps to compute the exposure vector from the PV are as follows: 1 If the asset is not a currency, add an exposure in the amount of the PV to the asset price. 2 If the asset is denominated in a foreign currency, add an exposure in the amount of the PV to the native FX rate. 3 If the asset is for forward delivery, have the VTS add an exposure in the amount of the PV to its discount factor. If the maturity of the asset is between the maturities represented in the fundamental asset set, the VTS will apportion this exposure to the one or two adjacent maturity points. Example Continuing our copper example from above, we get exposures of USD 62 720 to the spot price of copper and the 3-month discount factor for copper. A good example of the utility of the VTS abstraction is given by the specific problem of implementing the RiskMetrics methodology. RiskMetrics datasets supply volatilities for three types of fixed-income assets: money market, swaps, and government bonds. Money-market volatilities are supplied for maturities out to one year, swap-rate volatilities are supplied for maturities from 2 up to 10 years, and government bond rate volatilities are supplied for maturities from 2 out to 30 years. In a

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software implementation, it is desirable to isolate the mapping process from these specifics. One might thus construct two VTS from these data, a Treasury VTS from the money market and government-bond points and a LIBOR VTS from the moneymarket and swap rates, followed by government-bond points past the available swap rates. The mapping process would only deal with the VTS abstraction and has no knowledge of where the underlying data came from.

Mapping derivatives At this point, we have seen how to map primary assets. In particular, in the previous section we have seen how to map asset flows at arbitrary maturity points to maturity points contained within fundamental asset set. In this section, we shall see how to map assets that are derivatives of primary assets contained within the set of risk factors. We do this by approximating the derivative by primary asset flows.

Primary asset values The key to our approach is expressing the value of derivatives in terms of the values of primary asset flows. We will do this by developing expressions for the value of derivatives in terms of basic variables that represent the value of primary asset flows. We shall term these variables primary asset values (PAVs). An example of such a variable would be the value at time t of a US dollar to be delivered at (absolute) time T by the US government. We use the notation USD TTreasury (t) for this variable. We interpret these variables as equivalent ways of expressing value. Thus, just as one might express length equivalently in centimeters, inches, or cubits, one can express value equivalently in units of spot USD or forward GBP for delivery at time T by a counterparty of LIBOR credit quality. Just as we can assign a numerical value to the relative size of two units of length, we can compare any two of our units of value. For example, the spot FX rate GBP/ USD at time t, X GBP/USD (t), can be expressed as X GBP/USD (t)óGBPt (t)/USDt (t)2 Since both USDt (t) and GBPt (t) are units of value, the ratio X GBP/USD(t) is a pure unitless number. The physical analogy is inch/cmó2.54 which makes sense since both inches and centimeters are units of the same physical quantity, length. The main conceptual difference between our units of value and the usual units of length is that the relative sizes of our units of value change with time. The zero-coupon discount factor at time t for a USD cashflow with maturity qóTñt, D USD (t), can be expressed as q (t)óUSDT (t)/USDt (t) D USD q (We use upper case D here just to distinguish it typographically from other uses of d below.) Thus, the key idea is to consider different currencies as completely fungible units for expressing value rather than as incommensurable units. While it is meaningful to assign a number to the relative value of two currency units, an expression like

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USDt (t) is a pure unit of value and has no number attached to it; the value of USDt (t) is like the length of a centimeter. Example The PV as of t 0 ó30 September 1999 of a zero-coupon bond paying USD 1 000 000 at time Tó29 September 2000 by a counterparty of LIBOR credit quality would normally be written as vóUSD 1 000 000 D USD-LIBOR (t 0 ) q where D USD-LIBOR (t 0 ) denotes the zero-coupon discount factor (ZCDF) for USD by a q counterparty of LIBOR credit quality for a maturity of qóTñt 0 years at time t 0 . Using the identity D USD-LIBOR (t 0 )óUSD LIBOR (t 0 )/USD LIBOR (t 0 ) q T t we can rewrite this expression in terms of PAVs by vóUSD LIBOR (t 0 ) 1 000 000 USD LIBOR (t 0 )/USD LIBOR (t 0 ) t0 T t0 ó1 000 000 USD LIBOR (t 0 ) T Example The PV as of time t of a forward FX contract to pay USD 1 600 000 in exchange for GBP 1 000 000 at time Tó29 September 2000, is given by vó1 000 000 GBPT (t)ñ1 600 000 USDT (t) Example The Black–Scholes value for the PV on 30 September 1999 of a GBP call/USD put option with notional principal USD 1 600 000, strike 1.60 USD/GBP, expiration 29 September 2000, and volatility 20% can be expressed in terms of PAVs as vó1 000 000[GBPT (t)'(d1 )ñ1.60 USDT (t)'(d2 )] with d1,2 ó

ln[GBPT (t)/1.60 USDT (t)]ô0.02 0.20

where ' denotes the standard cumulative normal distribution.

Delta-equivalent asset ﬂows Once we have expressed the value of a derivative in terms of PAVs, we obtain approximating positions in primary assets by taking a first-order Taylor-series expansions in the PAVs. The Taylor-series expansion in terms of PAVs provides a first-order proxy in terms of primary asset flows. We term these fixed asset flows the delta-equivalent asset flows (DEAFs) of the instrument. Example Expanding the valuation function from the first example in the previous subsection in a Taylor series in the PAVs, we get dv(t)ó1 000 000 dUSDT (t)

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This says that the delta-equivalent position is a T-forward position in USD of size 1 000 000. This example is a trivial one since the valuation function is linear in PAVs. But it illustrates the interpretation of the coefficients of the first-order Taylor-series expansion as DEAFs. Primary assets have the nice property of being linear in PAVs. More interesting is the fact that many derivatives are linear in PAVs as well. Example Expanding the valuation function from the second example in the previous subsection in a Taylor series in the PAVs, we get dv(t)ó1 000 000 dGBPT (t)ñ1 600 000 dUSDT (t) This says that the delta-equivalent positions are a long T-forward position in GBP of size 1 000 000 and a short T-forward position in USD of size 1 600 000. Of course, options will generally be non-linear in PAVs. Example If the option is at the money, the DEAFs from the third example in the previous subsection are (details are given later in the chapter) dv(t)ó539 828 dGBPT (t)ñ863 725 dUSDT (t) This says that the delta-equivalent positions are T-forward positions in amounts GBP 539 828 and USD ñ863 725.

Gathering portfolio information from source systems Thus far, we have taken a bottom-up approach to the mapping problem; starting with the fundamental assets, we have been progressively expanding the range of assets that can be incorporated into the VaR calculation. At this point, we switch to a more top-down approach, which is closer to the point of view that is needed in implementation. Mathematically speaking, the VaR calculation, at least in the form presented here, is relatively trivial. However, gathering the data that is needed for this calculation can be enormously challenging. In particular, gathering the portfolio information required for the mapping process can present formidable problems. Information on the positions of a financial institution is typically held in a variety of heterogeneous systems and it is very difficult to gather this information together in a consistent way. We recommend the following approach. Each source system should be responsible for providing a description of its positions in terms of DEAFs. The VaR calculator is then responsible for converting DEAFs to exposure vectors for the fundamental assets. The advantage of this approach is that the DEAFs provide a well-defined, simple, and stable interface between the source systems and the VaR computational engine. DEAFs provide an unambiguous financial specification for the information that source systems must provide about financial instruments. They are specified in terms of basic financial-engineering abstractions rather than system-specific concepts, thus facilitating their implementation in a heterogeneous source-system environment. This specification is effectively independent of the particular selections made for the fundamental assets. Since the latter is likely to change over time, it is highly

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desirable to decouple the source–system interface from the internals of the risk system. Changes in the source–system interface definitions are expensive to implement, as they require analysis and changes in all of the source systems. It is therefore important that this interface is as stable as possible. In the presence of heterogeneous source systems, one reasonable approach to gathering the necessary information is to use a relational database. In the following two subsections, we outline a simple two-table design for this information.

The FinancialPosition table The rows in this table correspond to financial positions. These positions should be at the lowest level of granularity that is desired for reporting. One use of the table is as an index to the DEAFs, which are stored in the LinearSensitivities table described in the following subsection. For this purpose, the FinancialPosition table will need columns such as: mnemonicDescription A mnemonic identifier for the position. positionID This is the primary key for the table. It binds this table to the LinearSensitivities table entries. A second use of the FinancialPosition table is to support the selection of subportfolios. For example, it will generally be desirable to support limit setting and reporting for institutional subunits. For this purpose, one might include columns such as: book A string used to identify subportfolios within an institution. counterparty Counterparty institution for the position. For bonds, this will be the issuer. For exchange-traded instruments, this will be the exchange. currency The ISO code for currency in which presentValue is denominated, e.g. ‘USD’ or ‘GBP’. dealStatus An integer flag that describes that execution status of the position. Typically statuses might include analysis, executed, confirmed, etc. entity The legal entity within an institution that is party to the position, i.e. the internal counterpart of counterparty. instrumentType The type of instrument (e.g. swap, bond option, etc.). notional The notional value of the position. The currency units are determined by the contents of the currency column (above). Third, the FinancialPosition table would likely be used for operational purposes. Entries for such purpose might include: linearity An integer flag that describes qualitatively ‘how linear’ the position is. A simple example is described in Table 6.5. Such information might be useful in updating the table. The DEAFs for non-linear instruments will change with market conditions and therefore need to be updated frequently. The DEAFs for linear Table 6.5 Interpretation of linearity ﬂag Value

Accuracy

0 1 2

Linear sensitivities give ‘almost exact’ pricing. Linear sensitivities are not exact due to convexity. Linear sensitivities are not exact due to optionality.

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instruments remain constant between instrument lifecycle events such as resets and settlements. lastUpdated Date and time at which the FinancialPosition and LinearSensitivities table entries for this position were last updated. presentValue The present value (PV) of the position (as of the last update). The currency units for presentValue are determined by the contents of the currency column (above). source Indicates the source system for the position. unwindingPeriod An estimate of the unwinding period for the instrument in business days. (The unwinding period is the number of days it would take to complete that sale of the instrument after the decision to sell has been made.) A discussion of the use of this field is given later in this chapter. validUntil The corresponding entries in the LinearSensitivities table (as of the most recent update) remain valid on or before this date. This data could be used to support as-needed processing for LinearSensitivities table updates. An abbreviated example is shown in Table 6.6. Table 6.6 Example of a FinancialPosition table positionID instrumentType 1344 1378

FRA Swap

currency

notional

presentValue

USD USD

5 000 000 10 000 000

67 000 ñ36 000

The LinearSensitivities table This table is used to store DEAFs. Each row of the LinearSensitivities table describes a DEAF for one of the positions in the FinancialPosition table. This table might be designed as follows: amount The amount of the DEAF. For fixed cashflows, this is just the undiscounted amount of the cashflow. asset For DEAFs that correspond to currency assets, this is the ISO code for the currency, e.g. ‘USD’ or ‘GBP’. For commodities and equities, it is an analogous identifier. date The date of the DEAF. positionID This column indicates the entry in the FinancialPosition table that the DEAF corresponds to. termStructure For cash flows, this generally indicates credit quality or debt type, e.g. ‘LIBOR’ or ‘Treasury’. An example is given in Table 6.7.

Table 6.7 Example of a LinearSensitivity table positionID

Asset

termStructure

Date

Amount

1344 1344 1378

USD USD USD

LIBOR LIBOR LIBOR

15 Nov. 1999 15 Nov. 2000 18 Oct. 1999

ñ5 000 000 5 000 000 ñ35 000

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Translation tables The essential information that the source systems supply to the VaR system is DEAFs. As discussed in the previous section, it is desirable to decouple the generation of DEAFs from the specific choice of fundamental assets in the VaR system. A convenient means of achieving this decoupling is through the use of a ‘translation table’. This is used to tie the character strings used as asset and quality-credit identifiers in the source system to the strings used as fundamental-asset identifiers in the VaR system at run time. For example, suppose the base currency is USD and the asset in question is a corporate bond that pays in GBP. The source system might provide DEAFs in currency GBP and credit quality XYZ_Ltd. The first step in the calculation of the exposure vector for these DEAFs is to compute their PVs. To compute the PV, we need to associate the DEAF key GBP-XYZ_Ltd with an appropriate FX rate and discounting term structure (DTS). The translation table might specify that DEAFs with this key are assigned to the FX rate GBP and the DTS GBP-AA. The second step in the calculation of the exposure vector is assignment to appropriate volatility factors. The translation table might specify that DEAFs with this key are assigned to the FX volatility for GBP and the VTS GBP-LIBOR. The translation table might be stored in a relational database table laid out in the following way: externalPrimaryKey This key would generally be used to identify the asset in question. For example, for a currency one might use a standard currency code, e.g. USD or GBP. Similarly, one might identify an equity position by its symbol, and so forth. externalSecondaryKey This key would generally be used to specify discounting for forward delivery. For example, currency assets could be specified to be discounted according to government bond, LIBOR, and so forth. DTSPrimaryKey This key is used to identify asset prices in the asset price table as well as part of the key for the DTS. DTSSecondaryKey Secondary key for DTS. VTSPrimaryKey This key is used to identify asset volatilities in the asset price volatility table as well as part of the key for the VTS. VTSSecondaryKey Secondary key for VTS. A timely example for the need of the translation table comes with the recent introduction of the Euro. During the transition, many institutions will have deals denominated in Euros as well as DEM, FRF, and so on. While it may be convenient to use DEM pricing and discounting, it will generally be desirable to maintain just a Euro VTS. Thus, for example, it might be desirable to map a deal described as DEMLIBOR in a source system, to a DEM-Euribor DTS and a Euro-Euribor VTS. This would result in the table entries shown in Table 6.8. At this point, it may be worthwhile to point out that available discounting data will generally be richer than available volatility data. Thus, for example, we might have a AA discounting curve available, but rely on the RiskMetrics dataset for volatility information, which covers, at most, two credit-quality ratings.

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DEM LIBOR DEM LIBOR EUR Euribor

Design strategy summary There is no escaping the need to understand the semantics of instrument valuation. The challenge of doing this in a consistent manner across heterogeneous systems is probably the biggest single issue in building a VaR system. In large part, the design presented here has been driven by the requirement to make this as easy as possible. To summarize, the following analysis has to be made for each of the source systems: 1 For each instrument, write down a formula that expresses the present value in terms of PAVs. 2 Compute the sensitivities of the present value with respect to each of the PAVs. 3 If the Taylor-series expansion of the present value function in PAVs has coefficient ci with respect to the ith PAV, its first-order sensitivity to changes in the PAVs is the same as that of a position in amount ci in the asset corresponding to the PAV. Thus the sensitivities with respect to PAVs may be interpreted as DEAFs. In addition, we need to map the risk factors keys in each source system to appropriate discounting and volatility keys in the VaR system.

Covariance data Construction of volatility and correlation estimates At the heart of the variance/covariance methodology for computing VaR is a covariance matrix for the relative returns. This is generally computed from historical time series. For example, if ri (t j ), jó1, . . . , n, are the relative returns of the ith asset over nò1 consecutive days, then the variance of relative returns can be estimated by the sample variance n

m i2 ó ; r j2 (t j )/n jó1

In this expression, we assume that relative returns have zero means. The rationale for this is that sample errors for estimating the mean will often be as large as the mean itself (see Morgan and Reuters, 1996). Volatility estimates provided in the RiskMetrics data sets use a modification of this formula in which more recent data are weighted more heavily. This is intended to make the estimates more responsive to changes in volatility regimes. These estimates are updated daily. Estimation of volatilities and correlations for financial time series is a complex subject, which will not be treated in detail here. We will content ourselves with pointing out that the production of good covariance estimates is a laborious task.

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First, the raw data needs to be collected and cleaned. Second, since many of the risk factors are not directly observed, financial abstractions, such as zero-coupon discount factor curves, need to be constructed. Third, one needs to deal with a number of thorny practical issues such as missing data due to holidays and proper treatment of time-series data from different time zones. The trouble and expense of computing good covariance matrices has made it attractive to resort to outside data providers, such as RiskMetrics.

Time horizon Ideally, the time interval between the data points used to compute the covariance matrix should agree with the time horizon used in the VaR calculation. However, this is often impractical, particularly for longer time horizons. An alternative approach involves scaling the covariance matrix obtained for daily time intervals. Assuming relative returns are statistically stationary, the standard deviation of changes in portfolio value over n days is n times that over 1 day. The choice of time horizon depends on both the nature of the portfolio under consideration and the perspective of the user. To obtain a realistic estimate of potential losses in a portfolio, the time horizon should be at least on the order of the unwinding period of the portfolio. The time horizon of interest in an investment environment is generally longer than that in a trading environment.

Heterogeneous unwinding periods and liquidity risk As mentioned in the previous subsection a realistic risk assessment needs to incorporate the various unwinding periods present in a portfolio. If a position takes 5 days to liquidate, then the 1-day VaR does not fully reflect the potential loss associated with the position. One approach to incorporating liquidity effects into the VaR calculation involves associating an unwinding period with each instrument. Assuming that changes in the portfolio’s value over non-overlapping time intervals are statistically independent, the variance of the change in the portfolio’s value over the total time horizon is equal to the sum of the variances for each time-horizon interval. In this way, the VaR computation can be extended to incorporate liquidity risk. Example Consider a portfolio consisting of three securities, A, B, and C, with unwinding periods of 1, 2, and 5 days, respectively. The total variance estimate is obtained by adding a 1-day variance estimate for a portfolio containing all three securities, a 1-day variance estimate for a portfolio consisting of securities B and C, and a 3-day variance estimate for a portfolio consisting only of security C. The above procedure is, of course, a rather crude characterization of liquidity risk and does not capture the risk of a sudden loss of liquidity. Nonetheless, it may be better than nothing at all. It might be used, for example, to express a preference for instrument generally regarded as liquid for the purpose of setting limits.

Change of base currency With the system design that we have described, a change of base currency is relatively simple. First of all, since the DEAFs are defined independently of base currency,

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these remain unchanged. The only aspect of the fundamental assets that needs to be changed is the FX rates. The FX rate for the new base currency is removed from the set of risk factors and replaced by the FX rate for the old base currency. In addition, the definition of all FX rates used in the system need to be changed so that they are relative to the new base currency. Thus the rates in the FX table need to be recomputed. In addition, the volatilities of the FX rates and the correlations of FX rates with all other risk factors need to be recomputed. Fortunately, no new information is required, the necessary volatilities and correlations can be computed from the previously ones. The justification for this statement is given later in this chapter.

Information access It is useful to provide convenient access to the various pieces of information going into the VaR calculation as well as the intermediate and final results.

Input information Input information falls into three broad categories, portfolio data, current market data, and historical market data. Portfolio data In the design outline presented here, the essential portfolio data are the DEAF proxies stored in the FinancialPosition and LinearSensitivities tables. Current market data This will generally consist of spot prices and DTSs. It is convenient to have a graphical display available for the DTS, both in the form of zero-coupon discount factors as well as in the form of zero-coupon discount rates. Historical market data Generally speaking, this will consist of the historical covariance matrix. It may be more convenient to display this as a volatility vector and a correlation matrix. Since the volatility vector and correlation matrix will be quite large, some thought needs to be given as to how to display them in a reasonable manner. For example, reasonable size portions of the correlation matrix may be specified by limiting each axis to factors relevant to a single currency. In addition, it will be desirable to provide convenient access to the VTS, in order to resolve questions that may arise about the mapping of DEAFs.

Intermediate results – exposure vectors It is desirable to display the mapped exposure vectors. In the particular case of RiskMetrics, we have found it convenient to display mapped exposure vectors in the form of a matrix. The rows of this matrix correspond to asset class and maturity identifiers in the RiskMetrics data sets and the columns of this matrix correspond to currency codes in the RiskMetrics data set.

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VaR results In addition to total VaR estimates, it is useful to provide VaR estimate for individual asset classes (interest, equities, and FX) as well as for individual currencies.

Portfolio selection and reporting The VaR system must provide flexible and powerful facilities to select deals and portfolios for analysis. This is conveniently done using relational database technology. This allows the users to ‘slice and dice’ the portfolio across a number of different axes, e.g. by trader, currency, etc. It will be convenient to have persistent storage of the database queries that define these portfolios. Many institutions will want to produce a daily report of VaR broken out by sub-portfolios.

Appendix 1: Mathematical description of VaR methodologies One of the keys to the successful implementation of a VaR system is a precise financial-engineering design. There is a temptation to specify the design by providing simple examples and leave the details of the treatment of system details to the implementers. The result of this will often be inconsistencies and confusing behavior. To avoid this, it is necessary to provide an almost ‘axiomatic’ specification that provides an exact rule to handle the various contingencies that can come up. This will typically require an iterative approach, amending the specification as the implementers uncover situations that are not clearly specified. Thus, while the descriptions that follow may appear at first unnecessarily formal, experience suggests that a high level of precision in the specification pays off in the long run. The fundamental problem of VaR, based on information known at the anchor time, t 0 , is to estimate the probability distribution of the value of one’s financial position at the target date, T. In principle, one could do this in a straightforward way by coming up with a comprehensive probabilistic model of the world. In practice, it is necessary to make heroic assumptions and simplifications, the various ways in which these assumptions and simplifications are made lead to the various VaR methodologies. There is a key abstraction that is fundamental to almost all of the various VaR methodologies that have been proposed. This is that one restricts oneself to estimating the profit and loss of a given trading strategy that is due to changes in the value of a relatively small set of underlying variables termed risk factors. This assumption can be formalized by taking the risk factors to be the elements of a m-dimensional vector m t that describes the ‘instantaneous state’ of the market as it evolves over time. One then assumes that the value of one’s trading strategy at the target date expressed in base currency is given by a function vt 0,T : Rm î[t 0 , T ]R of the trajectory of m t for t é [t 0 , T ]. We term a valuation function of this form a future valuation function since it gives the value of the portfolio in the future as a function of the evolution of the risk factors between the anchor and target date.3 Note that vt 0,T is defined so that any dependence on market variables prior to the anchor date t 0 , e.g. due to resets, is assumed to be known and embedded in the valuation function vt 0,T .

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The same is true for market variables that are not captured in m t . For example, the value of a trading strategy may depend on credit spreads or implied volatilities that are not included in the set of risk factors. These may be embedded in the valuation function vt 0,T , but are then treated as known, i.e. deterministic, quantities. To compute VaR, one postulates the existence of a probability distribution kT on the evolution of m t in the interval [t 0 , T ]. Defining M[t 0,T ] •{m t : t é [t 0 , T ]}, the quantity vt 0,T (M[t 0,T ] ), where M[t 0,T ] is distributed according to kT , is then a real-valued random variable. VaR for the time horizon Tñt0 at the 1ña confidence level is defined to be the a percentile of this random variable. More generally, one would like to characterize the entire distribution of vt 0,T (M[t 0,T ] ). The problem of computing VaR thus comes down to computing the probability distribution of the random variable vt 0,T (M[t 0,T ] ). To establish a VaR methodology, we need to 1 2 3 4

Define the market-state vector m t . Establish a probabilistic model for M[t 0,T ] . Determine the parameters of the model for M[t 0,T ] based on statistical data. Establish computational procedures for obtaining the distribution of vt 0,T (M[t 0,T ] ).

In fact, most existing procedures for computing VaR have in common a stronger set of simplifying assumptions. Instead of explicitly treating a trading strategy whose positions may evolve between the anchor and target date, they simply consider the existing position as of the anchor date. We denote the valuation function as of the anchor date as a function of the risk factors by vt 0 : Rm R. We term a valuation function of this form a spot valuation function. Changes in the risk factors, which we term perturbations, are then modeled as being statistically stationary. The VaR procedure then amounts to computing the probability distribution of vt 0 (m t 0 ò*m), where *m is a stochastic perturbation.

Appendix 2: Variance/covariance methodologies In this appendix, we formalize a version of the RiskMetrics variance/covariance methodology. The RiskMetrics Technical Document sketches a number of methodological choices by example rather than a single rigid methodology. This has the advantage that the user can tailor the methodology somewhat to meet particular circumstances. When it comes time for software implementation, however, it is advantageous to formalize a precise approach. One source of potential confusion is in the description of the statistical model for portfolio value. In some places, it is modeled as normally distributed (see Morgan and Reuters, 1996, §1.2). In other places it is modeled as log-normally distributed (Morgan and Reuters, 1996, §1.1). The explanation for this apparent inconsistency is that RiskMetrics depends on an essential approximation. The most natural statistical model for changes in what we earlier termed fundamental asset prices is a log-normal model. An additional attractive feature of the log-normal model for changes in fundamental asset prices is that it implies that primary asset prices are log-normal as well. However, the difficulty with the log-normal model is that the distribution of a portfolio containing more than one asset is analytically intractable. To get around this difficulty, the RiskMetrics methodology uses an approximation in which relative changes in the primary assets are equated to changes in the log prices of these assets. Since relative returns over

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a short period of time tend to be small, the approximation is a reasonable one. For example, for a relative change of 1%, ln1.01ñln1.0óln(1.01) B0.00995. Perhaps the main danger is the possibility of conceptual confusion, the approximation means that a primary asset has a different statistical model when viewed as a market variable than when viewed as a financial position. In this appendix, we provide a formalization of these ideas that provides a consistent approach to multiple-asset portfolios.

Risk factors While in the body of this chapter we loosely referred to the fundamental assets as the risk factors, we now more precisely take the risk factors to be the logarithms of the fundamental asset prices. We denote the log price of the ith fundamental asset at time t by l i (t) for ió1, . . . , m.

Statistical model for risk factors Changes in the log asset prices between the anchor and target dates are assumed to have a jointly normal distribution with zero means. We denote the covariance matrix of this distribution by $. When *t is small, e.g. on the order of one day, the mean of this distribution is small and is approximated as being equal to zero.

Distribution of portfolio value We assume that we can write the valuation function of the portfolio as a function of the risk-factor vector l•[l 1 , . . . , l m ]T. Our procedure for computing the distribution of the portfolio is then defined by approximating the valuation function by its firstorder linear approximation in l. Thus, the first-order approximation to the change in the valuation function is given by dvB

Lv dl Ll

where

Lv Lv • Ll Ll2

Lv Ll m

We thus formalize what we termed an exposure to the ith risk factor as the partial derivative of the valuation function with respect to l i . Under this approximation, it follows that dv is normally distributed with mean zero and variance p2 ó

Lv LvT $ Ll Ll

Mapping of primary assets We now show that the above definitions agree with the mapping procedure for primary assets given in the body of this chapter. To begin with, consider, for example,

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a fundamental asset such as a zero-coupon bond in the base currency USD with face value N and maturity q. The valuation function for this bond is given by vt 0 óUSD ND USD (t 0 ) q We have Lvt 0 Lv óD USD (t 0 ) USDt 0 q USD L ln D q (t 0 ) LD q (t 0 ) óvt 0 We thus see that the required exposure to the discount factor is the present value of the bond. As another example, consider a primary asset such as a zero-coupon bond in a foreign currency with face value GBP N and maturity q. The valuation function for this bond from a USD perspective is given by vt 0 óUSD NX GBP/USD (t 0 )D qGBP (t 0 ) where X(t 0 ) is the FX rate for the foreign currency. A calculation similar to the one in the preceding paragraph shows that Lvt 0 Lvt 0 ó óvt 0 GBP L ln D q (t 0 ) L ln X GBP/USD (t 0 ) We thus see that the required exposure to the discount factor and the FX rate are both equal to the present value of the bond in the base currency. In summary, we can express the value of any primary asset for spot or forward delivery as a product of fundamental asset prices. It follows that a primary asset has an exposure to the fundamental asset prices affecting its value equal to the present value of the primary asset. This is the mathematical justification for the mapping rule presented in the body of this chapter.

Mapping of arbitrary instruments Arbitrary instruments, in particular derivatives, are treated by approximating them by positions in primary instruments. The procedure is to express the valuation function of the instrument in terms of PAVs. The approximating positions in primary instruments are then given by the coefficients of the first-order Taylor series expansion of the valuation function in the PAVs. To see this, suppose the valuation function v for a given derivative depends on m PAVs, which we denote PAVj , ió1, . . . , m. We write out the first-order expansion in PAVs: m

Lv dPAVi ió1 LPAVi

dvó ;

Now consider a portfolio consisting of positions in amounts Lv/LPAVi of the primary asset corresponding to the ith PAV. It is clear that the sensitivity of this portfolio to the PAVs is given by the right-hand side of the above equation. To summarize, the Taylor-series expansion in PAVs provides a first-order proxy in terms of primary asset flows. We term these fixed asset flows the delta-equivalent asset flows (DEAFs) of the instrument. One of the attractive features of expressing valuation functions in terms of PAVs is

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the base-currency independence of the resulting expressions. Consider, for example, the PV at time t of a GBP-denominated zero-coupon bond, paying amount N at time T. The expression in PAVs is given by vt óN GBPT (t) regardless of the base currency. In order to appreciate the base-currency independence of this expression, start from a USD perspective: vt óUSDt (t)NX GBP/USD (t)D qGBP (t) óUSDt (t)N

GBPt (t)GBPT (t) USDt (t)GBPt (t)

óN GBPT (t) As an example of a non-trivial DEAF calculation, we consider an FX option. The Black–Scholes value for the PV at time t of a GBP call/USD put option with strike ¯ GBP/USD with time qóTñt to expiry is given by X ¯ GBP/USD D USD vt óUSDt (t)[X GBP/USD (t)D qGBP (t)'(d1 )ñX (t)'(d2 )] q ¯ GBP/USD is the strike, ' denotes the cumulative probability distribution function where X for a standard normal distribution, and d1,2 ó

¯ GBP/USD )òln(D qGBP /D USD )ôp2q/2 ln(X GBP/USD /X q pq

where p is the volatility of the FX rate (Hull, 1997). Making use of the identities X GBP/USD (t)óGBPt (t)/USDt (t) D qGBP (t)GBPt (t)óGBPT (t) and (t)USDt (t)óUSDT (t) D USD q the equivalent expression in terms of PAVs is given by ¯ GBP/USD USDT '(d2 ) vt óGBPT '(d1 )ñX with d1,2 ó

¯ GBP/USD )ôp2q/2 ln[GBPT (t)/USDT (t)]ñln(X pq

Some calculation then gives the first-order Taylor-series expansion ¯ GBP/USD '(d2 )dUSDT (t) dvt ó'(d1 )dGBPT (t)ñX This says that the delta-equivalent positions are T-forward positions in amounts ¯ GBP/USD '(d1 ). GBP '(d1 ) and USD X

Change of base currency A key and remarkable fact is that the log-normal model for FX risk factors is invariant under a change of base currency. That is, if one models the FX rates relative to a

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given base currency as jointly log normal, the FX rates relative to any other currency induced by arbitrage relations are jointly log normal as well. Moreover, the covariance matrix for rates relative to a new base currency is determined from the covariance matrix for the old base currency. These two facts make a change of base currency a painless operation. These nice properties hinge on the choice of log FX rates as risk factors. For example, suppose the original base currency is USD so that the FX risk factors consist of log FX rates relative to USD. The statistical model for risk factors is that changes in log asset prices have a joint normal distribution. If we were to change base currency to, say GBP, then the new risk factors would be log FX rates relative to GBP. Thus, for example, we would have to change from a risk factor of ln X GBP/USD to ln X USD/GBP . But, using the PAV notation introduced earlier, ln X USD/GBP óln

USD GBP

ó ñln

GBP USD

ó ñln X GBP/USD Thus the risk factor from a GBP respective is just a scalar multiple of the risk factor from a USD perspective. For a third currency, say JPY, we have ln X JPY/GBP óln óln

JPY GBP JPY GBP ñln USD USD

óln X JPY/USD ñln X GBP/USD Thus the log FX rate for JPY relative to GBP is a linear combination of the log FX rates for JPY and GBP relative to USD. We see that the risk factors with GBP as base currency are just linear combinations of the risk factors with USD as base currency. Standard results for the multivariate normal distribution show that linear combination of zero-mean jointly normal random variables are also zero-mean and jointly normal. Moreover, the covariance matrix for the new variables can be expressed in terms of the covariance matrix for the old variables. We refer the reader to Morgan and Reuters (1996, §8.4) for the details of these calculations.

Appendix 3: Remarks on RiskMetrics The methodology that we described in this chapter agrees quite closely with that presented in RiskMetrics, although we deviate from it on some points of details. In this appendix, we discuss the motivations for some of these deviations.

Mapping of non-linear instruments In the previous appendix we described a general approach to treating instruments whose value depends non-linearly on the primary assets. This approach involved computing a first-order Taylor series expansion in what we termed PAVs. The

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coefficients of this expansion gave positions in primary assets that comprised a firstorder proxy portfolio to the instrument in question. The RiskMetrics Technical Document (Morgan and Reuters, 1996) describes a similar approach to mapping non-linear FX and/or interest-rate derivatives. The main difference is that the firstorder Taylor series expansion of the PV function is taken with respect to FX rates and zero-coupon discount factors (ZCDF). Using this expansion, it is possible to construct a portfolio of proxy instruments whose risk is, to first order, equivalent. This approach, termed the delta approximation, is illustrated by a number of relatively simple examples in Morgan and Reuters (1996). While the approach described in Morgan and Reuters (1996) is workable, it has a number of significant drawbacks relative to the DEAF approach: 1 As will be seen below, the proxy instrument constructed to reflect risk with respect to ZCDFs in foreign currencies does not correspond to a commonly traded asset; it is effectively a zero-coupon bond in a foreign currency whose FX risk has been removed. In contrast, the proxy instrument for the DEAF approach, a fixed cashflow, is both simple and natural. 2 Different proxy types are used for FX and interest-rate risk. In contrast, the DEAF approach captures both FX and interest-rate risk with a single proxy type. 3 The value of some primary instruments is non-linear in the base variables, with the proxy-position depending on current market data. This means they need to be recomputed whenever the market data changes. In contrast, primary instruments are linear in the DEAF approach, so the proxy positions are independent of current market data. 4 The expansion is base-currency dependent, and thus needs to be recomputed every time the base currency is changed. In contrast, proxies in the DEAF approach are base-currency independent. In general, when an example of first-order deal proxies is given in Morgan and Reuters (1996), the basic variable is taken to be the market price. This is stated in Morgan and Reuters (1996, table 6.3), where the underlying market variables are stated to be FX rates, bond prices, and stock prices. For example, in Morgan and Reuters (1996, §1.2.2.1), the return on a DEM put is written as dDEM/USD , where rDEM/USD is the return on the DEM/USD exchange rate and d is the delta for the option.4 Consider the case of a GBP-denominated zero-coupon bond with a maturity of q years. For a USD-based investor, the PV of this bond is given by vt 0 óUSD NX GBP/USD (t 0 )D qGBP (t 0 ) where X GBP/USD is the (unitless) GBP/USD exchange rate, D qGBP (t 0 ) denotes the q-year ZCDF for GBP, and N is the principal amount of the bond. We see that the PV is a non-linear function of the GBP/USD exchange rate and the ZCDF for GBP. (While the valuation function is a linear function of the risk factors individually, it is quadratic in the set of risk factors as a whole.) Expanding the valuation function in a first-order Taylor series in these variables, we get dvt ó

Lv Lv dD qGBP ò dX GBP/USD LD qGBP LX GBP/USD

óUSD N(X GBP/USD dD qGBP òD qGBP dX GBP/USD )

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This says that the delta-equivalent position is a spot GBP position of ND TGBP and a q-year GBP N ZCDF position with an exchange rate locked at X GBP/USD . Note that these positions are precisely what one would obtain by following the standard mapping procedure for foreign cashflows in RiskMetrics (see example 2 in Morgan and Reuters, 1996). This non-linearity contrasts with the DEAF formulation, where we have seen that the value of this instrument is a linear function of the PAV GBPT (T) and the delta-equivalent position is just a fixed cashflow of GBP N at time T.

Commodity prices Our treatment of commodities is somewhat different from the standard RiskMetrics approach, although it should generally give similar results. The reason for this is that the standard RiskMetrics approach is essentially ‘dollar-centric’, the RiskMetrics data sets give volatilities for forward commodities in forward dollars. The problem with this is that it conflates the commodity term structure with the dollar term structure. For example, to express the value of a forward copper position in JPY, we convert price in forward dollars to a price in spot dollars, using the USD yield curve, and then to JPY, based on the JPY/USD spot rate. As a result, a simple forward position becomes enmeshed with USD interest rates without good reason. To carry out our program of treating commodities in the same way as foreign currencies, we need to convert the volatility and correlation data for commodities in the RiskMetrics data sets so that they reflect future commodity prices expressed in terms of spot commodity instead of future dollars. Fortunately, the volatilities and correlations for the commodity discount factors can be derived from those provided in the RiskMetrics data sets. The flavor of the calculations is similar to that for the change of base currency described in the previous appendix. This transformation would be done when the RiskMetrics files are read in.

Appendix 4: Valuation-date issues We noted above that most VaR methodologies do not explicitly deal with the entire evolution of the risk factors and the valuation function between the anchor date and target date. Rather they just evaluate the valuation function as of the anchor date at perturbed values of the risk factors for the anchor date. This simplification, while done for strong practical reasons, can lead to surprising results.

Need for future valuation functions Consider, for example, a cashflow in amount USD N in the base currency payable one year after the anchor date. We would write the valuation function with respect to the USD base currency in terms of our risk factors as vt 0 óUSD ND1 (t 0 ) where D1(t 0 ) denotes the 1-year discount factor at time t 0 . This equation was the starting point for the analysis in the example on page 191, where we calculated VaR for a 1-day time horizon. Scaling this example to a 1-year time horizon, as discussed on page 201 would give a VaR approximately 16 times as large. But this is crazy! The value of the bond in one year is N, so there is no risk at all and VaR should be equal to zero. What went wrong? The problem is that we based

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our calculation on the valuation function as of the anchor date. But at the target date, the valuation function is given simply by vT óND0 (T ) óN Thus the formula that gives the value of the instrument in terms of risk factors changes over time. This is due to the fact that our risk factors are fixed-maturity assets. (Changing to risk factors with fixed-date assets is not a good solution to this problem as these assets will not be statistically stationary, indeed they will drift to a known value.) The first idea that comes to mind to fix this problem is to use the spot valuation function as of the target date instead of the anchor date. While this would fix the problem for our 1-year cashflow, it causes other problems. For example, consider an FRA that resets between the anchor and target dates and pays after the target date. The value of this instrument as of the target date will depend on the risk-factor vector between the anchor and target date and this dependence will not be captured by the spot valuation function at the target date. For another example, consider a 6-month cashflow. In most systems, advancing the valuation date by a year and computing the value of this cashflow would simply return zero. It therefore appears that in order to properly handle these cases, the entire trajectory of the risk factors and the portfolio valuation function needs to be taken into account, i.e. we need to work with future valuation functions are described in Appendix 1.

Path dependency In some instances, the future valuation function will only depend on the marketstate vector at the target time T. When we can write the future valuation function as a function of mT alone, we say that the future valuation function is path-independent. For example, consider a zero-coupon bond maturing after time T. The value of the bond at time T will depend solely on the term structure as of time T and is thus path independent. It should be recognized that many instruments that are not commonly thought of as being path dependent are path dependent in the context of future valuation. For example, consider a standard reset-up-front, pay-in-arrears swap that resets at time t r and pays at time t p , with t 0 \t r \T\t p . To perform future valuation at time T, we need to know the term-structure at time T, to discount the cashflow, as well as the term-structure as of time t r , to fix the reset. Thus, we see that the swap is path dependent. Similar reasoning shows that an option expiring at time t e and paying at time t p , with t 0 \t e \T\t p , is, possibly strongly, path dependent.

Portfolio evolution Portfolios evolve over time. There is a natural evolution of the portfolio due to events such as coupon and principal payments and the expiration and settlement of forward and option contracts. To accurately characterize the economic value of a portfolio at the target date, it is necessary to track cashflows that are received between the anchor and target dates and to make some reasonable assumptions as to how these received cashflows are reinvested. Probably the most expedient approach to dealing with the effects of received cashflows is to incorporate a reinvestment assumption into the VaR methodology.

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There are two obvious choices; a cashflow is reinvested in a cash account at the riskfree short rate or the coupon is reinvested in a zero-coupon bond maturing at time T, with the former choice being more natural since it is independent of the time horizon. If we make the latter choice, we need to know the term structure at time t p ; if we make the former choice, we need to know the short rate over the interval [t p , T ].

Implementation considerations Path-dependence and received cashflows in the context of future valuation can create difficulties in the implementation of VaR systems. The reason is that most instrument implementations simply do not support future valuation in the sense described above, distinguishing between anchor and target dates. Rather, all that is supported is a spot valuation function, with dependence on past market data, e.g. resets, embedded in the valuation function through a separate process. Thus the usual practice is to generate samples of the instantaneous market data vector as of time T, mT , and evaluate the instantaneous valuation function at time t 0 or T, i.e. vt 0 (m T ) or vT (mT ). It is easy to think of cases where this practice gives seriously erroneous results. An obvious problem with using vt 0 (m T ) as a proxy for a future valuation function is that it will erroneously show market risk for a fixed cashflow payable at time T. A better choice is probably vT (mT ), but this will return a PV of zero for a cashflow payable between t 0 and T. In addition, one needs to be careful about resets. For example, suppose there is a reset occurring at time t r with t 0 \t r \T. Since the spot valuation function only has market data as of time T available to it, it cannot determine the correct value of the reset, as it depends on past market data when viewed from time T. (In normal operation, the value of this reset would have been previously set by a separate process.) How the valuation function treats this missing reset is, of course, implementation dependent. For example, it may throw an error message, which would be the desired behavior in a trading environment or during a revaluation process, but is not very helpful in a simulation. Even worse, it might just fail silently, setting the missing reset to 0 or some other arbitrary value. To solve this problem completely, instrument implementations would have to support future valuation function with distinct anchor and target dates. Such a function would potentially need to access market-data values for times between the anchor and target date. As of today, valuation functions supporting these semantics are not generally available. Even if they were, a rigorous extension of even the parametric VaR methodology that would properly account for the intermediate evolutions of variables would be quite involved. We can, however, recommend a reasonably simple approximation that will at least capture the gross effects. We will just sketch the idea at a conceptual level. First of all, we construct DEAF proxies for all instruments based on the spot valuation function as of the anchor date. We then modify the mapping procedure as follows: 1 Exposures are numerically equal to the forward value of the asset at the target date rather than the present value at the anchor date. Forward values of assets for delivery prior to the target date are computed by assuming that the asset is reinvested until the target date at the forward price as of the anchor date. 2 Asset flows between the anchor and target dates do not result in discount-factor risk. Asset flows after the target date are mapped to discount factors with maturities equal to the difference between the delivery and target date.

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Glossary of terms Absolute Return: The change in value of an instrument or portfolio over the time horizon. Analytic VaR: Any VaR methodology in which the distribution of portfolio value is approximated by an analytic expression. Variance/covariance VaR is a special case. Anchor Date: Roughly speaking, ‘today’. More formally, the date up until which market conditions are known. DEAFs: Delta-equivalent asset flows. Primary asset flows that serve as proxies for derivative instruments in a VaR calculation. DTS: Discounting term structure. Data structure used for discounting of future asset flows relative to their value today. Exposure: The present value of the equivalent position in a fundamental asset. Fundamental Asset: A fundamental market factor in the form of the market price for a traded asset. Future Valuation Function: A function that gives the value of a financial instrument at the target date in terms of the evolution of the risk factors between the anchor and target date. FX: Foreign exchange. Historical VaR: A value-at-risk methodology in which the statistical model for the risk factors is directly tied to the historical time series of changes in these variables. Maturity: The interval of time between the anchor date and the delivery of a given cashflow, option expiration, or other instrument lifecycle event. Monte Carlo VaR: A value-at-risk methodology in which the distribution of portfolio values is estimated by drawing samples from the probabilistic model for the risk factors and constructing a histogram of the resulting portfolio values. Native Currency: The usual currency in which the price of a given asset is quoted. PAV: Primary asset value. A variable that denotes the value of an asset flow for spot or future delivery by a counterparty of a given credit quality. PV: Present value. The value of a given asset as of the anchor date. Relative Return: The change in value of an asset over the time horizon divided by its value at the anchor date. Risk Factors: A set of market variables that determine the value of a financial portfolio. In most VaR methodologies, the starting point is a probabilistic model for the evolution of these factors. Spot Valuation Function: A function that gives the value of a financial instrument at a given date in terms of the evolution of the risk factor vector for that date. Target Date Date in the future on which we are assessing possible changes in portfolio value. Time Horizon: The interval of time between the anchor and target dates. VaR: Value at risk. A given percentile point in the profit and loss distribution between the anchor and target date. Variance/Covariance VaR: A value-at-risk methodology in which the risk factors are modeled as jointly normal and the portfolio value is modeled as a linear combination of the risk factors and hence is normally distributed. VTS: Volatility term structure. Data structure used for assigning spot and future asset flows to exposure vectors. ZCDF: Zero-coupon discount factor. The ratio of the value of a given asset for future delivery with a given maturity by a counterparty of a given credit quality to the spot value of the asset.

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Acknowledgments This chapter largely reflects experience gained in building the Opus Value at Risk system at Renaissance Software and many of the ideas described therein are due to the leader of that project, Jim Lewis. The author would like to thank Oleg Zakharov and the editors of this book for their helpful comments on preliminary drafts of this chapter.

Notes 1

RiskMetrics is a registered trademark of J. P. Morgan. We use the term spot rate to mean for exchange as of today. Quoted spot rates typically reflect a settlement lag, e.g. for exchange two days from today. There is a small adjustment between the two to account for differences in the short-term interest rates in the two currencies. 3 Our initial impulse was to use the term forward valuation function, but this has an established and different meaning. The forward value of a portfolio, i.e. the value of the portfolio for forward delivery in terms of forward currency, is a deterministic quantity that is determined by an arbitrage relationship. In contrast, the future value is a stochastic quantity viewed from time t 0 . 4 The RiskMetrics Technical Document generally uses the notation r to indicate log returns. However, the words in Morgan and Reuters (1996, §1.2.2.1) seem to indicate that the variables are the prices themselves, not log prices. 2

References Hull, J. C. (1997) Options, Futures, and other Derivatives, Prentice-Hall, Englewood Cliffs, NJ, third edition. Morgan, J. P. and Reuters (1996) RiskMetricsT M-Technical Document, Morgan Guaranty Trust Company, New York, fourth edition.

7

Additional risks in fixed-income markets TERI L. GESKE

Introduction Over the past ten years, risk management and valuation techniques in fixed-income markets have evolved from the use of static, somewhat naı¨ve concepts such as Macaulay’s duration and nominal spreads to option-adjusted values such as effective duration, effective convexity, partial or ‘key rate’ durations and option-adjusted spreads (OAS). This reflects both the increased familiarity with these more sophisticated measures and the now widespread availability of the analytical tools required to compute them, including option models and Monte Carlo analyses for securities with path-dependent options (such as mortgage-backed securities with embedded prepayment options). However, although these option-adjusted measures are more robust, they focus exclusively on a security’s or portfolio’s interest rate sensitivity. While an adverse change in interest rates is the dominant risk factor in this market, there are other sources of risk which can have a material impact on the value of fixed-income securities. Those securities that offer a premium above risk-free Treasury rates do so as compensation either for some type of credit risk (i.e. that the issuer will be downgraded or actually default), or ‘model risk’ (i.e. the risk that valuations may vary because future cash flows change in ways that models cannot predict). We have seen that gains from a favorable interest rate move can be more than offset by a change in credit spreads and revised prepayment estimates can significantly alter previous estimates of a mortgage portfolio’s interest rate sensitivity. The presence of these additional risks highlights the need for measures that explain and quantify a bond’s or portfolio’s sensitivity to changes in these variables. This chapter discusses two such measures: spread duration and prepayment uncertainty. We describe how these measures may be computed, provide some historical perspective on changes in these risk factors, and compare spread duration and prepayment uncertainty to interest rate risk measures for different security types. A risk manager can use these measures to evaluate the firm’s exposure to changes in credit spreads and uncertainty associated with prepayments in the mortgage-backed securities market. Since spread duration and prepayment uncertainty may be calculated both for individual securities and at the portfolio level, they may be used to establish limits with respect to both individual positions and the firm’s overall

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exposure to these important sources of risk. Both measures are summarized below: Ω Spread Duration – A measure of a bond’s (or portfolio’s) credit spread risk, i.e. its sensitivity to a change in the premium over risk-free Treasury rates demanded by investors in a particular segment of the market, where the premium is expressed in terms of an option-adjusted spread (OAS). The impact of a change in spreads is an important source of risk for all dollar-denominated fixed-income securities other than US Treasuries, and will become increasingly important in European debt markets if the corporate bond market grows as anticipated as a result of EMU. To calculate spread duration, a bond’s OAS (as implied by its current price) is increased and decreased by a specified amount; two new prices are computed based on these new OASs, holding the current term structure of interest rates and volatilities constant. The bond’s spread duration is the average percentage change in its price, relative to its current price, given the higher and lower OASs (scaled to a 100 bp shift in OAS). Spread duration allows risk managers to quantify and differentiate a portfolio’s sensitivity to changes in the risk premia demanded across market segments such as investment grade and high yield corporates, commercial mortgage-backed and asset-backed securities and so on. Ω Prepayment Uncertainty – A measure of the sensitivity of a security’s price to a change in the forecasted rate of future prepayments. This concept is primarily applicable to mortgage-backed securities,1 where homeowner prepayments due to refinancing incentives and other conditions are difficult to predict. To calculate this measure, alternative sets of future cash flows for a security are generated by adjusting the current prepayment forecast upward and downward by some percentage, e.g. 10% (for mortgage-backed securities, prepayment rates may be expressed using the ‘PSA’ convention, or as SMMs, single monthly mortality rates, or in terms of CPR, conditional/constant prepayment rates). Holding all other things constant (including the initial term structure of interest rates, volatility inputs and the security’s OAS), two new prices are computed using the slower and faster versions of the base case prepayment forecasts. The average percentage change in price resulting from the alternative prepayment forecasts versus the current price is the measure of prepayment uncertainty; the more variable a security’s cash flows under the alternative prepay speeds versus the current forecast, the greater its prepayment uncertainty and therefore its ‘model’ risk. An alternative approach to deriving a prepayment uncertainty measure is to evaluate the sensitivity of effective duration to a change in prepayment forecasts. This approach provides additional information to the risk manager and may be used in place of or to complement the ‘price sensitivity’ form of prepayment uncertainty. Either method helps to focus awareness on the fact that while mortgage-backed security valuations capture the impact of prepayment variations under different interest rate scenarios (typically via some type of Monte Carlo simulation), these valuations are subject to error because of the uncertainty of any prepayment forecast. We now discuss these risk measures in detail, beginning with spread duration.

Spread duration As summarized above, spread duration describes the sensitivity of a bond’s price to

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a change in its option-adjusted spread (OAS). For those who may be unfamiliar with the concept of option-adjusted spreads, we give a brief definition here. In a nutshell, OAS is the constant spread (in basis points) which, when layered onto the Treasury spot curve, equates the present value of a fixed-income security’s expected future cash flows adjusted to reflect the exercise of any embedded options (calls, prepayments, interest rate caps and so on) to its market price (see Figure 7.1).

Figure 7.1 Treasury curve and OAS.

To solve for a security’s OAS, we invoke the appropriate option model (e.g. a binomial or trinomial tree or finite difference algorithm for callable/puttable corporate bonds, or some type of Monte Carlo-simulation for mortgage-backed and other path-dependent securities) to generate expected future cash flows under interest rate uncertainty and iteratively search for the constant spread which, when layered onto the Treasury spot rates, causes the present value of those option-adjusted cash flows, discounted at the Treasury spot rates plus the OAS, to equal the market price of the security.

OAS versus nominal spread For bonds with embedded options (e.g. call options, prepayments, embedded rate caps, and so on), the difference between the option-adjusted spread and nominal spread (the difference between the bond’s yield-to-maturity or yield-to-call and the yield on a specific Treasury) can be substantial. Compared to nominal spread, OAS is a superior measure of a security’s risk premium for a number of reasons: Ω OAS analysis incorporates the potential variation in the present value of a bond’s expected future cash flows due to option exercise or changes in prepayment speeds. Nominal spread is based on a single cash flow forecast and therefore cannot accommodate the impact of interest rate uncertainty on expected future cash flows. Ω OAS is measured relative to the entire spot curve, whereas nominal spread is measured relative to a single point on the Treasury curve. Even for option-free securities, this is a misleading indication of expected return relative to a portfolio of risk-free Treasuries offering the same cash flows, particularly in a steep yield curve environment. Ω Nominal spread is a comparison to a single average life-matched Treasury, but if a security’s average life is uncertain its nominal spread can change dramatically (especially if the yield curve is steeply sloped) if a small change in the Treasury curve causes the bond to ‘cross over’ and trade to its final maturity date instead

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of a call date, or vice versa. OAS is computed relative to the entire set of Treasury spot rates and uses expected cash flows which may fluctuate as interest rates change, thus taking into account the fact that a security’s average life can change when interest rates shift and a call option is exercised or prepayments speed up or slow down. Ω Nominal spread assumes all cash flows are discounted at a single yield, which ultimately implies that cash flows from different risk-free securities which are paid in the same period (e.g. Year 1 or Year 2) will be discounted at different rates, simply because the securities have different final maturities, and so on. Although OAS is clearly superior to nominal spread in determining relative value, there are a number of problems associated with using OAS. For example, OAS calculations can vary from one model to another due to differences in volatility parameters, prepayment forecasts, etc. Nonetheless, OAS is now a commonly accepted valuation tool, particularly when comparing the relative value of fixedincome securities across different markets.

Spread risk – a ‘real-world’ lesson In the Fall of 1998, fixed-income securities markets experienced unprecedented volatility in response to the liquidity crisis (real or perceived) and ‘flight to quality’ that resulted from the turmoil in Russian and Brazilian debt markets and from problems associated with the ‘meltdown’ of the Long Term Capital Management hedge fund. As Treasury prices rallied, sending yields to historic lows, spreads on corporate bonds, mortgage-backed securities and asset-backed securities all widened in the course of a few days by more than the sum of spread changes that would normally occur over a number of months or even years. Many ‘post-mortem’ analyses described the magnitude of the change as a ‘5 standard deviation move’, and one market participant noted that ‘spreads widened more than anyone’s risk models predicted and meeting margin calls sucked up liquidity’ (Bond Week, 1998). Spreads on commercial mortgage-backed securities widened to the point where liquidity disappeared completely and no price quotes could be obtained. While there are undoubtedly many lessons to be learned from this experience, certainly one is that while a firm’s interest rate risk may be adequately hedged, spread risk can overwhelm interest rate risk when markets are in turmoil.

Spread Duration/Spread Risk Since investors demand a risk premium to hold securities other than risk-free (i.e. free of credit risk, liquidity risk, prepayment model risk, etc.) debt, and that risk premium is not constant over time, spread duration is an important measure to include in the risk management process. Spreads can change in response to beliefs about the general health of the domestic economy, to forecasts about particular sectors (e.g. if interest rates rise, spreads in the finance sector may increase due to concerns about the profitability of the banking industry), to political events (particularly in emerging markets) that affect liquidity, and so on. Often, investors are just as concerned with the magnitude and direction of changes in spreads as with changes in interest rates, and spread duration allows the risk manager to quantify the impact of changes in option-adjusted sector spreads across a variety of fixed-income investment alternatives.

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Computing spread duration To calculate spread duration, we increase and decrease a security’s OAS by some amount and, holding Treasury (spot) rates and volatilities at current levels, compute two new prices based on these new spreads: POASñ100 bps ñPOASò100 bps î100 2îPBase case OAS Spread duration is the average percentage change in the security’s price given the lower and higher OASs. It allows us to quickly translate a basis point change in spreads to a percentage change in price, and by extension, a dollar value change in a position. For example, the impact of a 20 bp shift in OAS on the price of a bond with a spread duration of 4.37 is estimated by (0.20î4.37)ó0.874%. Therefore, a $50 million position in this security would decline by $43.7K ($50 millionî0.874) if spreads widened by 20 bps.

Spread changes – historical data Since the correlation between changes in credit spreads and changes in interest rates is unstable (the correlation even changes sign over time), it is important to measure a portfolio’s or an institution’s exposure to spread risk independent of an assessment of interest rate risk. For example, a portfolio of Treasuries with an effective duration of 5.0 has no spread risk, but does have interest rate risk. A portfolio of corporate bonds with an effective duration of 3.0 has less interest rate risk than the Treasury portfolio, but if adverse moves in interest rates and spreads occur simultaneously, the corporate portfolio may be a greater source of risk than the Treasury portfolio. Spread risk affects corporate bonds, mortgage-backed securities, asset-backed securities, municipal bonds and so on, and a change in spreads in one segment of the market may not carry over to other areas, as the fundamentals and technicals that affect each of these markets are typically unrelated. Nonetheless, we have seen that in times of extreme uncertainty, correlations across markets can converge rapidly to ò1.0, eliminating the benefits that might otherwise be gained by diversifying spread risk across different market sectors. What magnitude of spread changes can one reasonably expect over a given period? Table 7.1 shows the average and standard deviations of option-adjusted spreads for various sectors over the six-year period, August 1992 to July 1998. In parentheses, we show the standard deviations computed for a slightly different six-year period, November 1992 to October 1998 (note: statistics were computed from weekly observations). The reason the two standard deviations are so different is that the values in parentheses include October 1998 data and therefore reflect the spread volatility experienced during the market crisis discussed above. Of course, six years is a long time and statistics can change significantly depending upon the observations used to compute them, so we also show the average and standard deviation of optionadjusted spreads measured over a one-year period, August 1997 to July 1998, with the standard deviation computed over the one year period November 1997 to October 1998 shown in parentheses. When evaluating the importance of spread risk, it is important to stress-test a portfolio under scenarios that reflect possible market conditions. Although the October 1998 experience may certainly be viewed as a rare event, if we use the oneyear data set excluding October 1998 to forecast future spread changes (based on

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The Professional’s Handbook of Financial Risk Management Table 7.1 Average and standard deviations of option-adjusted spreads Option-Adjusted Spread (OAS) six years of data Sector/quality Industrial – AA Industrial – A Industrial – BAA Utility – AA Utility – A Utility – BAA Finance – AA Finance – A Finance – BAA Mortgage pass-throughs (30-yr FNMA)

8/92–7/98 Average 47 67 111 48 72 95 56 74 99 73 Average:

(11/92–10/98) Standard dev. 6.2 8.4 21.2 5.4 14.8 15.1 6.5 11.1 16.1

(8.8) (11.8) (22.7) (8.1) (14.4) (15.7) (11.6) (16.1) (19.7)

16.4 (17.1) 12.1 (14.6)

Option-Adjusted Spread (OAS) one year of data 8/97–7/98 Average 49 68 93 47 63 87 59 72 96 54 Average:

(11/97–10/98) Standard dev. 4.4 5.5 5.8 2.9 5.1 5.4 4.7 5.1 9.1

(13.8) (18.2) (26.9) (15.0) (15.2) (17.8) (21.3) (27.2) (28.9)

11.2 (25.2) 5.9 (20.9)

Note: OASs for corporate sectors are based on securities with an effective duration of approximately 5.0.

standard deviation), we could potentially underestimate a portfolio’s spread risk by more than threefold (the average standard deviation based on one year of data including October 1998 is 3.5 times the average standard deviation based on one year of data excluding October 1998). When stress-testing a portfolio, it would be a good idea to combine spread changes with interest rate shocks – for example, what would happen if interest rates rise by X bps while corporate spreads widen by Y bps and mortgage spreads widen by Z bps? If we have computed the portfolio’s effective duration and the spread duration of the corporate and mortgage components of the portfolio, we can easily estimate the impact of this scenario: [(Effective durationOverall îX ) ò(Spread durationCorporates îY )ò(Spread durationMortgages îZ )].

Spread risk versus interest rate risk How, if at all, does spread duration relate to the more familiar effective duration value that describes a bond’s or portfolio’s sensitivity to changes in interest rates? In this section, we attempt to provide some intuition for how spread risk compares to interest rate risk for different types of securities. In attempting to understand spread duration, it is necessary to think about how a change in spreads affects both the present value of a security’s future cash flows and the amount and timing of the cash flows themselves. In this respect, an interesting contrast between corporate bonds and mortgage-backed securities may be observed when analyzing spread duration. Corporate bonds A change in the OAS of a callable (or puttable) corporate bond directly affects the cash flows an investor expects to receive, since the corporate issuer (who is long the call option) will decide whether or not to call the bond on the basis of its price in the secondary market. If a security’s OAS narrows sufficiently, its market price will rise

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above its call price, causing the issuer to exercise the call option. Likewise, the investor who holds a puttable bond will choose to exercise the put option if secondary market spreads widen sufficiently to cause the bond’s price to drop below the put price (typically par). Since changing the bond’s OAS by X basis points has the same impact on its price as shifting the underlying Treasury yields by an equal number of basis points, the spread duration for a fixed rate corporate bond is actually equal to its effective duration.2 Therefore, either spread duration or effective duration can be used to estimate the impact of a change in OAS on a corporate bond’s price. The same applies to a portfolio of corporate bonds, so if a risk management system captures the effective (option-adjusted) duration of a corporate bond inventory, there is no need to separately compute the spread duration of these holdings. (Note that Macaulay’s, a.k.a. ‘Modified’, duration is not an acceptable proxy for the spread risk of corporate bonds with embedded options, for the same reasons it fails to adequately describe the interest rate sensitivity of these securities.) Floating rate securities Although we can see that for fixed rate corporate bonds, spread duration and effective duration are the same, for floating rate notes (FRNs), this is not the case. The effective duration of an (uncapped) FRN is roughly equal to the amount of time to its next reset date. For example, an FRN with a monthly reset would have an effective duration of approximately 0.08, indicating the security has very little interest rate sensitivity. However, since a change in secondary spreads does not cause the FRN’s coupon rate to change; a FRN can have substantial spread risk. This is due to the impact of a change in secondary spreads on the value of the remaining coupon payments – if spreads widen, the FRN’s coupon will be below the level now demanded by investors, so the present value of the remaining coupon payments will decline. The greater the time to maturity, the longer the series of below-market coupon payments paid to the investor and the greater the decline in the value of the FRN. Therefore, the spread duration of an FRN is related to its time to maturity; e.g. an FRN maturing in two years has a lower spread duration than an FRN maturing in ten years. Mortgage passthroughs The spread duration of a mortgage-backed security is less predictable than for a corporate bond and is not necessarily related to its effective duration. We observed that for corporate bonds, a change in secondary market spreads affects the cash flows to the bondholder because of the effect on the exercise of a call or put option. Can we make the same claim for mortgage-backed securities? In other words, can we predict whether or not a change in secondary market mortgage spreads will affect a homeowner’s prepayment behavior, thereby altering the expected future cash flows to the holder of a mortgage-backed security? What implications does this have for the spread risk involved in holding these securities? Let us consider two separate possibilities, i.e. that homeowners’ prepayment decisions are not affected by changes in secondary spreads for MBS, and conversely, that spread changes do affect homeowners’ prepayments. If we assume that a homeowner’s incentive to refinance is not affected by changes in spreads, we would expect the spread duration of a mortgage passthrough to resemble its Macaulay’s duration, with good reason. Recall that Macaulay’s duration tells us the percentage

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change in a bond’s price given a change in yield, assuming no change in cash flows. Spread duration is calculated by discounting a security’s projected cash flows using a new OAS, which is roughly analogous to changing its yield. Therefore, if we assume that a change in spreads has no effect on a mortgage-backed security’s expected cash flows, its spread duration should be close to its Macaulay’s duration.3 However, it may be more appropriate to assume that a change in OAS affects not only the discount rate used to compute the present value of expected future cash flows from a mortgage pool, but also the refinancing incentive faced by homeowners, thereby changing the amount and timing of the cash flows themselves. As discussed in the next section on prepayment uncertainty, the refinancing incentive is a key factor in prepayment modeling that can have a significant affect on the valuation and assessment of risk of mortgage-backed securities. If we assume that changes in spreads do affect refinancings, the spread duration of a mortgage passthrough would be unrelated to its Macaulay’s duration, and unrelated to its effective (optionadjusted) duration as well. Adjustable rate mortgage pools (ARMs) are similar to FRNs in that changes in spreads, unlike interest rate shifts, do not affect the calculation of the ARM’s coupon rate and thus would not impact the likelihood of encountering any embedded reset or lifetime caps. Therefore, an ARM’s spread duration may bear little resemblance to its effective duration, which reflects the interest rate risk of the security that is largely due to the embedded rate caps. CMOs and other structured securities The spread duration of a CMO depends upon the deal structure and the tranche’s payment seniority within the deal. If we assume that a widening (narrowing) of spreads causes prepayments to decline (increase), a CMO with extension (contraction) risk could have substantial spread risk. Also, it is important to remember that as interest rates change, changes in CMO spreads may be different than changes collateral spreads. For example, when interest rates fall, spreads on well-protected PACs may tighten as spreads on ‘cuspy’ collateral widen, if investors trade out of the more prepayment-sensitive passthroughs into structured securities with less contraction risk. Therefore, when stress-testing a portfolio of mortgage-backed securities, it is important to include simulation scenarios that combine changes in interest rates with changes in spreads that differentiate by collateral type (premium versus discount) and by CMO tranche type (e.g. stable PACs and VADMs versus inverse floaters, IOs and so on). PSA-linked index-amortizing notes (IANs) have enjoyed some degree of popularity among some portfolio managers as a way to obtain MBS-like yields without actually increasing exposure to mortgage-backed securities. Since the principal amortization rate on these securities is linked to the prepayment speed on a reference pool of mortgage collateral, one might think that the spread duration of an IAN would be similar to the spread duration of the collateral pool. However, it is possible that spreads on these structured notes could widen for reasons that do not affect the market for mortgage-backed securities (such as increased regulatory scrutiny of the structured note market). Therefore, when stress-testing a portfolio that includes both mortgage-backed securities and IANs, it would be appropriate to simulate different changes in spreads across these asset types. To summarize, spread risk for corporate bonds is analogous to interest rate risk, as a change in OAS produces the same change in price as a change in interest rates.

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Spread duration for mortgage-backed securities reflects the fact that a change in OAS affects the present value of expected future cash flows, but whether or not the cash flows themselves are affected by the change in the OAS is a function of assumptions made by the prepayment model used in the analysis. The spread duration of a diversified portfolio measures the overall sensitivity to a change in OASs across all security types, giving the portfolio manager important information about a portfolio’s risk profile which no other risk measure provides.

Prepayment uncertainty Now we turn to the another important source of risk in fixed-income markets, prepayment uncertainty. Prepayment modeling is one of the most critical variables in the mortgage-backed securities (MBS) market, as a prepayment forecast determines a security’s value and its perceived ‘riskiness’ (where ‘risky’ is defined as having a large degree of interest rate sensitivity, described by a large effective duration and/or negative convexity). For those who may be unfamiliar with CMOs, certain types of these securities (such as principal-only tranches, and inverse floaters) can have effective durations that are two or three times greater than the duration of the underlying mortgage collateral, while interest-only (IO) tranches typically have negative durations, and many CMOs have substantial negative convexity. Changes in prepayment expectations can have a considerable impact on the value of mortgagebacked and asset-backed securities and therefore represents an important source of risk. Let’s briefly review how mortgage prepayment modeling affects the valuation of mortgage-backed securities. To determine the expected future cash flows of a security, a prepayment model must predict the impact of a change in interest rates on a homeowner’s incentive to prepay (refinance) a mortgage; as rates decline, prepayments typically increase, and vice versa. Prepayment models take into account the age or ‘seasoning’ of the collateral, as a homeowner whose mortgage is relatively new is less likely to refinance in the near-term than a homeowner who has not recently refinanced. Prepayment models also typically incorporate ‘burnout’, a term that reflects the fact that mortgage pools will contain a certain percentage of homeowners who, despite a number of opportunities over the years, simply cannot or will not refinance their mortgages. Many models also reflect the fact that prepayments tend to peak in the summer months (a phenomenon referred to as ‘seasonality’), as homeowners will often postpone moving until the school year is over to ease their children’s transition to a new neighborhood. Prepayment models attempt to predict the impact of these and other factors on the level of prepayments received from a given pool of mortgages over the life of the collateral. Earlier, we alluded to the fact that mortgage valuation is a path-dependent problem. This is because the path that interest rates follow will determine the extent to which a given collateral pool is ‘burned out’ when a new refinancing opportunity arises. For example, consider a mortgage pool consisting of fairly new 7.00% mortgages, and two interest rate paths generated by a Monte Carlo simulation. For simplicity, we make the following assumptions: Ω Treasury rates are at 5.25% across the term structure Ω Mortgage lending rates are set at 150 bps over Treasuries Ω Homeowners require at least a 75 bp incentive to refinance their mortgages.

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Given this scenario, homeowners with a 7.00% mortgage do not currently have sufficient incentive to trigger a wave of refinancings. Now, imagine that along the first interest rate path Treasury rates rise to 6.00% over the first two years, then decline to 4.75% at the end of year 3; therefore, mortgage lending rates at the end of year 3 are at 6.25%, presenting homeowners with sufficient incentive to refinance at a lower rate for the first time in three years. Along this path, we would expect a significant amount of prepayments at the end of year 3. On the second path, imagine that rates decline to 4.75% by the end of the first year and remain there. This gives homeowners an opportunity to refinance their 7.00% mortgages for two full years before a similar incentive exists on the first path. Consequently, by the end of year 3 we would expect that most homeowners who wish to refinance have already done so, and the cash flows forecasted for the end of year 3 would differ markedly compared to the first path, even though interest rates are the same on both paths at that point in time. Therefore, we cannot forecast the prepayments to be received at a given point in time simply by observing the current level of interest rate; we must know the path that rates followed prior to that point. In valuing mortgage-backed securities, this is addressed by using some type of Monte Carlo simulation to generate a sufficient number of different interest rate paths which provide the basis for a prepayment model to predict cash flows from the collateral pool under a variety of possible paths, based upon the history of interest rates experienced along each path.

Differences in prepayment models Prepayment speed forecasts in the mortgage-backed securities market often differ considerably across various reliable dealers. Table 7.2 shows the median prepayment estimates provided to the Bond Market Association by ten dealer firms for different types of mortgage collateral, along with the high and low estimates that contributed to the median. Note that in many cases, the highest dealer prepayment forecast is more than twice as fast as the lowest estimate for the same collateral type. To illustrate the degree to which differences in prepayment model forecasts can affect one’s estimate of a portfolio’s characteristics, we created a portfolio consisting Table 7.2 Conventional 30-year ﬁxed-rate mortgages as of 15 October 1998 Collateral type

Dealer prepay (PSA%) forecast

High versus low differences

Coupon

Issue year

Median

Low

High

Absolute

6.0 6.0 6.0 6.5 6.5 6.5 7.0 7.0 7.0 7.5 7.5

1998 1996 1993 1998 1996 1993 1998 1996 1993 1997 1993

170 176 173 226 234 215 314 334 300 472 391

152 150 137 175 176 151 213 235 219 344 311

286 256 243 365 320 326 726 585 483 907 721

134 156 106 190 144 175 513 350 264 563 410

PSA PSA PSA PSA PSA PSA PSA PSA PSA PSA PSA

Percent 188% 171% 177% 209% 182% 216% 341% 249% 221% 264% 232%

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of the different mortgage collateral shown in Table 7.2, with equal par amounts of eleven collateral pools with various coupons and maturities, using the ‘Low’ (slowest) PSA% and the ‘High’ (fastest) PSA% for each collateral type. Using the ‘Low’ speed, the portfolio had an average life of 6.85 years, with a duration of 4.73; using the ‘High’ speeds, the same portfolio had an average life of 3.67 years and a duration of 2.69. Clearly, the uncertainty in prepayment modeling can have a large impact on one’s assessment of a portfolio’s risk profile. There are a number of reasons why no two prepayment models will produce the same forecast, even with the same information about the interest rate environment and the characteristics of the mortgage collateral of interest. For example, different firms’ prepayment models may be calibrated to different historical data sets – some use five or even ten years of data, others may use data from only the past few years; some models attach greater weight to more recent data, others attach equal weight to all time periods; the variables used to explain and forecast prepayment behavior differ across models, and so on. Therefore, differences in prepayment modeling across well-respected providers is to be expected.4 In addition to differences in the way models are calibrated and specified, there is some likelihood that the historical data used to fit the model no longer reflects current prepayment behavior. When new prepayment data indicates that current homeowner behavior is not adequately described by existing prepayment models, prepayment forecasts will change as dealers and other market participants revise their models in light of the new empirical evidence For example, in recent years mortgage lenders have become more aggressive in offering low-cost or no-cost refinancing. As a result, a smaller decline in interest rates is now sufficient to entice homeowners to refinance their mortgages compared to five years ago (the required ‘refinance incentive’ has changed). Further developments in the marketplace (e.g. the ability to easily compare lending rates and refinance a mortgage over the Internet) will undoubtedly affect future prepayment patterns in ways that the historical data used to fit today’s prepayment models does not reflect. As mentioned previously, in the Fall of 1998 a combination of events wreaked havoc in fixed-income markets. Traditional liquidity sources dried up in the MBS market, which forced a number of private mortgage lenders to file for bankruptcy over the course of a few days. At the same time, Treasury prices rose markedly as investors sought the safe haven of US Treasuries in the wake of the uncertainties in other markets. As a rule, when Treasury yields decline mortgage prepayments are expected to increase, because mortgage lenders are expected to reduce borrowing rates in response to the lower interest rate environment. This time, however, mortgage lenders actually raised their rates, because the significant widening of spreads in the secondary market meant that loans originated at more typical (narrower) spreads over Treasury rates were no longer worth as much in the secondary market, and many lenders rely on loan sales to the secondary market as their primary source of funds. (Note that this episode is directly relevant to the earlier discussion of spread duration for mortgage-backed securities.) These conditions caused considerable uncertainty in prepayment forecasting. Long-standing assumptions about the impact of a change in Treasury rates on refinancing activity did not hold up but it was uncertain as to whether or not this would be a short-lived phenomenon. Therefore, it was unclear whether prepayment models should be revised to reflect the new environment or whether this was a short-term aberration that did not warrant a permanent change to key modeling

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parameters. During this time, the reported durations of well-known benchmark mortgage indices, such as the Lehman Mortgage Index and Salomon Mortgage Index, swung wildly (one benchmark index’s duration more than doubled over the course of a week), indicating extreme uncertainty in risk assessments among leading MBS dealers. In other words, there was little agreement as to prepayment expectations for, and therefore the value of, mortgage-backed securities. Therefore we must accept the fact that a prepayment model can only provide a forecast, or an ‘educated guess’ about actual future prepayments. We also know that the market consensus about expected future prepayments can change quickly, affecting the valuation and risk measures (such as duration and convexity) that are being used to manage a portfolio of these securities. Therefore, the effect of revised prepayment expectations on the valuation of mortgage-backed securities constitutes an additional source of risk for firms that trade and/or invest in these assets. This risk, which we may call prepayment uncertainty risk, may be thought of as a ’model risk’ since it derives from the inherent uncertainty of all prepayment models. For an investment manager who is charged with managing a portfolio’s exposure to mortgages relative to a benchmark, or for a risk manager who must evaluate a firm’s interest rate risk including its exposure to mortgage-backed securities, this episode clearly illustrates the importance of understanding the sensitivity of a valuation or risk model’s output to a change in a key modeling assumption. We do this by computing a ‘prepayment uncertainty’ measure that tests the ‘stability’ of a model’s output given a change in prepayment forecasts. Deﬁning a measure of prepayment uncertainty While standard definitions for effective duration and convexity have gained universal acceptance as measures of interest rate risk,5 no standard set of prepayment uncertainty measures yet exists. Some proposed measures have been called ‘prepayment durations’ or ‘prepayment sensitivities’ (Sparks and Sung, 1995; Patruno, 1994). Here, we describe three measures that are readily understood and capture the major dimensions of prepayment uncertainty. These measures are labeled overall prepayment uncertainty, refinancing (‘refi’) partial prepayment uncertainty, and relocation (‘relo’) partial payment uncertainty. To derive an overall prepayment uncertainty measure, the ‘base case’ prepayment speeds predicted by a model are decreased by 10%, then increased by 10%, and two new prices are derived under the slower and faster versions of the model (holding the term structure of interest rates, volatilities and security’s option-adjusted spread constant): 6 PSMMñ10% ñPSMMò10% î100 2îPBase case SMM where Póprice and SMMósingle monthly mortality rate (prepayment speed expressed as a series of monthly rates). Computed this way, securities backed by discount collateral tend to show a negative prepayment uncertainty. This makes intuitive sense, as a slowdown in prepayment speeds means the investor must wait longer to be repaid at par. Conversely, securities backed by premium collateral tend to show a positive prepayment uncertainty, because faster prepayments decrease the amount of future income expected from the high-coupon mortgage pool compared to the base case forecast.

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Note that for CMOs, a tranche may be priced at a premium to par even though the underlying collateral is at a discount, and vice versa. Therefore, one should not assume that the prepayment uncertainty of a CMO is positive or negative simply by noting whether the security is priced below or above par.

Reﬁnance and relocation uncertainty One of the most important variables in a prepayment model is the minimum level or ‘threshold’ incentive it assumes a homeowner requires to go to the trouble of refinancing a mortgage. The amount of the required incentive has certainly declined over the past decade; in the early days of prepayment modeling, it was not unusual to assume that new mortgage rates had to be at least 150 bps lower than a homeowner’s mortgage rate before refinancings would occur. Today, a prepayment model may assume that only a 75 bp incentive or less is necessary to trigger a wave of refinancings. Therefore, we may wish to examine the amount of risk associated with a misestimate in the minimum incentive the model assumes homeowners will require before refinancing their mortgages. To do so, we separate the total prepayment uncertainty measure into two components: refinancing uncertainty and relocation uncertainty. The ‘refi’ measure describes the sensitivity of a valuation to changes in the above-mentioned refinancing incentive, and the ‘relo’ measure shows the sensitivity to a change in the level of prepayments that are independent of the level of interest rates (i.e. due to demographic factors such as a change in job status or location, birth of children, divorce, retirement, and so on). Table 7.3 shows the overall and partial (‘refi’ and ‘relo’ prepayment uncertainties) for selected 30-year passthroughs. Table 7.3 Prepayment uncertainty – 30-year mortgage collateral as of August 1998 Prepayment uncertainty (%)

Collateral seasoning*

Price

Total

Reﬁ

Relo

Effective duration

6.50% 7.50% 8.50%

New New New

99.43 102.70 103.69

ñ0.042 0.169 0.300

0.036 0.155 0.245

ñ0.080 0.014 0.055

3.70 1.93 1.42

6.50% 7.50% 8.50%

Moderate Moderate Moderate

99.60 102.51 104.38

ñ0.024 0.191 0.341

0.036 0.161 0.257

ñ0.060 0.031 0.083

3.37 1.92 1.87

6.50% 7.50% 8.50%

Seasoned Seasoned Seasoned

99.66 102.58 104.48

ñ0.014 0.206 0.363

0.038 0.163 0.258

ñ0.050 0.042 0.105

3.31 1.94 1.83

Coupon

*Note: ‘Seasoning’ refers to the amount of time since the mortgages were originated; ‘new’ refers to loans originated within the past 24 months, ‘moderate’ applies to loans between 25 and 60 months old, fully ‘seasoned’ loans are more than 60 months old.

At first glance, these prepayment uncertainty values appear to be rather small. For example, we can see that a 10% increase or decrease in expected prepayments would produce a 0.191% change in the value of a moderately seasoned 7.50% mortgage pool. However, it is important to note that a 10% change in prepayment expectations is a rather modest ‘stress test’ to impose on a model. Recall the earlier discussion of

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differences in prepayment forecasts among Wall Street mortgage-backed securities dealers, where the high versus low estimates for various collateral types differed by more than 200%. Therefore, it is reasonable to multiply the prepayment uncertainty percentages derived from a 10% change in a model by a factor of 2 or 3, or even more. It is interesting that there appears to be a negative correlation between total prepayment uncertainty and effective duration; in other words, as prepayment uncertainty increases, interest rate sensitivity decreases. Why would this be so? Consider the ‘New 6.50%’ collateral with a slightly negative total prepay uncertainty measure (ñ0.042). This collateral is currently priced at a slight discount to par, so a slowdown in prepayments would cause the price to decline as the investor would receive a somewhat below-market coupon longer than originally expected. Both the ‘refi’ and ‘relo’ components of this collateral’s total uncertainty measure are relatively small, partly because these are new mortgages and we do not expect many homeowners who have just recently taken out a new mortgage to relocate or even to refinance in the near future, and partly because the collateral is priced slightly below but close to par. Since the collateral is priced below par, even a noticeable increase in the rate of response to a refinancing incentive would not have much impact on homeowners in this mortgage pool so the ‘refi’ component is negligible. Also, since the price of the collateral is so close to par it means the coupon rate on the security is roughly equal to the currently demanded market rate of interest. An increase or decrease in prepayments without any change in interest rates simply means the investor will earn an at-market interest rate for a shorter or longer period of time; the investor is be indifferent as to whether the principal is prepaid sooner or later under these circumstances as there are reinvestment opportunities at the same interest rate that is currently being earned. In contrast, the effective duration is relatively large at 3.70 precisely because the collateral is new and is priced close to par. Since the collateral is new, the remaining cash flows extend further into the future than for older (seasoned) collateral pools and a change in interest rates would have a large impact on the present value of those cash flows. Also, since the price is so close to par a small decline in interest rates could cause a substantial increase in prepayments as homeowners would have a new-found incentive to refinance (in other words, the prepayment option is close to at-the-money). This highlights a subtle but important difference between the impact of a change in refinance incentive due to a change in interest rates, which effective duration reflects, and the impact of a prepayment model misestimate of refinancing activity absent any change in interest rates. We should also note that since prepayment uncertainty is positive for some types of collateral and negative for others, it is possible to construct a portfolio with a prepayment uncertainty of close to zero by diversifying across collateral types.

Prepayment uncertainty – CMOs For certain CMO tranche types, such as IO (interest-only), PO (principal only), inverse floaters and various ‘support’ tranches, and mortgage strips, the prepayment uncertainty measures can attain much greater magnitude, both positive and negative, than for passthroughs. By the same token, well-protected PACs will exhibit a lesser degree of prepayment uncertainty than the underlying pass-through collateral. At

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times, the prepayment uncertainty values for CMOs may seem counterintuitive; in other words, tranches that one would expect to be highly vulnerable to a small change in prepayment forecasts have fairly low prepayment uncertainties, and vice versa. This surprising result is a good reminder of the complexity of these securities. Consider the examples in Table 7.4. Table 7.4 Prepayment uncertainties for CMOs Tranche Type Total prepay. uncertainty ‘Reﬁ’ uncertainty ‘Relo’ uncertainty Collateral prepay. uncertainty

IO

PO

PAC #1

PAC #2

7.638 5.601 2.037 0.190

ñ2.731 ñ2.202 ñ0.529 (re-remic)

0.315 0.220 0.095 0.115

0.049 0.039 0.010 0.177

Here we see an IO tranche with an overall prepayment uncertainty value of 7.638; in other words, the tranche’s value would increase (decrease) by more than 7.5% if prepayments were expected to be 10% slower (faster) than originally forecasted. This is not surprising, given the volatile nature of IO tranches. If prepayment forecasts are revised to be faster than originally expected, it means that the (notional) principal balance upon which the IO’s cash flows are based is expected to pay down more quickly, thus reducing the total interest payments to the IO holder. In contrast, the prepayment uncertainty of the collateral pool underlying the IO tranche is a modest 0.19 – a 10% change in expected prepayment speeds would produce only a small change in the value of the collateral. One would expect PO tranches to exhibit fairly large prepayment uncertainty measures as well, as POs are priced at a substantial discount to par (they are zero coupon instruments) and a change in prepayment forecasts means the tranche holder expects to recoup that discount either sooner or later than originally estimated. The total prepayment uncertainty for this particular PO is –2.731; note that the underlying collateral of this PO is a ‘re-remic’ – in other words, the collateral is a combination of CMO tranches from other deals, which may be backed by various types of collateral. In a re-remic, the underlying collateral may be a combination of highly seasoned, premium mortgages of various ‘vintages’ so it is virtually impossible to estimate the tranche’s sensitivity to prepayment model risk simply by noting that it is a PO. The exercise of computing a prepayment uncertainty measure for CMOs reminds us that these are complicated securities whose sensitivities to changing market conditions bears monitoring.

Measuring prepayment uncertainty – a different approach An alternative way of looking at prepayment uncertainty is to consider the effect of a change in prepayment speed estimates on the effective duration of a mortgage or portfolio of mortgage-backed securities. Since many firms hedge their mortgage positions by shorting Treasuries with similar durations, it is important to note that that the duration of a mortgage portfolio is uncertain and can be something of a moving target. These tables show the impact of a ô10% change in prepayment speeds on the average life, effective duration and convexity of different types of mortgage collateral. We can see that a small change in prepayment expectations could significantly

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Table 7.5 Average life, effective duration and convexity – base case and prepay speeds ô10% Base case Coupon*

Avg life

Dur.

PSA% ò10%

Conv PSA%

PSA% ñ10%

Avg life

Dur

Conv PSA%

Avg life

Dur

Conv PSA%

30 year collateral 6.50 3.8 3.9 6.50 3.0 3.6 6.50 2.7 3.3 7.00 3.0 2.8 7.00 2.3 2.7 7.00 2.2 2.5 7.50 3.3 3.0 7.50 2.5 2.1 7.50 2.3 2.1 8.00 2.3 1.9 8.00 2.4 2.0

ñ1.2 ñ1.0 ñ0.9 ñ1.1 ñ0.8 ñ0.7 ñ0.6 ñ0.2 ñ0.3 ñ0.1 ñ0.1

470 467 511 631 572 609 577 536 594 618 562

3.6 2.7 2.4 2.8 2.1 1.9 3.0 2.3 2.0 2.2 2.2

3.7 3.4 3.1 2.6 2.4 2.3 3.0 2.0 1.9 1.7 1.9

ñ1.2 ñ1.0 ñ1.0 ñ1.1 ñ0.8 ñ0.7 ñ0.7 ñ0.3 ñ0.3 ñ0.1 ñ0.1

517 514 562 694 629 669 635 590 663 680 618

4.2 3.3 3.0 3.3 2.7 2.4 3.6 2.9 2.6 2.7 2.8

4.1 3.8 3.6 3.1 3.0 2.8 3.4 2.7 2.5 2.2 2.4

ñ1.1 ñ1.0 ñ0.9 ñ1.0 ñ0.8 ñ0.7 ñ0.7 ñ0.4 ñ0.4 ñ0.2 ñ0.2

423 420 460 568 515 549 519 482 535 556 506

15 year collateral 6.50 3.3 2.3 6.50 2.6 2.0 7.00 2.0 1.4

ñ1.1 ñ0.9 ñ0.7

493 457 599

3.1 2.4 1.8

2.1 1.8 1.2

ñ1.1 ñ0.9 ñ0.6

542 503 659

3.5 2.8 2.3

2.42 2.18 1.66

ñ1.0 ñ0.8 ñ0.7

444 411 537

Change in average life, effective duration and convexity versus base case prepay estimates Avg absolute chg ô10% Coupon*

Avg life

Avg percent chg ô10%

Eff dur

Conv

Avg life

Eff dur

Conv

30 year collateral 6.50 0.29 6.50 0.33 6.50 0.29 7.00 0.25 7.00 0.29 7.00 0.25 7.50 0.29 7.50 0.29 7.50 0.29 8.00 0.25 8.00 0.29

0.22 0.23 0.21 0.22 0.28 0.25 0.23 0.33 0.32 0.29 0.29

0.02 0.02 0.01 0.01 ñ0.02 ñ0.02 ñ0.01 ñ0.07 ñ0.06 ñ0.08 ñ0.07

7.62 11.11 10.92 8.33 12.52 11.52 8.97 11.67 12.96 10.73 12.05

5.54 6.30 6.34 7.80 10.34 9.96 7.40 15.70 14.79 15.24 14.18

ñ2.03 ñ2.07 ñ1.48 ñ0.45 2.53 2.90 1.02 29.55 18.18 61.54 59.09

15 year collateral 6.50 0.21 6.50 0.21 7.00 0.21

0.16 0.18 0.21

0.02 0.01 ñ0.02

6.26 8.07 10.42

6.86 8.79 14.79

ñ1.32 ñ1.11 2.11

*Note: The same coupon rate may appear multiple times, representing New, Moderately Seasoned and Fully seasoned collateral.

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change the computed interest rate sensitivity of a portfolio of mortgage-backed securities. For example, a 10% change in our prepayment forecast for 30-year, 7.0% collateral changes our effective duration (i.e. our estimated interest rate risk) by an average of 9.37% (across new, moderately seasoned and fully seasoned pools with a coupon rate, net WAC, of 7.0%). Since a small change in prepayment speeds can cause us to revise our estimated duration by close to 10% (or more), this means that an assessment of an MBS portfolio’s interest rate risk is clearly uncertain. With these two approaches to measuring prepayment uncertainty, i.e. the change in price or change in effective duration given a change in prepayment forecasts, a risk manager can monitor the portfolio’s sensitivity to prepayment model risk in terms of both market value and/or the portfolio’s interest rate sensitivity. For example, the ‘change in market value’ form of prepayment uncertainty might be used to adjust the results of a VAR calculation, while the ‘change in effective duration’ version could be used to analyze a hedging strategy to understand how a hedge against interest rate risk for a position in MBS would have to be adjusted if prepayment expectations shifted. The prepayment uncertainty measures presented here can also assist with trading decisions on a single-security basis, as differences in prepayment uncertainty may explain why two securities with seemingly very similar characteristics trade at different OASs.

Summary Risk management for fixed-income securities has traditionally focused on interest rate risk, relying on effective duration and other measures to quantify a security’s or portfolio’s sensitivity to changes in interest rates. Spread duration and prepayment uncertainty are measures that extend that risk management and investment analysis beyond interest rate risk to examine other sources of risk which impact fixed-income markets. At the individual security level these concepts can assist with trading and investment decisions, helping to explain why two securities with seemingly similar characteristics have different OASs and offer different risk/return profiles. At the portfolio level, these measures allow a manager to quantify and manage exposure to these sources of risk, trading off one type of exposure for another, depending upon expectations and risk tolerances. Examining the effect of interest rate moves combined with spread changes and shifts in prepayment modeling parameters can provide a greater understanding of a firm’s potential exposure to the various risks in fixed-income markets.

Notes 1

The term ‘mortgage-backed security’ refers to both fixed and adjustable rate mortgage passthroughs as well as CMOs. 2 Assuming effective duration is calculated using the same basis point shift used to calculate spread duration. 3 The two durations still would not be exactly equal, as spread duration is derived from a Monte Carlo simulation that involves an average of the expected prepayments along a number of possible interest rate paths, whereas Macaulay’s duration is computed using only a single set of cash flows generated by a specified lifetime PSA% speed.

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4

For a discussion of how to assess the accuracy of a prepayment model, see Phoa and Nercessian. Evaluating a Fixed Rate Payment Model – White Paper available from Capital Management Sciences. 5 Although convexity may be expressed as either a ‘duration drift’ term or a ‘contribution to return’ measure (the former is approximately twice the latter), its definition is generally accepted by fixed-income practitioners. 6 Our methodology assumes that a Monte Carlo process is used to compute OASs for mortgagebacked securities.

References Bond Week (1998) XVIII, No. 42, 19 October. Patruno, G. N. (1994) ‘Mortgage prepayments: a new model for a new era’, Journal of Fixed Income, March, 7–11. Phoa, W. and Nercessian, T. Evaluating a Fixed Rate Prepayment Model, White Paper available from Capital Management Sciences, Los Angeles, CA. Sparks, A. and Sung, F. F. (1995) ‘Prepayment convexity and duration’, Journal of Fixed Income, December, 42–56.

8

Stress testing PHILIP BEST

Does VaR measure risk? If you think this is a rhetorical question then consider another: What is the purpose of risk management? Perhaps the most important answer to this question is to prevent an institution suffering unacceptable loss. ‘Unacceptable’ needs to be defined and quantified, the quantification must wait until later in this chapter. A simple definition, however, can be introduced straight away: An unacceptable loss is one which either causes an institution to fail or materially damages its competitive position. Armed with a key objective and definition we can now return to the question of whether VaR measures risk. The answer is, at best, inconclusive. Clearly if we limit the VaR of a trading operation then we will be constraining the size of positions that can be run. Unfortunately this is not enough. Limiting VaR does not mean that we have prevented an unacceptable loss. We have not even identified the scenarios, which might cause such a loss, nor have we quantified the exceptional loss. VaR normally represents potential losses that may occur fairly regularly – on average, one day in twenty for VaR with a 95% confidence level. The major benefit of VaR is the ability to apply it consistently across almost any trading activity. It is enormously useful to have a comparative measure of risk that can be applied consistently across different trading units. It allows the board to manage the risk and return of different businesses across the bank and to allocate capital accordingly. VaR, however, does not help a bank prevent unacceptable losses. Using the Titanic as an analogy, the captain does not care about the flotsam and jetsam that the ship will bump into on a fairly regular basis, but does care about avoiding icebergs. If VaR tells you about the size of the flotsam and jetsam, then it falls to stress testing to warn the chief executive of the damage that would be caused by hitting an iceberg. As all markets are vulnerable to extreme price moves (the fat tails in financial asset return distributions) stress testing is required in all markets. However, it is perhaps in the emerging markets where stress testing really comes into its own. Consideration of an old and then a more recent crisis will illustrate the importance of stress testing. Figure 8.1 shows the Mexican peso versus US dollar exchange rate during the crisis of 1995. The figure shows the classic characteristics of a sudden crisis, i.e. no prior warning from the behavior of the exchange rate. In addition there is very low volatility prior to the crisis, as a result VaR would indicate that positions in this currency represented very low risk.1 Emerging markets often show very low volatility

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Figure 8.1 Mexican peso versus USD.

during normal market conditions, the lack of volatility results, in part, from the low trading volumes in these markets – rather than the lack of risk. It is not uncommon to see the exchange rate unchanged from one trading day to the next. Figure 8.2 shows the VaR ‘envelope’ superimposed on daily exchange rate changes (percent). To give VaR the best chance of coping with the radical shift in behavior the exponentially weighted moving average (EWMA) volatility model has been used (with a decay factor of 0.94 – giving an effective observation period of approximately 30 days). As can be seen, a cluster of VaR exceptions that make a mockery of the VaR measured before the crisis heralds the start of crisis. The VaR envelope widens rapidly in response to the extreme exchange rate changes. But it is too late – being after the event! If management had been relying on VaR as a measure of the riskiness of positions in the Mexican peso they would have been sadly misled. The start of the crisis sees nine exchange rate changes of greater than 20 standard deviations,2 including one change of 122 standard deviations!

Figure 8.2 VaR versus price change: EWMA – decay factor: 0.94.

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Figure 8.3 USD/IDR exchange rate (January 1997 to August 1998).

Figure 8.3 shows the Indonesian rupiah during the 1997 tumult in the Asian economies. Note the very stable exchange rate until the shift into the classic exponential curve of a market in a developing crisis. Figure 8.4 shows the overlay of VaR on daily exchange rate changes. There are several points to note from Figure 8.4. First, note that the VaR envelope prior to the crisis indicated a very low volatility and, by implication, therefore a low risk. VaR reflects the recent history of exchange rate changes and does not take account of changes in the economic environment until such changes show up in the asset’s price behavior. Second, although the total number of exceptions is within reasonable statistical bounds (6.2% VaR excesses over the two years3 ), VaR does not say anything about how large the excesses will be. In the two years of history examined for the Indonesian rupiah there were 12 daily changes in the exchange rate of greater than twice the 95% confidence VaR – and one change of 19 standard deviations. Consideration of one-day price changes, however, is not enough. One-day shocks are probably less important than the changes that happen over a number of days.

Figure 8.4 IDR/USD–VaR versus rate change: EWMA – decay factor: 0.94.

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Examining Figures 8.3 and 8.4 shows that the largest one-day change in the Indonesian rupiah exchange rate was 18%, bad enough you might think, but this is only one third the 57% drop during January 1998. Against this, the 95% confidence VaR at the beginning of January was ranging between 9% and 11.5%. Most people would agree that stress testing is required to manage ‘outliers’ 4 it is perhaps slightly less widely understood that what you really need to manage are strong directional movements over a more extended period of time.

Stress testing – central to risk control By now it should be clear that VaR is inadequate as a measure of risk by itself. Risk management must provide a way of identifying and quantifying the effects of extreme price changes on a bank’s portfolio. A more appropriate risk measurement methodology for dealing with the effect of extreme price changes is a class of methods known as stress testing. The essential idea behind stress testing is to take a large price change or, more normally, a combination of price changes and apply them to a portfolio and quantify the potential profit or loss that would result. There are a number of ways of arriving at the price changes to be used, this chapter describes and discusses the main methods of generating price changes and undertaking stress testing: Ω Scenario analysis: Creation and use of potential future economic scenarios to measure their profit and loss impact on a portfolio Ω Historical simulation: The application of actual past events to the present portfolio. The past events used can be either a price shock that occurred on a single day, or over a more extended period of time. Ω Stressing VaR: The parameters, which drive VaR, are ‘shocked’, i.e. changed and the resultant change in the VaR number produced. Stressing VaR will involve changing volatilities and correlations, in various combinations. Ω Systematic stress testing: The creation of a comprehensive series of scenarios that stress all major risk factors within a portfolio, singly and in combination. As with the first two methods, the desired end result is the potential profit and loss impact on the portfolio. The difference with this method is the comprehensive nature of the stress tests used. The idea is to identify all major scenarios that could cause a significant loss, rather than to test the impact of a small number scenarios, as in the first two methods above. One of the primary objectives of risk management is to protect against bankruptcy. Risk management cannot guarantee bankruptcy will never happen (otherwise all banks would have triple A credit ratings) but it must identify the market events that would cause a severe financial embarrassment. Note that ‘event’ should be defined as an extreme price move that occurs over a period of time ranging from one day to 60 days. Once an event is identified the bank’s management can then compare the loss implied by the event against the available capital and the promised return from the business unit. The probability of an extreme event occurring and the subsequent assessment of whether the risk is acceptable in prevailing market conditions has until now been partly subjective and partly based on a simple inspection of historic return series. Now, however, a branch of statistics known as extreme value theory (EVT) holds out the possibility of deriving the probability of extreme events consistently across

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different asset classes. EVT has long been used in the insurance industry but is now being applied to the banking industry. An introduction to EVT can be found below. Given the low probabilities of extreme shocks the judgement as to whether the loss event identified is an acceptable risk will always be subjective. This is an important point, particularly as risk management has become based increasingly on statistical estimation. Good risk management is still and always will be based first and foremost on good risk managers, assisted by statistical analysis. Stress testing must be part of the bank’s daily risk management process, rather than an occasional investigation. To ensure full integration into the bank’s risk management process, stress test limits must be defined from the bank’s appetite for extreme loss. The use of stress testing and its integration into the bank’s risk management framework is discussed in the final part of this chapter.

Extreme value theory – an introduction Value at risk generally assumes that returns are normally or log-normally distributed and largely ignores the fat tails of financial return series. The assumption of normality works well enough when markets are themselves behaving normally. As already pointed out above, however, risk managers care far more about extreme events than about the 1.645 or 2.33 standard deviation price changes (95% or 99% confidence) given by standard VaR. If measuring VaR with 99% confidence it is clear that, on average, a portfolio value change will be experienced one day in every hundred that will exceed VaR. By how much, is the key question. Clearly, the bank must have enough capital available to cover extreme events – how much does it need? Extreme value theory (EVT) is a branch of statistics that deals with the analysis and interpretation of extreme events – i.e. fat tails. EVT has been used in engineering to help assess whether a particular construction will be able to withstand extremes (e.g. a hurricane hitting a bridge) and has also been used in the insurance industry to investigate the risk of extreme claims, i.e. their size and frequency. The idea of using EVT in finance and specifically risk management is a recent development which holds out the promise of a better understanding of extreme market events and how to ensure a bank can survive them.

EVT risk measures There are two key measures of risk that EVT helps quantify: Ω The magnitude of an ‘X’ year return. Assume that senior management in a bank had defined its extreme appetite for risk as the loss that could be suffered from an event that occurs only once in twenty years – i.e. a twenty-year return. EVT allows the size of the twenty-year return to be estimated, based on an analysis of past extreme returns. We can express the quantity of the X year return, RX , where: P(r[RX )ó1ñF(RX ) Or in words; the probability that a return will exceed RX can be drawn from the distribution function, F. Unfortunately F is not known and must be estimated by fitting a fat-tailed distribution function to the extreme values of the series. Typical

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distribution functions used in EVT are discussed below. For a twenty-year return P(r[RX ) is the probability that an event, r, occurring that is greater than RX will only happen, on average, once in twenty years. Ω The excess loss given VaR. This is an estimate of the size of loss that may be suffered given that the return exceeds VaR. As with VaR this measure comes with a confidence interval – which can be very wide, depending on the distribution of the extreme values. The excess loss given VaR is sometimes called ‘Beyond VaR’, B-VaR, and can be expressed as: BñVaRóESrñVaR Dr[VaRT In words; Beyond VaR is the expected loss (mean loss) over and above VaR given (i.e. conditional on the fact) that VaR has been exceeded. Again a distribution function of the excess losses is required.

EVT distribution functions EVT uses a particular class of distributions to model fat tails:

F(X)óexp ñ 1òm

xñk t

ñ1/m

ò

where m, k and t are parameters which define the distribution, k is the location parameter (analogous to the mean), t is the scale parameter and m, the most important, is the shape parameter. The shape parameter defines the specific distribution to be used. mó0 is called the Gumbel distribution, m\0 is known as the Weibull and finally and most importantly for finance, m[0 is referred to as the Frechet distribution. Most applications of EVT to finance use the Fre´ chet distribution. From Figure 8.5 the fat-tailed behavior of the Fre´ chet distribution is clear. Also notice that the distribution has unbounded support to the right. For a more formal exposition of the theory of EVT see the appendix at the end of this chapter.

Figure 8.5 Fre´chet distribution.

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The use and limitations of EVT At present EVT is really only practically applicable to single assets. There is no easy to implement multivariate application available at the time of writing. Given the amount of academic effort going into this subject and some early indications of progress it is likely that a tractable multivariate solution will evolve in the near future. It is, of course, possible to model the whole portfolio as a single composite ‘asset’ but this approach would mean refitting the distribution every time the portfolio changed, i.e. daily. For single assets and indices EVT is a powerful tool. For significant exposures to single-asset classes the results of EVT analysis are well worth the effort. Alexander McNeil (1998) gives an example of the potential of EVT. McNeil cites the hypothetical case of a risk analyst fitting a Fre´ chet distribution to annual maxima of the S&P 500 index since 1960. The analyst uses the distribution to determine the 50-year return level. His analysis indicates that the confidence interval for the 50-year return lies between 4.9% and 24%. Wishing to give a conservative estimate the analyst reports the maximum potential loss to his boss as a 24% drop. His boss is sceptical. Of course the date of this hypothetical analysis is the day before the 1987 crash – on which date the S&P 500 dropped 20.4%. A powerful demonstration of how EVT can be used on single assets.

Scenario analysis When banks first started stress testing it was often referred to as Scenario Analysis. This seeks to investigate the effect, i.e. the change in value of a portfolio, of a particular event in the financial markets. Scenarios were typically taken from past, or potential future, economic or natural phenomena, such as a war in the Middle East. This may have a dramatic impact on many financial markets: Ω Oil price up 50%, which may cause Ω Drop in the US dollar of 20%, which in turn leads to Ω A rise in US interest rates of 1% These primary market changes would have significant knock-on effects to most of the world’s financial markets. Other political phenomena that could be investigated include the unexpected death of a head of state, a sudden collapse of a government or a crisis in the Euro exchange rate. In all cases it is the unexpected or sudden nature of the news that causes an extreme price move. Natural disasters can also cause extreme price moves, for example the Japanese earthquake in 1995. Financial markets very quickly take account of news and rumour. A failed harvest is unlikely to cause a sudden price shock, as there is likely to be plenty of prior warning, unless the final figures are much worse than the markets were expecting. However, a wellreported harvest failure could cause the financial markets to substantially revalue the commodity, thereby causing a sustained price increase. A strong directional trend in a market can have an equally devastating effect on portfolio value and should be included in scenario analyses.

Stress testing with historical simulation Another way of scenario testing is to recreate actual past events and investigate their impact on today’s portfolio. The historical simulation method of calculating VaR

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lends itself particularly well to this type of stress testing as historical simulation uses actual asset price histories for the calculation of VaR. Scenario testing with historical simulation simply involves identifying past days on which the price changes would have created a large change in the value of today’s portfolio. Note that a large change in the portfolio value is sought, rather than large price changes in individual assets. Taking a simple portfolio as an example: $3 million Sterling, $2 million gold and $1 million Rand, Figure 8.6 below shows 100 days of value changes for this portfolio.

Figure 8.6 Portfolio value change.

It is easy to see the day on which the worst loss would have occurred. The size of the loss is in itself an interesting result: $135000, compared to the VaR for the portfolio of $43000. Historical simulation allows easy identification of exactly what price changes caused this extreme loss (see Table 8.1). Table 8.1 Asset price change (%) Asset

% change

Sterling Gold Rand

ñ2.5 ñ5.6 ñ0.2

Total (weighted)

ñ2.3

This is useful information, a bank would be able to discuss these results in the context of its business strategy, or intended market positioning. It may also suggest that further analysis of large price changes in gold are indicated to see whether they have a higher correlation with large price changes in sterling and rand. Identifying the number of extreme price moves, for any given asset, is straightforward. Historical simulation enables you to go a step further, and identify which assets typically move together in times of market stress. Table 8.2 shows the price changes that caused the biggest ten losses in five years of price history for the example portfolio above. It can be seen from Table 8.2 that the two currencies in the portfolio seem to move together during market shocks. This is suggesting that in times of market stress the currencies have a higher correlation than they do usually. If this was shown to be the case with a larger number of significant portfolio value changes then it is extremely important information and should be used when constructing stress tests for a portfolio and the corresponding risk limits for the portfolio. In fact, for the example portfolio, when all changes in portfolio value of more than 1% were examined it was found that approximately 60% of them arose as a result of large price moves (greater than 0.5%) in both of the currencies. This is particularly

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Rand

Sterling

Change in portfolio value

ñ1.41 ñ0.17 ñ3.30 0.06 ñ0.29 ñ0.33 0.88 0.03 ñ0.60 ñ0.60

ñ13.31 ñ5.62 ñ1.77 ñ3.72 ñ0.71 ñ3.12 ñ0.79 ñ2.62 ñ2.14 1.44

ñ0.05 ñ2.53 ñ0.67 ñ1.77 ñ2.54 ñ1.66 ñ3.23 ñ1.98 ñ1.72 ñ2.68

ñ162 956 ñ135 453 ñ103 728 ñ89 260 ñ89 167 ñ87 448 ñ87 222 ñ85 157 ñ85 009 ñ77 970

interesting as the correlation between rand and sterling over the 5-year period examined is very close to zero. In times of market stress the correlation between sterling and rand increases significantly, to above 0.5.

Assessing the effect of a bear market Another form of scenario testing that can be performed with historical simulation is to measure the effect on a portfolio of an adverse run of price moves, no one price move of which would cause any concern by itself. The benefit of using historical simulation is that it enables a specific real life scenario to be tested against the current portfolio. Figure 8.7 shows how a particular period of time can be selected and used to perform a scenario test. The example portfolio value would have lost $517 000 over the two-week period shown, far greater than the largest daily move during the five years of history examined and 12 times greater than the calculated 95% VaR.

Figure 8.7 Portfolio value change.

A bank must be able to survive an extended period of losses as well as extreme market moves over one day. The potential loss over a more extended period is known as ‘maximum drawdown’ in the hedge-fund industry. Clearly it is easier to manage a period of losses than it is to manage a sudden one-day move, as there will be more opportunities to change the structure of the portfolio, or even to liquidate the portfolio. Although in theory it is possible to neutralize most positions in a relatively short period of time this is not always the case. Liquidity can become a real issue in times

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of market stress. In 1994 there was a sudden downturn in the bond markets. The first reversal was followed by a period of two weeks in which liquidity was much reduced. During that period, it was extremely difficult to liquidate Eurobond positions. These could be hedged with government bonds but that still left banks exposed to a widening of the spreads between government and Eurobonds. This example illustrates why examining the impact of a prolonged period of market stress is a worthwhile part of any stress-testing regime. One of the characteristics of financial markets is that economic shocks are nearly always accompanied by a liquidity crunch. There remains the interesting question of how to choose the extended period over which to examine a downturn in the market. To take an extreme; it would not make sense to examine a prolonged bear market over a period of several months or years. In such prolonged bear markets liquidity returns and positions can be traded out of in a relatively normal manner. Thus the question of the appropriate length of an extended period is strongly related to liquidity and the ability to liquidate a position – which in turn is dependent on the size of the position. Market crises, in which liquidity is severely reduced, generally do not extend for very long. In mature and deep markets the maximum period of severely restricted liquidity is unlikely to last beyond a month, though in emerging markets reduced liquidity may continue for some time. The Far Eastern and Russian crises did see liquidity severely reduced for periods of up to two months, after which bargain hunters returned and generated new liquidity – though at much lower prices. As a rule of thumb, it would not make sense to use extended periods of greater than one month in the developed markets and two months in the emerging markets. These guidelines assume the position to be liquidated is much greater than the normally traded market size. In summary, historical simulation is a very useful tool for investigating the impact of past events on today’s portfolio. Therein also lies its limitation. If a bank’s stress testing regime relied solely on historical simulation it would be assuming that past events will recur in the same way as before. This is extremely unlikely to be the case. In practice, as the dynamics of the world’s financial markets change, the impact of a shock in one part of the world or on one asset class will be accompanied by a new combination of other asset/country shocks – unlike anything seen before. The other limitation is, of course, that historical simulation can only give rise to a relatively small number of market shock scenarios. This is simply not a sufficiently rigorous way of undertaking stress testing. It is necessary to stress test shocks to all combinations of significant risk factors to which the bank is exposed.

Stressing VaR – covariance and Monte Carlo simulation methods The adoption of VaR as a new standard for measuring risk has given rise to a new class of scenario tests, which can be undertaken with any of the three main VaR calculation methods: covariance, historical simulation and Monte Carlo simulation (see Best, 1998). The use of historical simulation for scenario testing was discussed in the preceding section. With the covariance and Monte Carlo simulation methods the basic VaR inputs can be stressed to produce a new hypothetical VaR.

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Scenario testing using either covariance or Monte Carlo simulation is essentially the same, as volatilities and correlations are the key inputs for both the VaR methods. Before describing the stress testing of VaR it is worth considering what the results of such a stress test will mean. At the beginning of this chapter stress testing was defined as the quantification of potential significant portfolio losses as a result of changes in the prices of assets making up the portfolio. It was also suggested that stress tests should be used to ascertain whether the bank’s portfolio represents a level of risk that is within the bank’s appetite for risk. Scenario and stress testing have been implicitly defined so far as investigating the portfolio impact of a large change in market prices. Stress testing VaR is not an appropriate way to undertake such an investigation, as it is simpler to apply price changes directly to a portfolio. The stressing of volatility and correlation is asking how the bank’s operating, or dayto-day, level of risk would change if volatilities or correlations changed. This is a fundamentally different question than is answered by stress testing proper. Nonetheless it is a valid to ask whether the bank would be happy with the level of risk implied by different volatilities and correlations. Given that changes in volatilities and correlations are not instantaneous they do not pose the same threat to a trading institution as a market shock or adjustment.

Stressing volatility When stressing volatility in a VaR calculation it is important to be clear as to what is being changed with respect to the real world. Often, when volatility is stressed in a VaR calculation it is intended to imitate the effect of a market shock. If this is the intention, then it is better to apply the price move implied by the stressed volatility directly to the portfolio and measure its effect. Given that volatilities are calculated as some form of weighted average price change over a period of time it is clear that stressing volatility is an inappropriate way to simulate the effect of an extreme price movement. Therefore, we should conclude that the change in VaR given by stressing volatilities is answering the question ‘what would my day-to-day level of risk be if volatilities changes to X ?’

Stressing correlations Similar arguments apply to correlations as for volatilities. Stressing correlations is equivalent to undertaking a stress test directly on a portfolio. Consider a portfolio of Eurobonds hedged with government bonds. Under normal market conditions these two assets are highly correlated, with correlations typically lying between 0.8 and 0.95. A VaR stress test might involve stressing the correlation between these two asset classes, by setting the correlations to zero. A more direct way of undertaking this stress test is to change the price of one of the assets whilst holding the price of the second asset constant. Again, the question that must be asked is; what is intended by stressing correlations? Applying scenarios to VaR input parameters may give some useful insights and an interesting perspective on where the sources of risk are in a portfolio and equally, where the sources of diversification are in a portfolio. Nonetheless, stressing a VaR calculation, by altering volatilities and correlations, is not the most effective or efficient way of performing stress tests. Stressing volatilities and correlations in a VaR calculation will establish what the underlying risk in a portfolio would become if volatilities and correlations were to

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change to the levels input. Bank management can then be asked whether they would be happy with regular losses of the level given by the new VaR.

The problem with scenario analysis The three methods described above are all types of scenario analysis, i.e. they involve applying a particular scenario to a portfolio and quantifying its impact. The main problem with scenario testing, however it is performed, is that it only reveals a very small part of the whole picture of potential market disturbances. As shown below, in the discussion on systematic testing, there are an extremely large number of possible scenarios. Scenario analysis will only identify a tiny number of the scenarios that would cause significant losses. A second problem with scenario analysis is that the next crisis will be different. Market stress scenarios rarely if ever repeat themselves in the same way. For any individual asset over a period of time there will be several significant market shocks, which in terms of a one-day price move will look fairly similar to each other. What is unlikely to be the same is the way in which a price shock for one asset combines with price shocks for other assets. A quick examination of Table 8.2 above shows this to be true. ‘All right, so next time will be different. We will use our economists to predict the next economic shock for us.’ Wrong! Although this may be an interesting exercise it is unlikely to identify how price shocks will combine during the next shock. This is simply because the world’s financial system is extremely complex and trying to predict what will happen next is a bit like trying to predict the weather. The only model complex enough to guarantee a forecast is the weather system itself. An examination of fund management performance shows that human beings are not very good at predicting market trends, let alone sudden moves. Very few fund managers beat the stock indices on a consistent basis and if they do, then only by a small percentage. What is needed is a more thorough way of examining all price shock combinations.

Are simulation techniques appropriate? One way of generating a large number of market outcomes is to use simulation techniques, such as the Monte Carlo technique. Simulation techniques produce a random set of price outcomes based on the market characteristics assumed. Two points should be made here. One of the key market characteristics usually assumed is that price changes are normally distributed. The second point to note is that modelling extreme moves across a portfolio is not a practical proposition at present. Standard Monte Carlo simulation models will only produce as many extreme price moves as dictated by a normal distribution. For stress testing we are interested in price moves of greater than three standard deviations, as well as market moves covered by a normal distribution. Therefore, although simulation may appear to provide a good method of producing stress test events, in practice it is unlikely to be an efficient approach.

Systematic testing From the previous section, it should be clear that a more comprehensive method of stress testing is required. The methods so far discussed provide useful ways to

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investigate the impact of specific past or potential future scenarios. This provides valuable information but is simply not sufficient. There is no guarantee that all significant risk factors have been shocked, or that all meaningful combinations have been stressed. In fact it is necessary to impose systematically (deterministically) a large number of different combinations of asset price shocks on a portfolio to produce a series of different stress test outcomes. In this way scenarios that would cause the bank significant financial loss can be identified. Stress testing is often only thought about in the context of market risk, though it is clearly also just as applicable to credit risk. Stress tests for market and credit risk do differ as the nature and number of risk factors are very different in the two disciplines. Market risk stress testing involves a far greater number of risk factors than are present for credit risk. Having said this it is clear that credit risk exposure for traded products is driven by market risk factors. This relationship is discussed further in the section on credit risk stress testing.

Market risk stress tests As noted above, the dominant issue in stress testing for market risk is the number of risk factors involved and the very large number of different ways in which they can combine. Systematic stress testing for market risk should include the following elements: Ω Ω Ω Ω Ω

Non-linear price functions (gamma risk) Asymmetries Correlation breakdowns Stressing different combinations of asset classes together and separately appropriate size shocks

This section looks at ways of constructing stress tests that satisfy the elements listed above, subsequent sections look at how to determine the stress tests required and the size of the shocks to be used. Table 8.3 shows a matrix of different stress tests for an interest rate portfolio that includes options. The table is an example of a matrix of systematic stress tests. Table 8.3 Stress test matrix for an interest rate portfolio containing options – proﬁt and loss impact on the portfolio (£000s) Vol. multipliers î0.6 î0.8 î0.9 Null î1.1 î1.2 î1.4

Parallel interest rates shifts (%) ñ2

ñ1

ñ0.5

ñ0.1

Null

0.1

0.5

1

2

4

1145 1148 1150 1153 1157 1162 1174

435 447 456 466 478 490 522

34 78 100 122 144 167 222

ñ119 ñ60 ñ33 ñ8 18 46 113

ñ102 ñ48 ñ24 0 25 52 118

ñ102 ñ52 ñ29 ñ6 18 45 114

ñ188 ñ145 ñ125 ñ103 ñ77 ñ46 37

ñ220 ñ200 ñ189 ñ173 ñ151 ñ119 ñ25

37 ñ10 ñ12 2 32 80 223

ñ130 ñ165 ñ164 ñ151 ñ126 ñ90 21

The columns represent different parallel shifts in the yield curve and the rows represent different multiples of volatility. It should be noted that it is not normally sufficient to stress only parallel shifts to the yield curve. Where interest rate trading

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forms a substantial part of an operation it would be normal to also devise stress test matrices that shock the short and long end of the yield and volatility curves. The stress test matrix in Table 8.3 is dealing with gamma risk (i.e. a non-linear price function) by stressing the portfolio with a series of parallel shifts in the yield curve. The non-linear nature of option pricing means that systematic stress testing must include a number of different shifts at various intervals, rather than the single shift that would suffice for a portfolio of linear instruments (for example, equities). Also note that the matrix deals with the potential for an asymmetric loss profile by using both upward and downward shifts of both interest rates and implied volatility. The first thing to note is that the worst-case loss is not coincident with the largest price move applied. In fact had only extreme moves been used then the worst-case loss would have been missed altogether. In this example the worst-case loss occurs with small moves in rates. This is easier to see graphically, as in Figure 8.8.

Figure 8.8 Portfolio proﬁt and loss impact of combined stresses to interest rates and implied volatilities.

Stress test matrices are often presented graphically as well as numerically as the graphical representation facilitates an instant comprehension of the ‘shape’ of the portfolio – giving a much better feel for risks being run than can be obtained from a table of numbers. Table 8.3 shows a set of stress tests on a complete portfolio where price changes are applied to all products in the portfolio at the same time. This is only one of a number of sets of stress tests that should be used. In Table 8.3 there are a total of 69 individual scenarios. In theory, to get the total number of stress test combinations, the number of different price shocks must be applied to each asset in turn and to all combinations of assets. If the price shocks shown in Table 8.3 were applied to a portfolio of 10 assets (or risk factors), there would be a possible 70 587 6 stress tests. With a typical bank’s portfolio the number of possible stress tests would render

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comprehensive stress testing impractical. In practice a little thought can reduce the number of stress tests required. Table 8.3 is an example of a stress test matrix within a single asset class: interest rates. As a bank’s portfolio will normally contain significant exposures to several asset classes (interest rates, equities, currencies and commodities), it makes sense to devise stress tests that cover more than one asset class. Table 8.4 shows a stress test matrix that investigates different combinations of shocks to a bank’s equity and interest rate portfolios. Table 8.4 Cross-asset class stress test for equities and interest rates – proﬁt and loss impact on the portfolio (£000s) Index (% change) ñ50% ñ30% ñ10% 0 10% 30% 50%

Parallel interest rate shift (basis points) ñ200

ñ100

ñ50

0

50

100

200

50 250 450 550 650 850 1050

ñ250 ñ50 150 250 350 550 750

ñ370 ñ170 30 130 230 430 630

ñ500 ñ300 ñ100 0 100 300 500

ñ625 ñ425 ñ225 ñ125 ñ25 175 375

ñ700 ñ500 ñ300 ñ200 ñ100 100 300

ñ890 ñ690 ñ490 ñ390 ñ290 ñ90 110

As in the previous example, the columns represent different parallel shifts in the yield curve; the rows represent different shocks to an equity index. It should be noted that this stress test could be applied to a single market, i.e. one yield curve and the corresponding index, to a group of markets, or to the whole portfolio, with all yield curves and indices being shocked together. Again, it can be helpful to view the results graphically. Figure 8.9 instantly proves its worth, as it shows that the portfolio is behaving in a linear fashion, i.e. that there is no significant optionality present in the portfolio. In normal circumstances equity and interest rate markets are negatively correlated, i.e. if interest rates rise then equity markets often fall. One of the important things a stress test matrix allows a risk manager to do is to investigate the impact of changing the correlation assumptions that prevail in normal markets. The ‘normal’ market assumption of an inverse correlation between equities and interest rates is in effect given in the bottom right-hand quarter of Table 8.4. In a severe market shock one might expect equity and interest rate markets to crash together i.e. to be highly correlated. This scenario can be investigated in the upper right-hand quarter of the stress test matrix.

Credit risk stress testing There is, of course, no reason why stress testing should be constrained to market risk factors. In fact, it makes sense to stress all risk factors to which the bank is exposed and credit risk is, in many cases, the largest risk factor. It would seem natural having identified a potentially damaging market risk scenario to want to know what impact that same scenario would have on the bank’s credit exposure. Table 8.5 shows a series of market scenarios applied to trading credit risk exposures.

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Figure 8.9 Portfolio proﬁt and loss impact of combined stresses to interest rates and an equity index.

Table 8.5 A combined market and credit risk stress test (£000s) Market risk scenario Credit scenarios Portfolio portfolio value Portfolio unrealized proﬁt and loss Counterparty A exposure Counterparty B exposure Collateral value Loss given default; collateral agreement works Loss given default; collateral agreement failure Total portfolio loss – default of counterparty B

Now 10 000 0 5 000 5 000 5 000 0 ñ5 000 ñ5 000

A 9 000 ñ1 000 1 740

B 8 000 ñ2 000 ñ720

C 6 000 ñ4 000 ñ4 320

D 4 000 ñ6 000 ñ7 040

7 260 8 720 10 320 11 040 3 750 2 950 2 400 1 375 ñ3 510 ñ5 770 ñ7 920 ñ9 665 ñ7 260 ñ8 720 ñ10 320 ñ11 040 ñ8 260 ñ10 720 ñ14 320 ñ17 040

In general, a bank’s loan portfolio is considered to be relatively immune to market scenarios. This is not strictly true, as the value of a loan will change dependent on the level of the relevant yield curve; also, many commercial loans contain optionality (which is often ignored for valuation purposes). Recently there has been a lot of discussion about the application of traded product valuation techniques to loan books and the desirability of treating the loan and trading portfolio on the same basis for credit risk. This makes eminent sense and will become standard practice over the next few years. However, it is clear that the value of traded products, such as swaps, are far more sensitive to changes in market prices than loans. Table 8.5 shows a portfolio containing two counterparties (customers) the value of their exposure is equal at the present time (£5 million). Four different market risk scenarios have been applied to the portfolio to investigate the potential changes in the value of the counterparty exposure. The four scenarios applied to the portfolio

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can be taken to be a series of increasing parallel yield curve shifts. It is not really important, however, it may be the case that the counterparty portfolios contain mainly interest rate instruments, such as interest rate swaps, and that the counterparty portfolios are the opposite way round from each other (one is receiving fixed rates whilst the other is paying fixed rates). The thought process behind this series of stress could be that the systematic stress testing carried out on the market risk portfolio has enabled the managers to identify market risk scenarios that concern them. Perhaps they result from stressing exposure to a particular country. Risk managers then remember that there are two counterparties in that country that they are also concerned about from a purely credit perspective, i.e. they believe there is a reasonable probability of downgrade or default. They decide to run the same market risk scenarios against the counterparties and to investigate the resultant exposure and potential losses given default. Of course, multiple credit risk stress tests could be run with different counterparties going into default, either singly or in groups. Table 8.5 shows that exposure to counterparty A becomes increasingly negative as the scenarios progress. Taking scenario D as an example, the exposure to counterparty A would be ñ£7.04 million. In the case of default, the profit and loss for the total portfolio (unrealized plus realized profit and loss) would not change. Prior to default, the negative exposure would be part of the unrealized profit and loss on the portfolio. After default, the loss would become realized profit and loss.7 As the net effect on the portfolio value is zero, counterparties with negative exposure are normally treated as a zero credit risk. More worrying is the rapid increase in the positive value of exposure to counterparty B, this warrants further analysis. Table 8.5 gives further analysis of the credit risk stress test for counterparty B in the grey shaded area. The line after the exposure shows the value of collateral placed by counterparty B with the bank. At the current time, the exposure to counterparty B is fully covered by the value of collateral placed with the bank. It can be seen that this situation is very different, dependent on the size of the market shock experienced. In the case of scenario D the value of the collateral has dropped to £1.375 million – against an exposure of £11.04 million, i.e. the collateral would only cover 12.5% of the exposure! This may seem unrealistic but is actually based on market shocks that took place in the emerging markets in 1997 and 1998. The next part of the analysis is to assume default and calculate the loss that would be experienced. This is shown in the next two lines in Table 8.5. The first line assumes the collateral agreement is enforceable. The worst-case loss scenario is that the collateral agreement is found to be unenforceable. Again this may sound unrealistic but more than one banker has been heard to remark after the Asian crises that ‘collateral is a fair-weather friend’. The degree of legal certainty surrounding collateral agreements – particularly in emerging markets is not all that would be wished for. Note that the credit risk stress test does not involve the probability of default or the expected recovery rate. Both of these statistics are common in credit risk models but do not help in the quantification of loss in case of an actual default. The recovery rate says what you may expect to get back on average (i.e. over a large number of defaults with a similar creditor rating) and does not include the time delay. In case of default, the total loss is written off, less any enforceable collateral held, no account is taken of the expected recovery rate. After all, it may be some years before the counterparty’s assets are liquidated and distributed to creditors. The final piece of analysis to undertake is to examine the impact the default has

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on the total portfolio value. For a real portfolio this would need to be done by stripping out all the trades for the counterparties that are assumed to be in default and recalculating the portfolio values under each of the market scenarios under investigation. In the very simple portfolio described in Table 8.5, we can see what the impact would be. It has already been noted that the impact of counterparty A defaulting would be neutral for the portfolio value, as an unrealized loss would just be changed into a realized loss. In the case of counterparty B, it is not quite so obvious. Once a credit loss has occurred, the portfolio value is reduced by the amount of the loss (becoming a realised loss). The final line in Table 8.5 shows the total portfolio loss as a combination of the market scenario and the default of counterparty B. This is actually the result of a combined market and credit risk stress test.

Which stress test combinations do I need? The approach to determining which stress tests to perform is best described by considering a simple portfolio. Consider a portfolio of two assets, a 5-year Eurobond hedged with a 10-year government bond. The portfolio is hedged to be delta neutral, or in bond terminology, has a modified duration of zero. In other words the value of the portfolio will not change if the par yield curve moves up (or down) in parallel, by small amounts. If the stress test matrix shown in Table 8.3 were performed on the delta neutral bond portfolio the results would be close to zero (there would be some small losses shown due to the different convexity8 of the bonds). The value of this simple portfolio will behave in a linear manner and there is therefore no need to have multiple parallel shifts. However, even with the large number of shifts applied in Table 8.3, not all the risks present in the portfolio have been picked up. Being delta neutral does not mean the portfolio is risk free. In particular this portfolio is subject to spread risk; i.e. the risk that the prices of Euro and government bonds do not move in line with each other. It is speculated that this is the risk that sunk LTCM; it was purportedly betting on spreads narrowing and remaining highly correlated. The knock-on effect of the Asian crisis was that spreads widened dramatically in the US market. The other risk that the portfolio is subject to is curve risk; i.e. the risk that yield curve moves are not parallel. Stress tests must be designed that capture these risks. Curve risk can be investigated by applying curve tilts to the yield curve, rather than the parallel moves used in Table 8.3. Spread risk can be investigated by applying price shocks to one side of a pair of hedged asset classes. In this example a price shock could be applied to the Eurobonds only. This example serves to illustrate the approach for identifying the stress tests that need to be performed. A bank’s portfolio must be examined to identify the different types of risk that the portfolio is subject to. Stress tests should be designed that test the portfolio against price shocks for all the significant risks identified. There are typically fewer significant risks than there are assets in a portfolio and stress tests can be tailored to the products present in the portfolio. A portfolio without options does not need as many separate yield curve shocks as shown in Table 8.3, as the price function of such a portfolio will behave approximately linearly. From this example it can be seen that the actual number of stress tests that need to be performed, whilst still significant, is much smaller than the theoretical number of combinations. The stress tests illustrated by Table 8.3 in the previous section did not specify

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whether it is for a single market or group of markets. It is well known that the world’s interest rates are quite highly correlated and that a shock to one of the major markets, such as America, is likely to cause shocks in many of the world’s other markets. When designing a framework of systematic stress test such relationships must be taken into account. A framework of systematic stress test should include: Ω Stressing individual markets to which there is significant exposure Ω Stressing regional groups, or economic blocks; such as the Far East (it may also make sense to define a block whose economic fortunes are closely linked to Japan, The Euro block (with and without non-Euro countries) and a block of countries whose economies are linked to that of the USA9 Ω Stressing the whole portfolio together – i.e. the bank’s total exposure across all markets and locations stressing exposure in each trading location (regardless of the magnitude of the trading activity – Singapore was a minor operation for Barings). Stress tests in individual trading locations should mirror those done centrally but should be extended to separately stress any risk factors that are specific to the location.

Which price shocks should be used? The other basic question that needs to be answered is what magnitude of price shocks to use. A basic approach will entail undertaking research for each risk factor to be stressed to identify the largest ever move and also the largest move in the last ten years. Judgement must then be used to choose price shocks from the results of the research. The size of the price shocks used may be adjusted over time as asset return behavior changes. At the time of writing the world was in a period of high volatility. In such an environment it may make sense to increase the magnitude of price shocks. The price shocks used will not need to change often but should be reviewed once a year or as dictated by market behavior and changes in portfolio composition. A more sophisticated approach, for individual assets, would involve the use of extreme value theory (EVT). There are two approaches to designing stress tests with EVT: Ω Find the magnitude of price change that will be exceeded only, on average, once during a specified period of time. The period of time is a subjective decision and must be determined by the risk manager, typical periods to be considered may be 10, 20 or 50 years. If 20 years were chosen, the price change identified that would not be exceeded on average more than once in 20 years is called the ‘20-year return level’. Ω The second approach is merely the inverse of the first. Given that a bank will have identified its risk appetite (see below) in terms of the maximum loss it is prepared to suffer, then EVT can be used to determine the likelihood of such a loss. If the probability were considered to be too great (i.e. would occur more often than the bank can tolerate) then the risk appetite must be revisited.

Determining risk appetite and stress test limits The primary objective of stress testing is to identify the scenarios that would cause the bank a significant loss. The bank can then make a judgement as to whether it is happy with the level of risk represented by the current portfolios in the present market.

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Stress testing and capital In order to determine whether a potential loss identified by stress testing is acceptable, it must be related to the bank’s available capital. The available capital in this case will not be the ‘risk capital’, or shareholder-capital-at-risk allocated to the trading area (i.e. the capital used in risk-adjusted performance measures such as RAROC) but the bank’s actual shareholder capital. Shareholder-capital-at-risk is often based on VaR (as well as measures of risk for credit and operational risk). VaR will represent the amount of capital needed to cover losses due to market risk on a day-to-day basis. If, however, an extreme period of market stress is encountered then the bank may lose more than the allocated shareholder-capital-at-risk. This will also be the case if a downturn is experienced over a period of time. Clearly institutions must be able to survive such events, hence the need for the actual capital to be much larger than the shareholder-capital-atrisk typically calculated for RAROC. As with shareholder-capital-at-risk, the question of allocation arises. It is not possible to use stress testing for the allocation of actual shareholder capital until it is practical to measure the probability of extreme events consistently across markets and portfolios. Therefore it continues to make sense for shareholder-capital-at-risk to be used for capital allocation purposes.

Determining risk appetite A bank must limit the amount it is prepared to lose under extreme market circumstances; this can be done using stress test limits. As with all limit setting, the process should start with the identification of the bank’s risk appetite. If we start with the premise that a bank’s risk appetite is expressed as a monetary amount, let us say $10 million, then a natural question follows. Are you prepared to lose $10 million every day, once per month, or how often? The regularity with which a loss of a given magnitude can be tolerated is the key qualification of risk appetite. Figure 8.10 shows how a bank’s risk appetite could be defined. Several different losses are identified, along with the frequency with which each loss can be tolerated. The amount a bank is prepared to lose on a regular basis is defined as the 95%

Figure 8.10 Deﬁning risk appetite.

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confidence VaR (with a one-day holding period). The daily VaR limit should be set after consideration of the bank’s, or trading unit’s profit target. There would be little point in having a daily VaR limit that is larger than the annual profit target, otherwise there would be a reasonable probability that the budgeted annual profit would be lost during the year. The second figure is the most important in terms of controlling risk. An institution’s management will find it difficult to discuss the third figure – the extreme tolerance number. This is because it is unlikely that any of the senior management team have ever experienced a loss of that magnitude. To facilitate a meaningful discussion the extreme loss figure to be used to control risk must be of a magnitude that is believed possible, even if improbable. The second figure is the amount the bank is prepared to lose on an infrequent basis, perhaps once every two or three years. This amount should be set after consideration of the available or allocated capital. This magnitude of loss would arise from an extreme event in the financial markets and will therefore not be predicted by VaR. Either historic price changes or extreme value theory could be used to predict the magnitude. The bank’s actual capital must more than cover this figure. Stress tests should be used to identify scenarios that would give rise to losses of this magnitude or more. Once the scenarios have been identified, management’s subjective judgement must be used, in conjunction with statistical analysis, to judge the likelihood of such an event in prevailing market conditions. Those with vested interests should not be allowed to dominate this process. The judgement will be subjective, as the probabilities of extreme events are not meaningful over the short horizon associated with trading decisions. In other words the occurrence of extreme price shocks is so rare that a consideration of the probability of an extreme event would lead managers to ignore such events. EVT could be used in conjunction with management’s subjective judgement of the likelihood of such an event, given present or predicted economic circumstance. The loss labelled ‘extreme tolerance’ does not have an associated frequency, as it is the maximum the bank is prepared to lose – ever. This amount, depending on the degree of leverage, is likely to be between 10% and 20% of a bank’s equity capital and perhaps equates to a 1 in 50-year return level. Losses greater than this would severely impact the bank’s ability to operate effectively. Again stress tests should be used to identify scenarios and position sizes that would give rise to a loss of this magnitude. When possible (i.e. for single assets or indices) EVT should then be used to estimate the probability of such an event.

Stress test limits A bank must limit the amount it is prepared to lose due to extreme market moves, this is best achieved by stress test limits. As VaR only controls day-to-day risk, stress test limits are required in addition to VaR limits. Stress test limits are entirely separate from VaR limits and can be used in a variety of ways (see below). However they are used, it is essential to ensure that stress test limits are consistent with the bank’s VaR risk management limits, i.e. the stress test limits should not be out of proportion with the VaR limits. Stress test limits should be set at a magnitude that is consistent with the ‘occasional loss’ figure from Figure 8.10, above. However, significantly larger price shocks should also be tested to ensure that the ‘extreme tolerance number is not breached’.

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Stress test limits are especially useful for certain classes of products, particularly options. Traditional limits for options were based around the greeks; delta, gamma, vega, rho and theta. A single matrix of stress tests can replace the first three greeks. The advantage of stress tests over the greeks is that stress tests quantify the loss on a portfolio in a given market scenario. The greeks, particularly, gamma, can provide misleading figures. When options are at-the-money and close to expiry gamma can become almost infinitely large. This has nothing to do with potential losses and everything to do with the option pricing function (Black–Scholes). Figure 8.11 gives an example of stress limits for an interest rate option portfolio. This example of stress test limits could be used for the matrix of stress tests given above in Table 8.3.

Figure 8.11 Stress test limits for an interest option portfolio.

Figure 8.11 shows three stress test limits, which increase in magnitude with the size of the shift in interest rates and the change in volatility. Stress tests limits like this make it easy for trading management to see that losses due to specified ranges of market shifts are limited to a given figure. Using the greeks, the loss caused by specific market shifts is not specified (except for the tiny shifts used by the greeks). Stress test limits can be used as are standard VaR, or other, risk limits, i.e. when a stress test identifies that a portfolio could give rise to a loss greater than specified by the stress test limit, then exposure cannot be increased and must be decreased. This approach establishes stress test limits as ‘hard’ limits and therefore, along with standard risk limits, as absolute constraints on positions and exposures that can be created. Another approach is to set stress test limits but use them as ‘trigger points’ for discussion. Such limits need to be well within the bank’s absolute tolerance of loss. When a stress test indicates that the bank’s portfolio could give rise to a specified loss, the circumstances that would cause such a loss are distributed to senior management, along with details of the position or portfolio. An informed discussion can then take place as to whether the bank is happy to run with such a risk. Although EVT and statistics can help, the judgement will be largely subjective and will be based on the experience of the management making the decision.

Conclusion Due to the extreme price shocks experienced regularly in the world’s financial markets VaR is not an adequate measure of risk. Stress testing must be used to complement VaR. The primary objective of stress testing is to identify the scenarios

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that would cause a significant loss and to put a limit on risk exposures that would cause such losses. Stress testing must be undertaken in a systematic way. Ad-hoc scenario tests may produce interesting results but are unlikely to identify the worst-case loss a bank could suffer. Care must be taken to identify the stress tests required by examining the types of risk in the bank’s portfolio. Stress tests should be run daily as a bank’s portfolio can change significantly over a 24-hour period. A bank’s risk appetite should be set with reference to VaR and to the worst-case loss a bank is prepared to countenance under extreme market conditions. This is best done with reference to the frequency with which a certain loss can be tolerated. Stress test limits can then be established to ensure that the bank does not create positions that could give rise, in severe market circumstances, to a loss greater than the bank’s absolute tolerance of loss. Stress testing should be an integral part of a bank’s risk management framework and stress test limits should be used along side other risk limits, such as VaR limits.

Appendix: The theory of extreme value theory – an introduction © Con Keating In 1900 Bachelier introduced the normal distribution to financial analysis (see also Cootner, 1964); today most students of the subject would be able to offer a critique of the shortcomings of this most basic (but useful) model. Most would point immediately to the ‘fat tails’ evident in the distributions of many financial time series. Benoit Mandelbrot (1997), now better known for his work on fractals, and his doctoral student Eugene Fama published extensive studies of the empirical properties of the distributions of a wide range of financial series in the 1960s and 1970s which convincingly demonstrate this non-normality. Over the past twenty years, both academia and the finance profession have developed a variety of new techniques, such as the ARCH family, to simulate the observed oddities of actual series. The majority fall short of delivering an entirely satisfactory result. At first sight the presence of skewness or kurtosis in the distributions suggests that of a central limit theorem failing but, of course, the central limit theorem should only be expected to apply strongly to the central region, the kernel of the distribution. Now this presents problems for the risk manager who naturally is concerned with the more unusual (or extreme) behavior of markets, i.e. the probability and magnitudes of the events forming the tails of the distributions. There is also a common misunderstanding that the central limit theorem implies that any mixture of distributions or samplings from a distribution will result in a normal distribution, but a further condition exists, which often passes ignored, that these samplings should be independent. Extreme value theory (EVT) is precisely concerned with the analysis of tail behaviour. It has its roots in the work of Fisher and Tippett first published in 1928 and a long tradition of application in the fields of hydrology and insurance. EVT considers the asymptotic (limiting) behavior of series and, subject to the assumptions listed below, states lim P

nê

1 (Xn ñbn )Ox ó lim F n (an xòbn )óH(x) nê an

(A1)

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which implies that: F é MDA(Hm (x)) for some m

(A2)

which is read as: F is a realization in the maximum domain of attraction of H. To illustrate the concept of a maximum domain of attraction, consider a fairground game: tossing ping-pong balls into a collection of funnels. Once inside a funnel, the ball would descend to its tip – a point attractor. The domain of attraction is the region within a particular funnel and the maximum domain of attraction is any trajectory for a ping-pong ball which results in its coming to rest in a particular funnel. This concept of a stable, limiting, equilibrium organization to which dynamic systems are attracted is actually widespread in economics and financial analysis. The assumptions are that bn and an [0 (location and scaling parameters) exist such that the financial time series, X, demonstrates regular limiting behavior and that the distribution is not degenerate. These are mathematical technicalities necessary to ensure that we do not descend inadvertently into paradox and logical nonsenses. m is referred to as a shape parameter. There is (in the derivation of equation (A1)) an inherent assumption that the realisations of X, x0 , x1 , . . . , xn are independently and identically distributed. If this assumption were relaxed, the result, for serially dependent data, would be slower convergence to the asymptotic limit. The distribution Hm (x) is defined as the generalized extreme value distribution (GEV) and has the functional form: Hm(x)ó

exp(ñ(1òmx) ñ1/m )

exp(ñe ñx )

for mÖ0, mó0 respectively

(A3)

The distributions where the value of the tail index, m, is greater than zero, equal to zero or less than zero are known, correspondingly, as Fre´ chet, Gumbel and Weibull distributions. The Fre´ chet class includes Student’s T, Pareto and many other distributions occasionally used in financial analysis; all these distributions have heavy tails. The normal distribution is a particular instance of the Gumbel class where m is zero. This constitutes the theory underlying the application of EVT techniques but it should be noted that this exposition was limited to univariate data.10 Extensions of EVT to multivariate data are considerably more intricate involving measure theory, the theory of regular variations and more advanced probability theory. Though much of the multivariate theory does not yet exist, some methods based upon the use of copulas (bivariate distributions whose marginal distributions are uniform on the unit interval) seem promising. Before addressing questions of practical implementation, a major question needs to be considered. At what point (which quantile) should it be considered that the asymptotic arguments or the maximum domain of attraction applies? Many studies have used the 95th percentile as the point beyond which the tail is estimated. It is far from clear that the arguments do apply in this still broad range and as yet there are no simulation studies of the significance of the implicit approximation of this choice. The first decision when attempting an implementation is whether to use simply the ordered extreme values of the entire sample set, or to use maxima or minima in defined time periods (blocks) of, say, one month or one year. The decision trade-off is the number of data points available for the estimation and fitting of the curve

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parameters versus the nearness to the i.i.d. assumption. Block maxima or minima should be expected to approximate an i.i.d. series more closely than the peaks over a threshold of the whole series but at the cost of losing many data-points and enlarging parameter estimation uncertainty. This point is evident from examination of the following block maxima and peaks over threshold diagrams. Implementation based upon the whole data series, usually known as peaks over threshold (POT), uses the value of the realization (returns, in most financial applications) beyond some (arbitrarily chosen) level or threshold. Anyone involved in the insurance industry will recognize this as the liability profile of an unlimited excess of loss policy. Figures 8A.1 and 8A.2 illustrate these two approaches:

Figure 8A.1 25-Day block minima.

Figure 8A.2 Peaks over thresholds.

Figure 8A.1 shows the minimum values in each 25-day period and may be compared with the whole series data-set below. It should be noted that both series are highly autocorrelated and therefore convergence to the asymptotic limit should be expected to be slow. Figure 8A.2 shows the entire data-set and the peaks under an arbitrary value (1.5). In this instance, this value has clearly been chosen too close to the mean of the distribution – approximately one standard deviation. In a recent paper, Danielsson and De Vries (1997) develop a bootstrap method for the automatic choice of this cutoff point but as yet, there is inadequate knowledge of the performance of small sample estimators. Descriptive statistics of the two (EVT) series are given in Table 8A.1.

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604 ñ2.38 ñ0.00203 ñ0.87826 0.511257 ñ1.25063 ñ0.885 ñ0.42 ñ0.8293 ñ0.11603

Minima 108 ñ3.88 3.7675 ñ0.15054 1.954465 ñ1.6482 ñ0.47813 1.509511 ñ1.12775 0.052863

Notice that the data-set for estimation in the case of block minima has declined to just 108 observations and further that neither series possesses ‘fat tails’ (positive kurtosis). Figure 8A.3 presents these series.

Figure 8A.3 Ordered 25-day block minima and peaks over (1.5) threshold.

The process of implementing EVT is first to decide which approach, then the level of the tail boundary and only then to fit a parametric model of the GEV class to the processed data. This parametric model is used to generate values for particular VaR quantiles. It is standard practice to fit generalized Pareto distributions (GPD) to POT data: GPDm,b (x)ó

1ñ(1òmx/b) ñ1/m

for mÖ0

1ñexp(ñx/b)

for mó0

(A4)

where b[0 is a scaling parameter. There is actually a wide range of methods, which may be used to estimate the parameter values. For the peaks over threshold approach it can be shown that the tail estimator is:

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ˆ (x)ó1ñ nu 1òmˆ xñu F N bˆ

ñ1/mˆ

(A5)

where n is the number of observations in the tail, u is the value of the threshold observation and the ‘hatted’ parameters b, m are the estimated values. These latter are usually derived using maximum likelihood (MLE) from the log likelihood function, which can be shown to be: n

Ln (m, b)óñn ln(b)ò ; ln 1òm ió1

xñu b

ñ1/m

ñln 1òm

xñu b

(A6)

omitting the u and x subscripts. The numerical MLE solution should not prove problematic provided m[ñ 21 which should prove the case for most financial data. A quotation from R. L. Smith is appropriate: ‘The big advantage of maximum likelihood procedures is that they can be generalized, with very little change in the basic methodology, to much more complicated models in which trends or other effects may be present.’ Estimation of the parameters may also be achieved by either linear (see, for example, Kearns and Pagan, 1997) or non-linear regression after suitable algebraic manipulation of the distribution function. It should be immediately obvious that there is one potential significant danger for the risk manager in using EVT; that the estimates of the parameters introduce error non-linearly into the estimate of the VaR quantile. However, by using profile likelihood, it should be possible to produce confidence intervals for these estimates, even if the confidence interval is often unbounded. Perhaps the final point to make is that it becomes trivial to estimate the mean expected loss beyond VaR in this framework; that is, we can estimate the expected loss given a violation of the VaR limit – an event which can cause changes in management behavior and cost jobs. This brief appendix has attempted to give a broad overview of the subject. Of necessity, it has omitted some of the classical approaches such as Pickand’s and Hill’s estimators. An interested reader would find the introductory texts10 listed far more comprehensive. There has been much hyperbole surrounding extreme value theory and its application to financial time series. The reality is that more structure (ARCH, for example) needs to be introduced into the data-generating processes before it can be said that the method offers significant advantages over conventional methods. Applications, however, do seem most likely in the context of stress tests of portfolios.

Acknowledgements Certain sections of this chapter were drawn from Implementing Value at Risk, by Philip Best, John Wiley, 1998. John Wiley’s permission to reproduce these sections is kindly acknowledged. The author would also like to thank Con Keating for his invaluable assistance in reviewing this chapter and for writing the appendix on Extreme Value Theory. This chapter also benefited from the comments of Gurpreet Dehal and Patricia Ladkin.

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Notes 1

Note that observing other market parameters, such as the volatility of short-term interest rates, might have warned the risk manager that a currency devaluation was possible. Observed by a central risk management function in a different country, however, the chances of spotting the danger are much reduced. 2 That is, twenty times the return volatility prior to the crisis. 3 Z score of binomial distribution of exceptions: 1.072, i.e. the VaR model would not be rejected by a Type I error test. 4 Extreme price changes that have an almost infinitesimally small probability in a normal distribution but which we know occur with far greater regularity in financial markets. 5 For a more comprehensive coverage of EVT see Embrechs et al. (1997). 6 This is the number of ways of selecting n assets from a set of 10, all multiplied by the number of scenarios – 69 for this example. 7 Counterparty A’s liquidators would expect the bank to perform on the contracts, thus their value at the time of default would have to be written off. Once written off, of course, there is no potential for future beneficial market moves to improve the situation. 8 Bond price curvature – the slight non-linearity of bond prices for a given change in yield. 9 Note that this is not the same as the group of countries who have chosen to ‘peg’ their currencies to the US dollar. 10 For a more formal and complete introduction to EVT see Embrechs et al. (1997), Reiss and Thomas (1997) and Beirlant et al. (1996). Readers interested in either the rapidly developing multivariate theory or available software should contact the author.

References Bachelier, L. (1900) ‘Theorie de la speculation’, Annales Scientifique de l’Ecole Normale Superieur, 21–86, 111–17. Beirlant, J., Teugels, J. and Vynckier, P. (1996) Practical Analysis of Extreme Values, Leuven University Press. Best, P. (1998) Implementing Value at Risk, John Wiley. Cootner, E. (ed.) (1964) The Random Character of Stock Market Prices, MIT Press. Danielsson, J. and de Vries, C. (1997) Beyond the Sample: Extreme Quantile and Probability Estimation, Tinbergen Institute. Embrechs, P., Kluppelberg, C. and Mikosch, T. (1997) Modelling Extremal Events for Insurance and Finance, Springer-Verlag. Fisher, R. and Tippett, L. (1928) ‘Limiting forms of the frequency distribution of the largest and smallest member of a sample’, Proceedings of the Cambridge Philosophical Society, 24, 180–90. Kearns, P. and Pagan, A. (1997) ‘Estimating the tail density index for financial time series’, Review of Economics and Statistics, 79, 171–5. Mandelbrot, B. (1997) Fractals and Scaling in Finance: Discontinuity, Concentration, Risk, Springer-Verlag. McNeil, A. (1998) Risk, January, 96–100. Reiss, R. and Thomas, M. (1997) Statistical Analysis of Extreme Values, Birhausen.

9

Backtesting MARK DEANS The aim of backtesting is to test the effectiveness of market risk measurement by comparing market risk figures with the volatility of actual trading results. Banks must carry out backtesting if they are to meet the requirements laid down by the Basel Committee on Banking Supervision in the Amendment to the Capital Accord to incorporate market risks (1996a). If the results of the backtesting exercise are unsatisfactory, the local regulator may impose higher capital requirements on a bank. Further, when performed at a business line or trading desk level, backtesting is a useful tool to evaluate risk measurement methods.

Introduction Backtesting is a requirement for banks that want to use internal models to calculate their regulatory capital requirements for market risk. The process consists of comparing daily profit and loss (P&L) figures with corresponding market risk figures over a period of time. Depending on the confidence interval used for the market risk measurement, a certain proportion of the P&L figures are expected to show a loss greater than the market risk amount. The result of the backtest is the number of losses greater than their corresponding market risk figures: the ‘number of exceptions’. According to this number, the regulators will decide on the multiplier used for determining the regulatory capital requirement. Regulations require that backtesting is done at the whole bank level. Regulators may also require testing to be broken down by trading desk (Figure 9.1). When there is an exception, this breakdown allows the source of the loss to be analysed in more detail. For instance, the loss might come from one trading desk, or from the sum of losses across a number of different business areas. In addition to the regulatory requirements, backtesting is a useful tool for evaluating market risk measurement and aggregation methods within a bank. At the whole bank level, the comparison between risk and P&L gives only a broad overall picture of the effectiveness of the chosen risk measurement methods. Satisfactory backtesting results at the aggregate level could hide poor risk measurement methods at a lower level. For instance, risks may be overestimated for equity trading, but underestimated for fixed income trading. Coincidentally, the total risk measured could be approximately correct. Alternatively, risks could be underestimated for each broad risk category (interest rate, equity, FX, and commodity risk), but this fact could be hidden by a very conservative simple sum aggregation method. Backtesting at the portfolio level, rather than just for the whole bank, allows

Figure 9.1

Hierarchy of trading divisions and desks at a typical investment bank.

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individual market risk measurement models to be tested in practice. The lower the level at which backtesting is applied, the more information becomes available about the risk measurement methods used. This allows areas to be identified where market risk is not measured accurately enough, or where risks are being taken that are not detected by the risk measurement system. Backtesting is usually carried out within the risk management department of a bank where risk data is relatively easily obtained. However, P&L figures, often calculated by a business unit control or accounting department, are equally important for backtesting. The requirements of these departments when calculating P&L are different from those of the risk management department. The accounting principle of prudence means that it is important not to overstate the value of the portfolio, so where there is uncertainty about the value of positions, a conservative valuation will be taken. When backtesting, the volatility of the P&L is most important, so capturing daily changes in value of the portfolio is more important than having a conservative or prudent valuation. This difference in aims means that P&L as usually calculated for accounting purposes is often not ideal for backtesting. It may include unwanted contributions from provisions or intraday trading. Also, the bank’s breakdown of P&L by business line may not be the same as the breakdown used for risk management. To achieve effective backtesting, the risk and P&L data must be brought together in a single system. This system should be able to identify exceptions, and produce suitable reports. The data must be processed in a timely manner, as some regulators (e.g. the FSA) require an exception to be reported to them not more than one business day after it occurs. In the last few years, investment banks have been providing an increasing amount of information about their risk management activities in their annual reports. The final part of this chapter reviews the backtesting information given in the annual reports of some major banks.

Comparing risk measurements and P&L Holding period For regulatory purposes, the maximum loss over a 10-business-day period at the 99% confidence level must be calculated. This measurement assumes a static portfolio over the holding period. In a realistic trading environment, however, portfolios usually change significantly over 10 days, so a comparison of 10-day P&L with market risk would be of questionable value. A confidence level of 99% and a holding period of 10 days means that one exception would be expected in 1000 business days (about 4 years). If exceptions are so infrequent, a very long run of data has to be observed to obtain a statistically significant conclusion about the risk measurement model. Because of this, regulators require a holding period of one day to be used for backtesting. This gives an expected 2.5 events per year where actual loss exceeds the market risk figure. Figure 9.2 shows simulated backtesting results. Even with this number of expected events, the simple number of exceptions in one year has only limited power to distinguish between an accurate risk measurement model and an inaccurate one. As noted above, risk figures are often calculated for a holding period of 10 days. For backtesting, risks should ideally be recalculated using a 1-day holding period.

Backtesting graph.

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Figure 9.2

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For the most accurate possible calculation, this would use extreme moves of risk factors and correlations based on 1-day historical moves rather than 10-day moves. Then the risk figures would be recalculated. The simplest possible approach is simply to scale risk figures by the square root of 10. The effectiveness of a simple scaling approach depends on whether the values of the portfolios in question depend almost linearly on the underlying risk factors. For instance, portfolios of bonds or equities depend almost linearly on interest rates or equity prices respectively. If the portfolio has a significant non-linear component (significant gamma risk), the scaling would be inaccurate. For example, the value of a portfolio of equity index options would typically not depend linearly on the value of the underlying equity index. Also, if the underlying risk factors are strongly mean reverting (e.g. spreads between prices of two grades of crude oil, or natural gas prices), 10-day moves and 1-day moves would not be related by the square root of time. In practice, the simple scaling approach is often used. At the whole bank level, this is likely to be reasonably accurate, as typically the majority of the risk of a whole bank is not in options portfolios. Clearly, this would not be so for specialist businesses such as derivative product subsidiaries, or banks with extensive derivative portfolios.

Comparison process Risk reports are based on end-of-day positions. This means that the risk figures give the loss at the chosen confidence interval over the holding period for the portfolio that is held at the end of that business day. With a 1-day holding period, the risk figure should be compared with the P&L from the following business day. The P&L, if unwanted components are removed, gives the change in value from market movements of the portfolio the risk was measured for. Therefore, the risk figures and P&L figures used for comparison must be skewed by 1 business day for meaningful backtesting.

Proﬁt and loss calculation for backtesting When market risk is calculated, it gives the loss in value of a portfolio over a given holding period with a given confidence level. This calculation assumes that the composition of the portfolio does not change during the holding period. In practice, in a trading portfolio, new trades will be carried out. Fees will be paid and received, securities bought and sold at spreads below or above the mid-price, and provisions may be made against possible losses. This means that P&L figures may include several different contributions other than those related to market risk measurement. To compare P&L with market risk in a meaningful way, there are two possibilities. Actual P&L can be broken down so that (as near as possible) only contributions from holding a position from one day to the next remain. This is known as cleaning the P&L. Alternatively, the trading positions from one day can be revalued using prices from the following day. This produces synthetic or hypothetical P&L. Regulators recognize both these methods. If the P&L cleaning is effective, the clean figure should be almost the same as the synthetic figure. The components of typical P&L figures, and how to clean them, or calculate synthetic P&L are now discussed.

Dirty or raw P&L As noted above, P&L calculated daily by the business unit control or accounting department usually includes a number of separate contributions.

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Fees and commissions When a trade is carried out, a fee may be payable to a broker, or a spread may be paid relative to the mid-market price of the security or contract in question. Typically, in a market making operation, fees will be received, and spreads will result in a profit. For a proprietary trading desk, in contrast, fees would usually be paid, and spreads would be a cost. In some cases, fees and commissions are explicitly stated on trade tickets. This makes it possible to separate them from other sources of profit or loss. Spreads, however, are more difficult to deal with. If an instrument is bought at a spread over the mid-price, this is not generally obvious. The price paid and the time of the trade are recorded, but the current mid-price at the time of the trade is not usually available. The P&L from the spread would become part of intraday P&L, which would not impact clean P&L. To calculate the spread P&L separately, the midprice would have to be recorded with the trade, or it would have to be calculated afterwards from tick-by-tick security price data. Either option may be too onerous to be practical. Fluctuations in fee income relate to changes in the volume of trading, rather than to changes in market prices. Market risk measures give no information about risk from changes in fee income, therefore fees and commissions should be excluded from P&L figures used for backtesting. Provisions When a provision is taken, an amount is set aside to cover a possible future loss. For banking book positions that are not marked to market (e.g. loans), provisioning is a key part of the portfolio valuation process. Trading positions are marked to market, though, so it might seem that provisioning is not necessary. There are several situations, however, where provisions are made against possible losses. Ω The portfolio may be marked to market at mid-prices and rates. If the portfolio had to be sold, the bank would only receive the bid prices. A provision of the mid–bid spread may be taken to allow for this. Ω For illiquid instruments, market spreads may widen if an attempt is made to sell a large position. Liquidity provisions may be taken to cover this possibility. Ω High yield bonds pay a substantial spread over risk-free interest rates, reflecting the possibility that the issuer may default. A portfolio of a small number of such bonds will typically show steady profits from this spread with occasional large losses from defaults. Provisions may be taken to cover losses from such defaults. When an explicit provision is taken to cover one of these situations, it appears as a loss. For backtesting, such provisions should be removed from the P&L figures. Sometimes, provisions may be taken by marking the instrument to the bid price or rate, or to an even more conservative price or rate. The price of the instrument may not be marked to market daily. Price testing controls verify that the instrument is priced conservatively, and therefore, there may be no requirement to price the instrument except to make sure it is not overvalued. From an accounting point of view, there is no problem with this approach. However, for backtesting, it is difficult to separate out provisions taken in this way, and recover the mid-market value of the portfolio. Such implicit provisions smooth out fluctuations in portfolio value, and lead to sudden jumps in value when provisions are reevaluated. These jumps may lead to backtesting exceptions despite an accurate risk measurement method. This is illustrated in Figure 9.9 (on p. 281).

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Funding When a trading desk buys a security, it requires funding. Often, funding is provided by the bank’s treasury desk. In this case, it is usually not possible to match up funding positions to trading positions or even identify which funding positions belong to each trading desk. Sometimes, funding costs are not calculated daily, but a monthly average cost of funding is given. In this case, daily P&L is biased upwards if the trading desk overall requires funds, and this is corrected by a charge for funding at the month end. For backtesting, daily funding costs should be included with daily P&L figures. The monthly funding charge could be distributed retrospectively. However, this would not give an accurate picture of when funding was actually required. Also, it would lead to a delay in reporting backtesting exceptions that would be unacceptable to some regulators. Intraday trading Some trading areas (e.g. FX trading) make a high proportion of their profits and losses by trading during the day. Daily risk reports only report the risk from end of day positions being held to the following trading day. For these types of trading, daily risk reporting does not give an accurate picture of the risks of the business. Backtesting is based on daily risk figures and a 1-day holding period. It should use P&L with contributions from intra-day trading removed. The Appendix gives a detailed definition of intra- and interday P&L with some examples. It may be difficult to separate intraday P&L from the general P&L figures reported. For trading desks where intraday P&L is most important, however, it may be possible to calculate synthetic P&L relatively easily. Synthetic P&L is based on revaluing positions from the end of the previous day with the prices at the end of the current day (see below for a full discussion). Desks where intraday P&L is most important are FX trading and market-making desks. For these desks, there are often positions in a limited number of instruments that can be revalued relatively easily. In these cases, calculating synthetic P&L may be a more practical alternative than trying to calculate intraday P&L based on all trades during the day, and then subtracting it from the reported total P&L figure. Realized and unrealized P&L P&L is usually also separated into realized and unrealized P&L. In its current form, backtesting only compares changes in value of the portfolio with value at risk. For this comparison, the distinction between realized and unrealized P&L is not important. If backtesting were extended to compare cash-flow fluctuations with a cash-flow at risk measure, this distinction would be relevant.

Clean P&L Clean P&L for backtesting purposes is calculated by removing unwanted components from the dirty P&L and adding any missing elements. This is done to the greatest possible extent given the information available. Ideally, the clean P&L should not include: Ω Ω Ω Ω

Fees and commissions Profits or losses from bid–mid–offer spreads Provisions Income from intraday trading

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The clean P&L should include: Ω Interday P&L Ω Daily funding costs

Synthetic or hypothetical P&L Instead of cleaning the existing P&L figures, P&L can be calculated separately for backtesting purposes. Synthetic P&L is the P&L that would occur if the portfolio was held constant during a trading day. It is calculated by taking the positions from the close of one trading day (exactly the positions for which risk was calculated), and revaluing these using prices and rates at the close of the following trading day. Funding positions should be included. This gives a synthetic P&L figure that is directly comparable to the risk measurement. This could be written: Synthetic P&LóP0 (t1)ñP0 (t0 ) where P0 (t 0) is the value of the portfolio held at time 0 valued with the market prices as of time 0 P0 (t 1) is the value of the portfolio held at time 0 valued with the market prices as of time 1 The main problem with calculating synthetic P&L is valuing the portfolio with prices from the following day. Some instruments in the portfolio may have been sold, so to calculate synthetic P&L, market prices must be obtained not just for the instruments in the portfolio but for any that were in the portfolio at the end of the previous trading day. This can mean extra work for traders and business unit control or accounting staff. The definition of synthetic P&L is the same as that of interday P&L given in the Appendix.

Further P&L analysis for option books P&L analysis (or P&L attribution) breaks down P&L into components arising from different sources. The above breakdown removes unwanted components of P&L so that a clean P&L figure can be calculated for backtesting. Studying these other components can reveal useful information about the trading operation. For instance, on a market-making desk, does most of the income come from fees and commissions and spreads as expected, or is it from positions held from one day to the next? A change in the balance of P&L from different sources could be used to trigger a further investigation into the risks of a trading desk. The further breakdown of interday P&L is now considered. In many cases, the P&L analysis would be into the same factors as are used for measuring risk. For instance, P&L from a corporate bond portfolio could be broken down into contributions from treasury interest rates, movements in the general level of spreads, and the movements of specific spreads of individual bonds in the portfolio. An equity portfolio could have P&L broken down into one component from moves in the equity index, and another from movement of individual stock prices relative to the index. This type of breakdown allows components of P&L to be compared to general market risk and specific risk separately. More detailed backtesting can then be done to demonstrate the adequacy of specific risk measurement methods.

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P&L for options can be attributed to delta, gamma, vega, rho, theta, and residual terms. The option price will change from one day to the next, and according to the change in the price of the underlying and the volatility input to the model, this change can be broken down. The breakdown for a single option can be written as follows: *có

Lc 1 L2 c Lc Lc Lc *Sò (*S)2 ò *pò *rò *tòResidual LS 2 LS 2 Lp Lr Lt

This formula can also be applied to a portfolio of options on one underlying. For a more general option portfolio, the greeks relative to each underlying would be required. If most of the variation of the price of the portfolio is explained by the greeks, then a risk measurement approach based on sensitivities is likely to be effective. If the residual term is large, however, a full repricing approach would be more appropriate. The breakdown of P&L allows more detailed backtesting to validate risk measurement methods by risk factor, rather than just at an aggregate level. When it is possible to see what types of exposure lead to profits and losses, problems can be identified. For instance, an equity options desk may make profits on equity movements, but losses on interest rate movements.

Regulatory requirements The Basel Committee on Banking Supervision sets out its requirements for backtesting in the document Supervisory framework for the use of ‘backtesting’ in conjunction with the internal models approach to market risk capital requirements (1996b). The key points of the requirements can be summarized as follows: Ω Risk figures for backtesting are based on a 1-day holding period and a 99% confidence interval. Ω A 1-year observation period is used for counting the number of exceptions. Ω The number of exceptions is formally tested quarterly. The committee also urges banks to develop the ability to use synthetic P&L as well as dirty P&L for backtesting. The result of the backtesting exercise is a number of exceptions. This number is used to adjust the multiplier used for calculating the bank’s capital requirement for market risk. The multiplier is the factor by which the market risk measurement is multiplied to arrive at a capital requirement figure. The multiplier can have a minimum value of 3, but under unsatisfactory backtesting results can have a value up to 4. Note that the value of the multiplier set by a bank’s local regulator may also be increased for other reasons. Table 9.1 (Table 2 from Basel Committee on Banking Supervision (1996b)) provides guidelines for setting the multiplier. The numbers of exceptions are grouped into zones. A result in the green zone is taken to indicate that the backtesting result shows no problems in the risk measurement method. A result in the yellow zone is taken to show possible problems. The bank is asked to provide explanations for each exception, the multiplier will probably be increased, and risk measurement methods kept under review. A result in the red zone is taken to mean that there are severe problems with the bank’s risk measurement model or system. Under some circumstances, the local regulator may decide

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The Professional’s Handbook of Financial Risk Management Table 9.1 Guidelines for setting the multiplier Zone

Number of exceptions

Increase in multiplier

Cumulative probability %

Green Green Green Green Green Yellow Yellow Yellow Yellow Yellow Red

0 1 2 3 4 5 6 7 8 9 10 or more

0.00 0.00 0.00 0.00 0.00 0.40 0.50 0.65 0.75 0.85 1.00

8.11 28.58 54.32 75.81 89.22 95.88 98.63 99.60 99.89 99.97 99.99

The cumulative probability column shows the probability of recording at least the number of exceptions shown if the risk measurement method is accurate, and assuming normally distributed P&L ﬁgures.

that there is an acceptable reason for an exception (e.g. a sudden increase in market volatilities). Some exceptions may then be disregarded, as they do not indicate problems with risk measurement. Local regulations are based on the international regulations given in Basel Committee on Banking Supervision (1996b) but may be more strict in some areas.

FSA regulations The Financial Services Authority (FSA) is the UK banking regulator. Its requirements for backtesting are given in section 10 of the document Use of Internal Models to Measure Market Risks (1998). The key points of these regulations that clarify or go beyond the requirements of the Basel Committee are now discussed. Ω When a bank is first seeking model recognition (i.e. approval to use its internal market risk measurement model to set its market risk capital requirement), it must supply 3 months of backtesting data. Ω When an exception occurs, the bank must notify its supervisor orally by close of business two working days after the loss is incurred. Ω The bank must supply a written explanation of exceptions monthly. Ω A result in the red zone may lead to an increase in the multiplication factor greater than 1, and may lead to withdrawal of model recognition. The FSA also explains in detail how exceptions may be allowed to be deemed ‘unrecorded’ when they do not result from deficiencies in the risk measurement model. The main cases when this may be allowed are: Ω Final P&L figures show that the exception did not actually occur. Ω A sudden increase in market volatility led to exceptions that nearly all models would fail to predict. Ω The exception resulted from a risk that is not captured within the model, but for which regulatory capital is already held.

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Other capabilities that the bank ‘should’ rather than ‘must’ have are the ability to analyse P&L (e.g. by option greeks), and to split down backtesting to the trading book level. The bank should also be able to do backtesting based on hypothetical P&L (although not necessarily on a daily basis), and should use clean P&L for its daily backtesting.

EBK regulations The Eidgeno¨ ssische Bankenkommission, the Swiss regulator, gives its requirements in the document Richtlinien zur Eigenmittelunterlegung von Marktrisiken (Regulations for Determining Market Risk Capital) (1997). The requirements are generally closely in line with the Basel Committee requirements. Reporting of exceptions is on a quarterly basis unless the number of exceptions is greater than 4, in which case, the regulator must be informed immediately. The bank is free to choose whether dirty, clean, or synthetic P&L are used for backtesting. The chosen P&L, however, must be free from components that systematically distort the backtesting results.

Backtesting to support speciﬁc risk measurement In September 1997, the Basel Committee on Banking Supervision released a modification (1997a,b) to the Amendment to the Capital Accord to include market risks (1996) to allow banks to use their internal models to measure specific risk for capital requirements calculation. This document specified additional backtesting requirements to validate specific risk models. The main points were: Ω Backtesting must be done at the portfolio level on portfolios containing significant specific risk. Ω Exceptions must be analysed. If the number of exceptions falls in the red zone for any portfolio, immediate action must be taken to correct the model. The bank must demonstrate that it is setting aside sufficient capital to cover extra risk not captured by the model. FSA and EBK regulations on the backtesting requirements to support specific risk measurement follow the Basel Committee paper very closely.

Beneﬁts of backtesting beyond regulatory compliance Displaying backtesting data Stating a number of exceptions over a given period gives limited insight into the reliability of risk and P&L figures. How big were the exceptions? Were they closely spaced in time, or separated by several weeks or months? A useful way of displaying backtesting data is the backtesting graph (see Figure 9.2). The two lines represent the 1-day 99% risk figure, while the columns show the P&L for each day. The P&L is shifted in time relative to the risk so that the risk figure for a particular day is compared with the P&L for the following trading day. Such a graph shows not only how many exceptions there were, but also their timing and magnitude. In addition, missing data, or unchanging data can be easily identified. Many banks show a histogram of P&L in their annual report. This does not directly compare P&L fluctuations with risk, but gives a good overall picture of how P&L was

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distributed over the year. Figure 9.3 shows a P&L histogram that corresponds to the backtesting graph in Figure 9.2.

Analysis of backtesting graphs Backtesting graphs prepared at a trading desk level as well as a whole bank level can make certain problems very clear. A series of examples shows how backtesting graphs can help check risk and P&L figures in practice, and reveal problems that may not be easily seen by looking at separate risk and P&L reports. Most of the examples below have been generated synthetically using normally distributed P&L, and varying risk figures. Figure 9.5 uses the S&P 500 index returns and risk of an index position. Risk measured is too low When there are many exceptions, the risk being measured is too low (Figures 9.4 and 9.5). Possible causes There may be risk factors that are not included when risk is measured. For instance, a government bond position hedged by a swap may be considered riskless, when there is actually swap spread risk. Some positions in foreign currency-denominated bonds may be assumed to be FX hedged. If this is not so, FX fluctuations are an extra source of risk. Especially if the problem only shows up on a recent part of the backtesting graph, the reason may be that volatilities have increased. Extreme moves used to calculate risk are estimates of the maximum moves at the 99% confidence level of the underlying market prices or rates. Regulatory requirements specify a long observation period for extreme move calculation (at least one year). This means that a sharp increase in volatility may not affect the size of extreme moves used for risk measurement much even if these are recalculated. A few weeks of high volatility may have a relatively small effect on extreme moves calculated from a two-year observation period. Figure 9.5 shows the risk and return on a position in the S&P 500 index. The risk figure is calculated using two years of historical data, and is updated quarterly. The period shown is October 1997 to October 1998. Volatility in the equity markets increased dramatically in September and October 1998. The right-hand side of the graph shows several exceptions as a result of this increase in volatility. The mapping of business units for P&L reporting may be different from that used for risk reporting. If extra positions are included in the P&L calculation that are missing from the risk calculation, this could give a risk figure that is too low to explain P&L fluctuations. This is much more likely to happen at a trading desk level than at the whole bank level. This problem is most likely to occur for trading desks that hold a mixture of trading book and banking book positions. Risk calculations may be done only for the trading book, but P&L may be calculated for both trading and banking book positions. Solutions To identify missing risk factors, the risk measurement method should be compared with the positions held by the trading desk in question. It is often helpful to discuss sources of risk with traders, as they often have a good idea of where the main risks

Figure 9.3

P&L distribution histogram.

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Backtesting graph – risk too low.

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Figure 9.4

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Figure 9.5

Backtesting graph – volatility increase.

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of their positions lie. A risk factor could be missed if instruments’ prices depend on a factor outside the four broad risk categories usually considered (e.g. prices of mortgage backed securities depend on real estate values). Also, positions may be taken that depend on spreads between two factors that the risk measurement system does not distinguish between (e.g. a long and a short bond position both fall in the same time bucket, and appear to hedge each other perfectly). When volatility increases suddenly, a short observation period could be substituted for the longer observation period usually used for calculating extreme moves. Most regulators allow this if the overall risk figure increases as a result. Mapping of business units for P&L and risk calculation should be the same. When this is a problem with banking and trading book positions held for the same trading desk, P&L should be broken down so that P&L arising from trading book positions can be isolated. Risk measured is too high There are no exceptions, and the P&L figures never even get near the risk figures (Figure 9.6). Possible causes It is often difficult to aggregate risk across risk factors and broad risk categories in a consistent way. Choosing too conservative a method for aggregation can give risk figures that are much too high. An example would be using a simple sum across delta, gamma, and vega risks, then also using a simple sum between interest rate, FX, and equity risk. In practice, losses in these markets would probably not be perfectly correlated, so the risk figure calculated in this way would be too high. A similar cause is that figures received for global aggregation may consist of sensitivities from some business units, but risk figures from others. Offsetting and diversification benefits between business units that report only total risk figures cannot be measured, so the final risk figure is too high. Solutions Aggregation across risk factors and broad risk categories can be done in a number of ways. None of these is perfect, and this article will not discuss the merits of each in detail. Possibilities include: Ω Historical simulation Ω Constructing a large correlation matrix including all risk factors Ω Assuming zero correlation between broad risk categories (regulators would require quantitative evidence justifying this assumption) Ω Assuming some worst-case correlation (between 1 and 0) that could be applied to the risks (rather than the sensitivities) no matter whether long or short positions were held in each broad risk category. To gain full offsetting and diversification benefits at a global level, sensitivities must be collected from all business units. If total risks are reported instead, there is no practical way of assessing the level of diversification or offsetting present. P&L has a positive bias There may be exceptions on the positive but not the negative side. Even without exceptions, the P&L bars are much more often positive than negative (Figure 9.7).

Figure 9.6

Backtesting graph – risk too high.

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Backtesting graph – positive P&L bias.

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Figure 9.7

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Possible causes A successful trading desk makes a profit during the year, so unless the positive bias is very strong, the graph could be showing good trading results, rather than identifying a problem. The P&L figures may be missing funding costs or including a large contribution from fees and commissions. A positive bias is especially likely for a market making desk, or one that handles a lot of customer business where profits from this are not separated from P&L arising from holding nostro positions. Solutions P&L should include daily funding costs, and exclude income from fees and commissions. Risk ﬁgures are not being recalculated daily The risk lines on the backtesting graph show flat areas (Figure 9.8). Possible causes Almost flat areas indicating no change in risk over a number of days could occur for a proprietary trading desk that trades only occasionally, specialising in holding positions over a period of time. However, even in such a situation, small changes in risk would usually be expected due to change in market prices and rates related to the instruments held. Backtesting is often done centrally by a global risk management group. Sometimes risk figures from remote sites may be sent repeatedly without being updated, or if figures are not available, the previous day’s figures may be used. On a backtesting graph the unchanging risk shows up clearly as a flat area. Solutions First it is necessary to determine if the flat areas on the graph are from identical risk figures, or just ones that are approximately equal. Risk figures that are identical from one day to the next almost always indicate a data problem. The solution is for daily risk figures to be calculated for all trading desks. Instruments are not being marked to market daily P&L is close to zero most of the time, but shows occasional large values (Figure 9.9). Possible causes The portfolio may include illiquid instruments for which daily market prices are not available. Examples of securities that are often illiquid are corporate bonds, emerging market corporate bonds and equities, and municipal bonds. Dynamically changing provisions may be included in the P&L figures. This can smooth out fluctuations in P&L, and mean that a loss is only shown when the provisions have been used up. Including provisions can also lead to a loss being shown when a large position is taken on, and a corresponding provision is taken. In securitization, the assets being securitized may not be marked to market. P&L may be booked only when realized, or only when all asset backed securities have been sold. If the bank invests in external funds, these may not be marked to market daily.

Backtesting graph – risk values not updated.

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Figure 9.8

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Figure 9.9

Backtesting graph – smooth P&L due to provisioning or infrequent valuation.

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However, such investments would not normally form part of the trading book. It is not usually possible to measure market risk in any meaningful way for such funds, because as an external investor, details of the funds’ positions would not be available. Solutions Illiquid instruments should be priced using a model where possible. For example, an illiquid corporate bond could be priced using a spread from the treasury yield curve for the appropriate currency. If the spread of another bond from the same issuer was available, this could be used as a proxy spread to price the illiquid bond. A similar approach could be used for emerging market corporate bonds. Emerging market equities could be assigned a price using a beta estimate, and the appropriate equity index. These last two methods would only give a very rough estimate of the values of the illiquid securities. However, such estimates would work better in a backtesting context than an infrequently updated price. Where possible, provisions should be excluded from P&L used for backtesting. Risk measurement methods were changed When risk measurement methods are changed, the backtesting graph may have a step in it where the method changes (Figure 9.10). This is not a problem in itself. For example, if a simple sum aggregation method was being used across some risk factors, this might prove too conservative. A correlation matrix may be introduced to make the risk measurement more accurate. The backtesting graph would then show a step change in risk due to the change in method. It is useful to be able to go back and recalculate the risk using the new method for old figures. Then a backtesting graph could be produced for the new method. Backtesting like this is valuable for checking and gaining regulatory approval for a new risk measurement method. Note that a step change in risk is also likely when extreme moves are updated infrequently (e.g. quarterly). This effect can be seen clearly in Figure 9.5, where the extreme move used is based on the historical volatility of the S&P 500 index.

P&L histograms Backtesting graphs clearly show the number of exceptions, but it is difficult to see the shape of the distribution of P&L outcomes. A P&L histogram (Figure 9.3) makes it easier to see if the distribution of P&L is approximately normal, skewed, fat-tailed, or if it has other particular features. Such a histogram can give additional help in diagnosing why backtesting exceptions occurred. The weakness of such histograms is that they show the distribution of P&L arising from a portfolio that changes with time. Even if the underlying risk factors were normally distributed, and the prices of securities depended linearly on those risk factors, the changing composition of the portfolio would result in a fat-tailed distribution. A histogram showing the P&L divided by the risk figure gives similar information to a backtesting graph. However, it is easier to see the shape of the distribution, rather than just the number of exceptions. Figure 9.11 shows an example. This graph can be useful in detecting a fat-tailed distribution that causes more exceptions than expected.

Systems requirements A backtesting system must store P&L and risk data, and be able to process it into a suitable form. It is useful to be able to produce backtesting graphs, and exception

Figure 9.10

Backtesting graph – risk measurement methods changed.

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P&L/risk distribution histogram.

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Figure 9.11

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statistics. The following data should be stored: Ω P&L figures broken down by: – Business unit (trading desk, trading book) – Source of P&L (fees and commissions, provisions, intraday trading, interday trading) Ω Risk figures broken down by: – Business unit The backtesting system should be able at a minimum to produce backtesting graphs, and numbers of exceptions at each level of the business unit hierarchy. The system should be able to process information in a timely way. Data must be stored so that at least 1 year’s history is available.

Review of backtesting results in annual reports Risk management has become a focus of attention in investment banking over the last few years. Most annual reports of major banks now have a section on risk management covering credit and market risk. Many of these now include graphs showing the volatility of P&L figures for the bank, and some show backtesting graphs. Table 9.2 shows a summary of what backtesting information is present in annual reports from a selection of banks. Table 9.2 Backtesting information in annual reports

Company

Date

Risk management section

Dresdner Bank Merrill Lynch Deutsche Bank J. P. Morgan Lehman Brothers ING Group ABN AMRO Holding Credit Suisse Group Sanwa Bank

1997 1997 1997 1997 1997 1997 1997 1997 1998

Yes Yes Yes Yes Yes Yes Yes Yes Yes

P&L graph

Backtesting graph

No Yesa No Yes Yesd No No Yes Noe

No No Nob Noc No No No Yes Yes

a

Merrill Lynch’s P&L graph is of weekly results. It shows 3 years’ results year by year for comparison. Deutsche Bank show a graph of daily value at risk c J. P. Morgan gives a graph of Daily Earnings at Risk (1-day holding period, 95% conﬁdence interval) for two years. The P&L histogram shows average DEaR for 1997, rebased to the mean daily proﬁt. d Lehman Brothers graph is of weekly results. e Sanwa Bank also show a scatter plot with risk on one axis and P&L on the other. A diagonal line indicates the conﬁdence interval below which a point would be an exception. b

Of the banks that compare risk to P&L, J. P. Morgan showed a number of exceptions (12 at the 95% level) that was consistent with expectations. They interpret their Daily Earnings at Risk (DEaR) figure in terms of volatility of earnings, and place the confidence interval around the mean daily earnings figure of $12.5 million. The shift of the P&L base for backtesting obviously increases the number of exceptions. It

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compensates for earnings that carry no market risk such as fees and commissions, but also overstates the number of exceptions that would be obtained from a clean P&L figure by subtracting the average profit from that figure. Comparing to the average DEaR could over or understate the number of exceptions relative to a comparison of each day’s P&L with the previous day’s risk figure. Credit Suisse Group show a backtesting graph for their investment bank, Credit Suisse First Boston. This graph plots the 1-day, 99% confidence interval risk figure against P&L (this is consistent with requirements for regulatory reporting). The graph shows no exceptions, and only one loss that even reaches close to half the 1-day, 99% risk figure. The Credit Suisse First Boston annual review for 1997 also shows a backtesting graph for Credit Suisse Financial Products, Credit Suisse First Boston’s derivative products subsidiary. This graph also shows no exceptions, and has only two losses that are around half of the 1-day, 99% risk figure. Such graphs show that the risk figure measured is overestimating the volatility of earnings. However, the graph shows daily trading revenue, not clean or hypothetical P&L prepared specially for backtesting. In a financial report, it may make more sense to show actual trading revenues than a specially prepared P&L figure that would be more difficult to explain. Sanwa Bank show a backtesting graph comparing 1-day, 99% confidence interval risk figures with actual P&L (this is consistent with requirements for regulatory reporting). Separate graphs are shown for the trading and banking accounts. The trading account graph shows only one loss greater than half the risk figure, while the banking account graph shows one exception. The trading account graph shows an overestimate of risk relative to volatility of earnings, while the banking account graph is consistent with statistical expectations The backtesting graphs presented by Credit Suisse Group and Sanwa Bank indicate a conservative approach to risk measurement. There are several good reasons for this: Ω It is more prudent to overestimate, rather than underestimate risk. This is especially so as market risk measurement systems in general do not have several years of proven performance. Ω From a regulatory point of view, overestimating risk is acceptable, whereas an underestimate is not. Ω Risk measurement methods may include a margin for extreme events and crises. Backtesting graphs for 1998 will probably show some exceptions. This review of annual reports shows that all banks reviewed have risk management sections in their annual reports. Backtesting information was only given in a few cases, but some information on volatility of P&L was given in over half the reports surveyed.

Conclusion This chapter has reviewed the backtesting process, giving practical details on how to perform backtesting. The often difficult task of obtaining useful profit and loss figures has been discussed in detail with suggestions on how to clean available P&L figures for backtesting purposes. Regulatory requirements have been reviewed, with specific discussion of the Basel Committee regulations, and the UK (FSA) and Swiss (EBK) regulations. Examples were given of how backtesting graphs can be used to pinpoint problems in P&L and risk calculation. The chapter concluded with a brief overview of backtesting information available in the annual reports of some investment banks.

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Appendix: Intra- and interday P&L For the purposes of backtesting, P&L from positions held from one day to the next must be separated from P&L due to trading during the day. This is because market risk measures only measure risk arising from the fluctuations of market prices and rates with a static portfolio. To make a meaningful comparison of P&L with risk, the P&L in question should likewise be the change in value of a static portfolio from close of trading one day to close of trading the next. This P&L will be called interday P&L. Contributions from trades during the day will be classified as intraday P&L. This appendix aims to give unambiguous definitions for inter- and intraday P&L, and show how they could be calculated for a portfolio. The basic information required for this calculation is as follows: Ω Prices of all instruments in the portfolio at the close of the previous business day. This includes the prices of all OTC instruments, and the price and number held of all securities or exchange traded contracts. Ω Prices of all instruments in the portfolio at the close of the current business day. This also includes the prices of all OTC instruments, and the price and number held of all securities or exchange traded contracts. Ω Prices of all OTC contracts entered into during the day. Price and amount of security traded for all securities trades (including exchange traded contract trades). The definitions shown are for single-security positions. They can easily be extended by summing together P&L for each security to form values for a whole portfolio. OTC contracts can be treated similarly to securities, except that they only have one intraday event. This is the difference between the value when the contract is entered into and its value at the end of that business day. Inter- and intraday P&L for a single-security position can be defined as follows: Interday P&LóN(t0 )(P(t1)ñP(t0 )) where N(t)ónumber of units of security held at time t P(t)óPrice of security at time t t 0 óClose of yesterday t 1 óClose of today This is also the definition of synthetic P&L. Intraday P&L is the total value of the day’s transactions marked to market at the end of the day. For a position in one security, this could be written: No. of trades

Intraday P&Ló(N(t1)ñN(t0 ))P(t1))ñ

;

*Ni Pi

ió1

where *Ni ónumber of units of security bought in trade i Pi óprice paid per unit of security in trade i The first term is the value of net amount of the security bought during the day valued at the end of the day. The second term can be interpreted as the cost of purchase of this net amount, plus any profit or loss made on trades during the day

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which result in no net increase or decrease in the amount of the security held. For a portfolio of securities, the intra- and interday P&L figures are just the sum of those for the individual securities. Examples 1. Hold the same position for one day Size of position Price at close of yesterday Price at close of today Intraday P&L Interday P&L

N(t0) and N(t1) P(t0) P(t1) N(t0)(P(t1)ñP(t0))

1000 units $100.00 $100.15 0 $150

With no trades during the day, there is no intraday P&L. 2. Sell off part of position Size of position at close of yesterday Price at close of yesterday Size of position at close of today Price at close of today Trade 1, sell 500 units at $100.10 Intraday P&L Interday P&L Total P&L

N(t0) P(t0) N(t1) P(t1) *N1 P1 (N(t1)ñN(t0))P(t1)ñ*N1P1 N(t0)(P(t1)ñP(t0)) Intraday P&Lòinterday P&L

1000 units $100.00 500 units $100.15 ñ500 units $100.10 ñ$25 $150 $125

The intraday P&L shows a loss of $25 from selling out the position ‘too soon’. 3. Buy more: increase position Size of position at close of yesterday Price at close of yesterday Size of position at close of today Price at close of today Trade 1, sell 500 units at $100.05 Intraday P&L Interday P&L Total P&L

N(t0) P(t0) N(t1) P(t1) *N1 P1 (N(t1)ñN(t0))P(t1)ñ*N1P1 N(t0)(P(t1)ñP(t0)) Intraday P&Lòinterday P&L

1000 units $100.00 1500 units $100.15 500 units $100.05 $50 $150 $200

Extra profit was generated by increasing the position as the price increased. 4. Buy then sell Size of position at close of yesterday Price at close of yesterday Size of position at close of today Price at close of today

N(t0) P(t0) N(t1) P(t1)

1000 units $100.00 500 units $100.15

Backtesting

Trade 1, sell 500 units at $100.05 Trade 2, sell 1000 units at $100.10 Intraday P&L Interday P&L Total P&L

289

*N1 P1 *N2 P2 (N(t1)ñN(t0))P(t1)ñ*N1P1 ñ*N2P2 N(t0)(P(t1)ñP(t0)) Intraday P&Lòinterday P&L

500 units $100.05 ñ1000 units $100.10 $0 $150 $150

The profit from buying 500 units as the price increased was cancelled by the loss from selling 1000 too soon, giving a total intraday P&L of zero.

References Basel Committee on Banking Supervision (1996a) Amendment to the Capital Accord to incorporate market risks, January. Basel Committee on Banking Supervision (1996b) Supervisory framework for the use of ‘backtesting’ in conjunction with the internal models approach to market risk capital requirements. Financial Services Authority (1998) Use of Internal Models to measure Market Risks, September. Eidgeno¨ ssische Bankenkommission (1997) Richtlinien zur Eigenmittelunterlegung von Marktrisiken. Basel Committee on Banking Supervision (1997a) Modifications to the market risk amendment: Textual changes to the Amendment to the Basel Capital Accord of January 1996, September. Basel Committee on Banking Supervision (1997b) Explanatory Note: Modification of the Basel Accord of July 1988, as amended in January 1996, September.

10

Credit risk management models RICHARD K. SKORA

Introduction Financial institutions are just beginning to realize the benefits of credit risk management models. These models are designed to help the risk manager project risk, measure profitability, and reveal new business opportunities. This chapter surveys the current state of the art in credit risk management models. It provides the reader with the tools to understand and evaluate alternative approaches to modeling. The chapter describes what a credit risk management model should do, and it analyses some of the popular models. We take a high-level approach to analysing models and do not spend time on the technical difficulties of their implementation and application.1 We conclude that the success of credit risk management models depends on sound design, intelligent implementation, and responsible application of the model. While there has been significant progress in credit risk management models, the industry must continue to advance the state of the art. So far the most successful models have been custom designed to solve the specific problems of particular institutions. As a point of reference we refer to several credit risk management models which have been promoted in the industry press. The reader should not interpret this as either an endorsement of these models or as a criticism of models that are not cited here, including this author’s models. Interested readers should pursue their own investigation and can begin with the many references cited below.

Motivation Banks are expanding their operation around the world; they are entering new markets; they are trading new asset types; and they are structuring exotic products. These changes have created new opportunities along with new risks. While banking is always evolving, the current fast rate of change is making it a challenge to respond to all the new opportunities. Changes in banking have brought both good and bad news. The bad news includes the very frequent and extreme banking debacles. In addition, there has been a divergence between international and domestic regulation as well as between regulatory capital and economic capital. More subtly, banks have wasted many valuable

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resources correcting problems and repairing outdated models and methodologies. The good news is that the banks which are responding to the changes have been rewarded with a competitive advantage. One response is the investment in risk management. While risk management is not new, not even in banking, the current rendition of risk management is new. Risk management takes a firmwide view of the institution’s risks, profits, and opportunities so that it may ensure optimal operation of the various business units. The risk manager has the advantage of knowing all the firm’s risks extending across accounting books, business units, product types, and counterparties. By aggregating the risks, the risk manager is in the unique position of ensuring that the firm may benefit from diversification. Risk management is a complicated, multifaceted profession requiring diverse experience and problem-solving skills (see Bessis, 1998). The risk manager is constantly taking on new challenges. Whereas yesterday a risk manager may have been satisfied with being able to report the risk and return characteristics of his firm’s various business units, today he or she is using that information to improve his firm’s business opportunities. Credit risk is traditionally the main risk of banks. Banks are in the business of taking credit risk in exchange for a certain return above the riskless rate. As one would expect, banks deal in the greatest number of markets and types of products. Banks above all other institutions, including corporations, insurance companies, and asset managers, face the greatest challenge in managing their credit risk. One of the credit risk managers’ tools is the credit risk management model.

Functionality of a good credit risk management model A credit risk management model tells the credit risk manager how to allocate scarce credit risk capital to various businesses so as to optimize the risk and return characteristics of the firm. It is important to understand that optimize does not mean minimize risk otherwise every firm would simply invest its capital in riskless assets. Optimize means for any given target return, minimize the risk. A credit risk management model works by comparing the risk and return characteristics between individual assets or businesses. One function is to quantify the diversification of risks. Being well-diversified means that the firm has no concentrations of risk to, say, one geographical location or one counterparty. Figure 10.1 depicts the various outputs from a credit risk management model. The output depicted by credit risk is the probability distribution of losses due to credit risk. This reports for each capital number the probability that the firm may lose that amount of capital or more. For a greater capital number, the probability is less. Of course, a complete report would also describe where and how those losses might occur so that the credit risk manager can take the necessary prudent action. The marginal statistics explain the affect of adding or subtracting one asset to the portfolio. It reports the new risks and profits. In particular, it helps the firm decide whether it likes that new asset or what price it should pay for it. The last output, optimal portfolio, goes beyond the previous two outputs in that it tells the credit risk manager the optimal mix of investments and/or business ventures. The calculation of such an output would build on the data and calculation of the previous outputs. Of course, Figure 10.1 is a wish lists of outputs. Actual models may only produce

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Figure 10.1 Various outputs of a portfolio credit risk management model.

some of the outputs for a limited number of products and asset classes. For example, present technology only allows one to calculate the optimal portfolio in special situations with severe assumptions. In reality, firms attain or try to attain the optimal portfolio through a series of iterations involving models, intuition, and experience. Nevertheless, Figure 10.1 will provide the framework for our discussion. Models, in most general terms, are used to explain and/or predict. A credit risk management model is not a predictive model. It does not tell the credit risk manger which business ventures will succeed and which will fail. Models that claim predictive powers should be used by the firm’s various business units and applied to individual assets. If these models work and the associated business unit consistently exceeds its profit targets, then the business unit would be rewarded with large bonuses and/ or increased capital. Regular success within a business unit will show up at the credit risk management level. So it is not a contradiction that the business unit may use one model while the risk management uses another. Credit risk management models, in the sense that they are defined here, are used to explain rather than predict. Credit risk management models are often criticized for their failure to predict (see Shirreff, 1998). But this is an unfair criticism. One cannot expect these models to predict credit events such as credit rating changes or even defaults. Credit risk management models can predict neither individual credit events nor collective credit events. For example, no model exists for predicting an increase in the general level of defaults. While this author is an advocate of credit risk management models and he has seen many banks realize the benefits of models, one must be cautioned that there

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are risks associated with developing models. At present many institutions are rushing to lay claim to the best and only credit risk management model. Such ambitions may actually undermine the risk management function for the following reasons. First, when improperly used, models are a distraction from the other responsibilities of risk management. In the bigger picture the model is simply a single component, though an important one, of risk management. Second, a model may undermine risk management if it leads to a complacent, mechanical reliance on the model. And more subtly it can stifle competition. The risk manager should have the incentive to innovate just like any other employee.

Review of Markowitz’s portfolio selection theory Harry Markowitz (1952, 1959) developed the first and most famous portfolio selection model which showed how to build a portfolio of assets with the optimal risk and return characteristics. Markowitz’s model starts with a collection of assets for which it is assumed one knows the expected returns and risks as well as all the pair-wise correlation of the returns. Here risk is defined as the standard deviation of return. It is a fairly strong assumption to assume that these statistics are known. The model further assumes that the asset returns are modeled as a standard multivariate normal distribution, so, in particular, each asset’s return is a standard normal distribution. Thus the assets are completely described by their expected return and their pairwise covariances of returns E[ri ] and Covariance(ri , rj )óE[ri rj ]ñE[ri ]E[rj ] respectively, where ri is the random variable of return for the ith asset. Under these assumptions Markowitz shows for a target expected return how to calculate the exact proportion to hold of each asset so as to minimize risk, or equivalently, how to minimize the standard deviation of return. Figure 10.2 depicts the theoretical workings of the Markowitz model. Two different portfolios of assets held by two different institutions have different risk and return characteristics. While one may slightly relax the assumptions in Markowitz’s theory, the assumptions are still fairly strong. Moreover, the results are sensitive to the inputs; two users of the theory who disagree on the expected returns and covariance of returns may calculate widely different portfolios. In addition, the definition of risk as the standard deviation of returns is only reasonable when returns are a multi-normal distribution. Standard deviation is a very poor measure of risk. So far there is no consensus on the right probability distribution when returns are not a multi-normal distribution. Nevertheless, Markowitz’s theory survives because it was the first portfolio theory to quantify risk and return. Moreover, it showed that mathematical modeling could vastly improve portfolio theory techniques. Other portfolio selection models are described in Elton and Gruber (1991).

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Figure 10.2 Space of possible portfolios.

Adapting portfolio selection theory to credit risk management Risk management distinguishes between market risk and credit risk. Market risk is the risk of price movement due either directly or indirectly to changes in the prices of equity, foreign currency, and US Treasury bonds. Credit risk is the risk of price movement due to credit events. A credit event is a change in credit rating or perceived credit rating, which includes default. Corporate, municipal, and certain sovereign bond contain credit risk. In fact, it is sometimes difficult to distinguish between market risk and credit risk. This has led to debate over whether the two risks should be managed together, but this question will not be debated here. Most people are in agreement that the risks are different, and risk managers and their models must account for the differences. As will be seen below, our framework for a credit risk management model contains a market risk component. There are several reasons why Markowitz’s portfolio selection model is most easily applied to equity assets. First, the model is what is called a single-period portfolio model that tells one how to optimize a portfolio over a single period, say, a single day. This means the model tells one how to select the portfolio at the beginning of the period and then one holds the portfolio without changes until the end of the period. This is not a disadvantage when the underlying market is liquid. In this case, one just reapplies the model over successive periods to determine how to manage the

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portfolio over time. Since transaction costs are relatively small in the equity markets, it is possible to frequently rebalance an equity portfolio. A second reason the model works well in the equity markets is that their returns seem to be nearly normal distributions. While much research on equity assets shows that their returns are not perfectly normal, many people still successfully apply Markowitz’s model to equity assets. Finally, the equity markets are very liquid and deep. As such there is a lot of data from which to deduce expected returns and covariances of returns. These three conditions of the equity markets do not apply to the credit markets. Credit events tend to be sudden and result in large price movements. In addition, the credit markets are sometimes illiquid and have large transaction costs. As a result many of the beautiful theories of market risk models do not apply to the credit markets. Since credit markets are illiquid and transactions costs are high, an appropriate single period can be much longer that a single day. It can be as long as a year. In fact, a reasonable holding period for various instruments will differ from a day to many years. The assumption of normality in Markowitz portfolio model helps in another way. It is obvious how to compare two normal distributions, namely, less risk is better than more risk. In the case of, say, credit risk, when distributions are not normal, it is not obvious how to compare two distributions. For example, suppose two assets have probability distribution of losses with the same mean but standard deviations of $8 and $10, respectively. In addition, suppose they have maximum potential losses of $50 and $20, respectively. Which is less risky? It is difficult to answer and depends on an economic utility function for measuring. The theory of utility functions is another field of study and we will not discuss it further. Any good portfolio theory for credit risk must allow for the differences between market and credit risk.

A framework for credit risk management models This section provides a framework in which to understand and evaluate credit risk management models. We will describe all the components of a complete (or nearly complete) credit risk model. Figure 10.3 labels the major components of a credit risk model. While at present there is no model that can do everything in Figure 10.3, this description will be a useful reference by which to evaluate all models. As will be seen below, portfolio models have a small subset of the components depicted in Figure 10.3. Sometimes by limiting itself to particular products or particular applications, a model is able to either ignore a component or greatly simplify it. Some models simply settle for an approximately correct answer. More detailed descriptions and comparisons of some of these models may be found in Gordy (1998), Koyluglu and Hickman (1998), Lopez and Saidenber (1998), Lentino and Pirzada (1998), Locke (1998), and Crouhy and Mark (1998). The general consensus seems to be that we stand to learn much more about credit risk. We have yet to even scratch the surface in bringing high-powered, mathematical techniques to bear on these complicated problems. It would be a mistake to settle for the existing state of the art and believe we cannot improve. Current discussions should promote original, customized solutions and thereby encourage active credit risk management.

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Figure 10.3 Portfolio credit risk model.

Value-at-Risk Before going into more detail about credit risk management models, it would be instructive to say a few words about Value-at-Risk. The credit risk management modeling framework shares many features with this other modeling framework called Value-at-Risk. This has resulted in some confusions and mistakes in the industry, so it is worth-while explaining the relationship between the two frameworks. Notice we were careful to write framework because Value-at-Risk (VaR) is a framework. There are many different implementations of VaR and each of these implementations may be used differently. Since about 1994 bankers and regulators have been using VaR as part of their risk management practices. Specifically, it has been applied to market risk management. The motivation was to compute a regulatory capital number for market risk. Given a portfolio of assets, Value-at-Risk is defined to be a single monetary capital number which, for a high degree of confidence, is an upper bound on the amount of gains or losses to the portfolio due to market risk. Of course, the degree of confidence must be specified and the higher that degree of confidence, the higher the capital number. Notice that if one calculates the capital number for every degree of confidence then one has actually calculated the entire probability distribution of gains or losses (see Best, 1998). Specific implementation of VaR can vary. This includes the assumptions, the model, the input parameters, and the calculation methodology. For example, one implementation may calibrate to historical data and another to econometric data. Both implementations are still VaR models, but one may be more accurate and useful than the other may. For a good debate on the utility of VaR models see Kolman, 1997. In practice, VaR is associated with certain assumptions. For example, most VaR implementations assume that market prices are normally distributed or losses are

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independent. This assumption is based more on convenience than on empirical evidence. Normal distributions are easy to work with. Value-at-Risk has a corresponding definition for credit risk. Given a portfolio of assets, Credit Value-at-Risk is defined to be a single monetary capital number which, for a high degree of confidence, is an upper bound on the amount of gains or losses to the portfolio due to credit risk. One should immediately notice that both the credit VaR model and the credit risk management model compute a probability distribution of gains or losses. For this reason many risk managers and regulators do not distinguish between the two. However, there is a difference between the two models. Though the difference may be more of one of the mind-frame of the users, it is important. The difference is that VaR models put too much emphasis on distilling one number from the aggregate risks of a portfolio. First, according to our definition, a credit risk management model also computes the marginal affect of a single asset and it computes optimal portfolios which assist in making business decisions. Second, a credit risk management model is a tool designed to assist credit risk managers in a broad range of dynamic credit risk management decisions. This difference between the models is significant. Indeed, some VaR proponents have been so driven to produce that single, correct capital number that it has been at the expense of ignoring more important risk management issues. This is why we have stated that the model, its implementation, and their applications are important. Both bankers and regulators are currently investigating the possibility of using the VaR framework for credit risk management. Lopez and Saidenberg (1998) propose a methodology for generating credit events for the purpose of testing and comparing VaR models for calculating regulatory credit capital.

Asset credit risk model The first component is the asset credit risk model that contains two main subcomponents: the credit rating model and the dynamic credit rating model. The credit rating model calculates the credit riskiness of an asset today while the dynamic credit rating model calculates how that riskiness may evolve over time. This is depicted in more detail in Figure 10.4. For example, if the asset is a corporate bond, then the credit riskiness of the asset is derived from the credit riskiness of the issuer. The credit riskiness may be in the form of a probably of default or in the form of a credit rating. The credit rating may correspond to one of the international credit rating services or the institution’s own internal rating system. An interesting point is that the credit riskiness of an asset can depend on the particular structure of the asset. For example, the credit riskiness of a bond depends on its seniority as well as its maturity. (Short- and long-term debt of the same issuer may have different credit ratings.) The credit risk does not necessarily need to be calculated. It may be inputted from various sources or modeled from fundamentals. If it is inputted it may come from any of the credit rating agencies or the institution’s own internal credit rating system. For a good discussion of banks’ internal credit rating models see Treacy and Carey (1998). If the credit rating is modeled, then there are numerous choices – after all, credit risk assessment is as old as banking itself. Two examples of credit rating models are the Zeta model, which is described in Altman, Haldeman, and Narayanan (1977), and the Lambda Index, which is described in Emery and Lyons (1991). Both models are based on the entity’s financial statements.

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Figure 10.4 Asset credit risk model.

Another well-publicized credit rating model is the EDF Calculator. The EDF model is based on Robert Merton’s (1974) observation that a firm’s assets are the sum of its equity and debt, so the firm defaults when the assets fall below the face value of the debt. It follows that debt may be thought of as a short option position on the firm’s assets, so one may apply the Black–Scholes option theory. Of course, real bankruptcy is much more complicated and the EDF Calculator accounts for some of these complications. The model’s strength is that it is calibrated to a large database of firm data including firm default data. The EDF Calculator actually produces a probability of default, which if one likes, can be mapped to discrete credit ratings. Since the EDF model is proprietary there is no public information on it. The interested reader may consult Crosbie (1997) to get a rough description of its workings. Nickell, Perraudin, and Varotto (1998) compare various credit rating models including EDF. To accurately measure the credit risk it is essential to know both the credit riskiness today as well as how that credit riskiness may evolve over time. As was stated above, the dynamic credit rating model calculates how an asset’s credit riskiness may evolve over time. How this component is implemented depends very much on the assets in the portfolio and the length of the time period for which risk is being calculated. But if the asset’s credit riskiness is not being modeled explicitly, it is at least implicitly being modeled somewhere else in the portfolio model, for

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example in a pricing model – changes in the credit riskiness of an asset are reflected in the price of that asset. Of course, changes in credit riskiness of various assets are related. So Figure 10.4 also depicts a component for the correlation of credit rating which may be driven by any number of variables including historical, econometric, or market variables. The oldest dynamic credit rating model is the Markov model for credit rating migration. The appeal of this model is its simplicity. In particular, it is easy to incorporate non-independence of two different firm’s credit rating changes. The portfolio model CreditMetrics (J.P. Morgan, 1997) uses this Markov model. The basic assumption of the Markov model is that a firm’s credit rating migrates at random up or down like a Markov process. In particular, the migration over one time period is independent of the migration over the previous period. Credit risk management models based on a Markov process are implemented by Monte Carlo simulation. Unfortunately, there has been recent research showing that the Markov process is a poor approximation to the credit rating process. The main reason is that the credit rating is influenced by the economy that moves through business cycles. Thus the probability of downgrade and, thus, default is greater during a recession. Kolman (1998) gives a non-technical explanation of this fact. Also Altman, and Kao (1991) mention the shortcomings of the Markov process and propose two alternative processes. Nickell, Perraudin, and Varotto (1998a,b) give a more thorough criticism of Markov processes by using historical data. In addition, the credit rating agencies have published insightful information on their credit rating and how they evolve over time. For example, see Brand, Rabbia and Bahar (1997) or Carty (1997). Another credit risk management model, CreditRiskò, models only two states: nondefault and default (CSFP, 1997). But this is only a heuristic simplification. Rolfes and Broeker (1998) have shown how to enhance CreditRiskò to model a finite number of credit rating states. The main advantage of the CreditRiskò model is that it was designed with the goal of allowing for an analytical implementation as opposed to Monte Carlo. The last model we mention is Portfolio View (McKinsey, 1998). This model is based on econometric models and looks for relationships between the general level of default and economic variables. Of course, predicting any economic variable, including the general level of defaults, is one of the highest goals of research economics. Risk managers should proceed with caution when they start believing they can predict risk factors. As mentioned above, it is the extreme events that most affect the risk of a portfolio of credit risky assets. Thus it would make sense that a model which more accurately measures the extreme event would be a better one. Wilmott (1998) devised such a model called CrashMetrics. This model is based on the theory that the correlation between events is different from times of calm to times of crisis, so it tries to model the correlation during times of crisis. This theory shows great promise. See Davidson (1997) for another discussion of the various credit risk models.

Credit risk pricing model The next major component of the model is the credit risk pricing model, which is depicted in detail in Figure 10.5. This portion of the model together with the market

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Figure 10.5 Credit risk pricing model.

risk model will allow the credit risk management model to calculate the relevant return statistics. The credit risk pricing model is necessary because the price of credit risk has two components. One is the credit rating that was handled by the previous component, the other is the spread over the riskless rate. The spread is the price that the market charges for a particular credit risk. This spread can change without the underlying credit risk changing and is affected by supply and demand. The credit risk pricing model can be based on econometric models or any of the popular risk-neutral pricing models which are used for pricing credit derivatives. Most risk-neutral credit pricing models are transplants of risk-neutral interest rate pricing models and do not adequately account for the differences between credit risk and interest rate risk. Nevertheless, these risk-neutral models seem to be popular. See Skora (1998a,b) for a description of the various risk-neutral credit risk pricing models. Roughly speaking, static models are sufficient for pricing derivatives which do not have an option component and dynamic models are necessary for pricing derivatives which do have an option component. As far as credit risk management models are concerned, they all need a dynamic credit risk term structure model. The reason is that the credit risk management model needs both the expected return of each asset as well as the covariance matrix of returns. So even if one had both the present price of the asset and the forward price, one would still need to calculate the probability distribution of returns. So the credit risk model calculates the credit risky term structure, that is, the yield curve for the various credit risky assets. It also calculates the corresponding term structure for the end of the time period as well as the distribution of the term structure. One way to accomplish this is by generating a sample of what the term structure may look like at the end of the period. Then by pricing the credit risky assets off these various term structures, one obtains a sample of what the price of the assets may be. Since credit spreads do not move independently of one another, the credit risk pricing model, like the asset credit risk model, also has a correlation component. Again depending on the assets in the portfolio, it may be possible to economize and combine this component with the previous one.

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Finally, the choice of inputs can be historical, econometric or market data. The choice depends on how the portfolio selection model is to be used. If one expects to invest in a portfolio and divest at the end of the time period, then one needs to calculate actual market prices. In this case the model must be calibrated to market data. At the other extreme, if one were using the portfolio model to simply calculate a portfolio’s risk or the marginal risk created by purchasing an additional asset, then the model may be calibrated to historical, econometric, or market data – the choice is the risk manager’s.

Market risk pricing model The market risk pricing model is analogous to the credit risk pricing model, except that it is limited to assets without credit risk. This component models the change in the market rates such as credit-riskless, US Treasury interest rates. To price all the credit risky assets completely and accurately it is necessary to have both a market risk pricing model and credit risk pricing model. Most models, including CreditMetrics, CreditRiskò, Portfolio Manager, and Portfolio View, have a dynamic credit rating model but lack a credit risk pricing model and market risk pricing model. While the lack of these components partially cripples some models, it does not completely disable them. As such, these models are best suited to products such as loans that are most sensitive to major credit events like credit rating migration including defaults. Two such models for loans only are discussed in Spinner (1998) and Belkin et al. (1998).

Exposure model The exposure model is depicted in Figure 10.6. This portion of the model aggregates the portfolio of assets across business lines and legal entities and any other appropriate category. In particular, netting across a counterparty would take into account the relevant jurisdiction and its netting laws. Without fully aggregating, the model cannot accurately take into account diversification or the lack of diversification. Only after the portfolio is fully aggregated and netted can it be correctly priced. At this point the market risk pricing model and credit risk pricing model can actually price all the credit risky assets. The exposure model also calculates for each asset the appropriate time period, which roughly corresponds to the amount of time it would take to liquidate the asset. Having a different time period for each asset not only increases the complexity of the model, it also raises some theoretical questions. Should the time period corresponding to an asset be the length of time it takes to liquidate only that asset? To liquidate all the assets in the portfolio? Or to liquidate all the assets in the portfolio in a time of financial crisis? The answer is difficult. Most models simply use the same time period, usually one year, for all exposures. One year is considered an appropriate amount of time for reacting to a credit loss whether that be liquidating a position or raising more capital. There is an excellent discussion of this issue in Jones and Mingo (1998). Another responsibility of the exposure model is to report the portfolio’s various concentrations.

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Figure 10.6 Exposure model and some statistics.

Risk calculation engine The last component is the risk calculation engine which actually calculates the expected returns and multivariate distributions that are then used to calculate the associated risks and the optimal portfolio. Since the distributions are not normal, this portion of the portfolio model requires some ingenuity. One method of calculation is Monte Carlo simulation. This is exemplified in many of the above-mentioned models. Another method of calculating the probability distribution is numerical. One starts by approximating the probability distribution of losses for each asset by a discrete probability distribution. This is a reasonable simplification because one is mainly interested in large, collective losses – not individual firm losses. Once the individual probability distributions have been discretized, there is a well-known computation called convolution for computing the aggregate probability distribution. This numerical method is easiest when the probability distributions are independent – which in this case they are not. There are tricks and enhancements to the convolution technique to make it work for nonindependent distributions. The risk calculation engine of CreditRiskò uses the convolution. It models the nonindependence of defaults with a factor model. It assumes that there is a finite number of factors which describe nonindependence. Such factors would come from the firm’s country, geographical location, industry, and specific characteristics.

Capital and regulation Regulators ensure that our financial system is safe while at the same time that it prospers. To ensure that safety, regulators insist that a bank holds sufficient capital

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Component

Subcomponent

Counterparty credit Fundamental data risk model Credit rating Credit rating model Correlation model Credit risk model Credit risk term structure Dynamic credit risk term structure Recovery model Correlation model Market risk model

Products

Aggregation

Output

Other

Interest rate model Foreign exchange rate model Correlation Loans Bonds Derivatives Structured products Collateral Risk reduction Liquidity Concentration Legal Products Counterparty Business Books Probability distribution Capital Marginal statistics Optimization Stress Scenario

Question Accepts fundamental counterparty, market, or economic data? Computes credit rating? Models evolution of credit rating? Changes in credit rating are non-independent? Accurately constructs credit risk term structure? Robust model of changes in credit risk term structure? Recovery is static or dynamic? Changes in credit risk term structure nonindependent? Robust model of changes in riskless interest rate term structure? Robust model of changes in exchange rates? Market risk and credit risk non-independent? Accepts loans? Accepts bonds? Accepts derivatives? Accepts credit derivatives, credit-linked notes, etc.? Accepts collateral? Models covenants, downgrade triggers, etc.? Accounts for differences in liquidity? Calculates limits? Nets according to legal jurisdiction? Aggregate across products? Aggregate across counterparty? Aggregate across business lines? Aggregate across bank books? Computes cumulative probability distribution of losses? Computes economic capital? Computes marginal statistics for one asset? Computes optimal portfolio? Performs stress tests? Performs scenario tests?

to absorb losses. This includes losses due to market, credit, and all other risks. The proper amount of capital raises interesting theoretical and practical questions. (See, for example, Matten, 1996 or Pratt, 1998.) Losses due to market or credit risk show up as losses to the bank’s assets. A bank should have sufficient capital to absorb not only losses during normal times but also losses during stressful times. In the hope of protecting our financial system and standardizing requirements around the world the 1988 Basel Capital Accord set minimum requirements for calculating bank capital. It was also the intent of regulators to make the rules simple. The Capital Accord specified that regulatory capital is 8% of risk-weighted assets. The risk weights were 100%, 50%, 20%, or 0% depending on the asset. For example, a loan to an OECD bank would have a risk weighting of 20%. Even at the time the

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regulators knew there were shortcomings in the regulation, but it had the advantage of being simple. The changes in banking since 1988 have proved the Capital Accord to be very inadequate – Jones and Mingo (1998) discuss the problems in detail. Banks use exotic products to change their regulatory capital requirements independent of their actual risk. They are arbitraging the regulation. Now there is arbitrage across banking, trading, and counterparty bank books as well as within individual books (see Irving, 1997). One of the proposals from the industry is to allow banks to use their own internal models to compute regulatory credit risk capital similar to the way they use VaR models to compute add-ons to regulatory market risk capital. Some of the pros and cons of internal models are discussed in Irving (1997). The International Swaps and Derivatives Association (1998) has proposed a model. Their main point is that regulators should embrace models as soon as possible and they should allow the models to evolve over time. Regulators are examining ways to correct the problems in existing capital regulation. It is a very positive development that the models, and their implementation, will be scrutinized before making a new decision on regulation. The biggest mistake the industry could make would be to adopt a one-size-fits all policy. Arbitrarily adopting any of these models would certainly stifle creativity. More importantly, it could undermine responsibility and authority of those most capable of carrying out credit risk management.

Conclusion The rapid proliferation of credit risk models, including credit risk management models, has resulted in sophisticated models which provide crucial information to credit risk managers (see Table 10.1). In addition, many of these models have focused attention on the inadequacy of current credit risk management practices. Firms should continue to improve these models but keep in mind that models are only one tool of credit risk management. While many banks have already successfully implemented these models, we are a long way from having a ‘universal’ credit risk management model that handles all the firm’s credit risky assets.

Author’s note This paper is an extension of Richard K. Skora, ‘Modern credit risk modeling’, presented at the meeting of the Global Association of Risk Professionals. 19 October 1998.

Note 1

Of course implementing and applying a model is a crucial step in realizing the benefits of modeling. Indeed, there is a feedback effect, the practicalities of implemention and application affect many decisions in the modeling process.

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References Altman, E., Haldeman, R. G. and Narayanan, P. (1997) ‘ZETA analysis: A new model to identify bankruptcy risk of corporations’, J. Banking and Finance, 1, 29–54. Altman, E. I. and Kao, D. L. (1991) ‘Examining and modeling corporate bond rating drift’, working paper, New York University Salomon Center (New York, NY). Belkin, B., Forest, L., Aguais, S. and Suchower, S. (1998) ‘Expect the unexpected’, CreditRisk – a Risk special report, Risk, 11, No. 11, 34–39. Bessis, J. (1998) Risk Management in Banking, Wiley, Chichester. Best, P. (1998) Implementing Value at Risk, Wiley, Chichester. Brand, L., Rabbia, J. and Bahar, R. (1997) Rating Performance 1996: Stability and Transition, Standard & Poor’s. Carty, L. V. (1997) Moody’s Rating Migration and Credit Quality Correlation, 1920– 1996, Moody’s. Crosbie, P. (1997) Modeling Default Risk, KMV Corporation. Crouhy, M. and Mark, R. (1998) ‘A comparative analysis of current credit risk models’, Credit Risk Modelling and Regulatory Implications, organized by The Bank of England and Financial Services Authority, 21–22 September. Davidson, C. (1997) ‘A credit to the system’, CreditRisk – supplement to Risk, 10, No. 7, July, 61–4. Credit Suisse Financial Products (1997) CreditRiskò. Dowd, K. (1998) Beyond Value at Risk, Wiley, Chichester. Elton, E. J. and Gruber, M. J. (1991) Modern Portfolio Theory and Investment Analysis, fourth edition, Wiley, New York. Emery, G. W. and Lyons, R. G. (1991) ‘The Lambda Index: beyond the current ration’, Business Credit, November/December, 22–3. Gordy, M. B. (1998) ‘A comparative anatomy of credit risk models’, Finance and economics discussion series, Federal Reserve Board, Washington DC. Irving, R. (1997) ‘The internal question’, Credit Risk – supplement to Risk, 10, No. 7, July, 36–8. International Swaps and Derivatives Association (1998) Credit Risk and Regulatory Capital, March. Jones, D. and Mingo, J. (1998) ‘Industry practices in credit risk modeling and internal capital allocations: implications for a models-based regulatory standard’, in Financial Services at the Crossroads: Capital Regulation in the Twenty First Century, Federal Reserve Bank of New York, February. Jorion, P. (1997) Value at Risk, McGraw-Hill, New York. Kolman, J. (1997) ‘Roundtable on the limits of VAR’, Derivatives Strategy, 3, No. 4, April, 14–22. Kolman, J. (1998) ‘The world according to Edward Altman’, Derivatives Strategy, 3, No. 12, 47–51 supports the statement that the models do not try to match reality. Koyluoglu, H. V. and Hickman, A. (1998) ‘Reconcilable differences’, Risk, 11, No. 10, October, 56–62. Lentino, J. V. and Pirzada, H. (1998) ‘Issues to consider in comparing credit risk management models’, J. Lending & Credit Risk Management, 81, No. 4, December, 16–22. Locke, J. (1998) ‘Off-the-peg, off the mark?’ CreditRisk – a Risk special report, Risk, 11, No. 11, November, 22–7. Lopez, J. A. and Saidenberg, M. R. (1998) ‘Evaluating credit risk models’, Credit Risk

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Modelling and Regulatory Implications, organized by The Bank of England and Financial Services Authority, 21–22 September. Markowitz, H. (1952) ‘Portfolio selection’, J. of Finance, March, 77–91. Markowitz, H. (1959) Portfolio Selection, Wiley, New York. Matten, C. (1996) Managing Bank Capital, Wiley, Chichester. Merton, R. C. (1974) ‘On the pricing of corporate debt: the risk structure of interest rates’, Journal of Finance, 29, 449–70. J. P. Morgan (1997) CreditMetrics. McKinsey & Company, Inc. (1998) A Credit Portfolio Risk Measurement & Management Approach. Pratt, S. P. (1998) Cost of Capital, Wiley, New York. Rhode, W. (1998a) ‘McDonough unveils Basel review’, Risk, 11, No. 9, September. Nickell, P., Perraudin, W. and Varotto, S. (1998a) ‘Stability of rating transitions’, Credit Risk Modelling and Regulatory Implications, organized by The Bank of England and Financial Services Authority, 21–22 September. Nickell, P., Perraudin, W. and Varotto, S. (1998b) ‘Ratings-versus equity-based risk modelling’, Credit Risk Modelling and Regulatory Implications, organized by The Bank of England and Financial Services Authority, 21–22 September. Rolfes, B. and Broeker, F. (1998) ‘Good migrations’, Risk, 11, No. 11, November, 72–5. Shirreff, D. (1998) ‘Models get a thrashing’, Euromoney Magazine, October. Skora, R. K. (1998a) ‘Modern credit risk modeling’, presented at the meeting of the Global Association of Risk Professionals, 19 October. Skora, R. K. (1998b) Rational modelling of credit risk and credit derivatives’, in Credit Derivatives – Applications for Risk Management, Investment and Portfolio Optimization, Risk Books, London. Spinner, K. (1998) ‘Managing bank loan risk’, Derivatives Strategy, 3, No. 1, January, 14–22. Treacy, W. and Carey, M. (1998) ‘Internal credit risk rating systems at large U.S. banks’, Credit Risk Modelling and Regulatory Implications, organized by The Bank of England and Financial Services Authority, 21–22 September. Wilmott, P. (1998) CrashMetrics, Wilmott Associates, March.

11

Risk management of credit derivatives KURT S. WILHELM

Introduction Credit risk is the largest single risk in banking. To enhance credit risk management, banks actively evaluate strategies to identify, measure, and control credit concentrations. Credit derivatives, a market that has grown from virtually zero in 1993 to an estimated $350 billion at year end 1998,1 have emerged as an increasingly popular tool. Initially, banks used credit derivatives to generate revenue; more recently, bank usage has evolved to using them as a capital and credit risk management tool. This chapter discusses the types of credit derivative products, market growth, and risks. It also highlights risk management practices that market participants should adopt to ensure that they use credit derivatives in a safe and sound manner. It concludes with a discussion of a portfolio approach to credit risk management. Credit derivatives can allow banks to manage credit risk more effectively and improve portfolio diversification. Banks can use credit derivatives to reduce undesired risk concentrations, which historically have proven to be a major source of bank financial problems. Similarly, banks can assume risk, in a diversification context, by targeting exposures having a low correlation with existing portfolio risks. Credit derivatives allow institutions to customize credit exposures, creating risk profiles unavailable in the cash markets. They also enable creditors to take risk-reducing actions without adversely impacting the underlying credit relationship. Users of credit derivatives must recognize and manage a number of associated risks. The market is new and therefore largely untested. Participants will undoubtedly discover unanticipated risks as the market evolves. Legal risks, in particular, can be much higher than in other derivative products. Similar to poorly developed lending strategies, the improper use of credit derivatives can result in an imprudent credit risk profile. Institutions should avoid material participation in the nascent credit derivatives market until they have fully explored, and developed a comfort level with, the risks involved. Originally developed for trading opportunities, these instruments recently have begun to serve as credit risk management tools. This chapter primarily deals with the credit risk management aspects of banks’ use of credit derivatives. Credit derivatives have become a common element in two emerging trends in how banks assess their large corporate credit portfolios. First, larger banks increasingly devote human and capital resources to measure and model credit portfolio risks more quantitatively, embracing the tenets of modern portfolio theory (MPT). Banks

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have pursued these efforts to increase the efficiency of their credit portfolios and look to increase returns for a given level of risk or, conversely, to reduce risks for a given level of returns. Institutions adopting more advanced credit portfolio measurement techniques expect that increased portfolio diversification and greater insight into portfolio risks will result in superior relative performance over the economic cycle. The second trend involves tactical bank efforts to reduce regulatory capital requirements on high-quality corporate credit exposures. The current Basel Committee on Bank Supervision Accord (‘Basel’) requirements of 8% for all corporate credits, regardless of underlying quality, reduce banks’ incentives to make higher quality loans. Banks have used various securitization alternatives to reconcile regulatory and economic capital requirements for large corporate exposures. Initially, these securitizations took the form of collateralized loan obligations (CLOs). More recently, however, banks have explored ways to reduce the high costs of CLOs, and have begun to consider synthetic securitization structures. The synthetic securitization structures banks employ to reduce regulatory capital requirements for higher-grade loan exposures use credit derivatives to purchase credit protection against a pool of credit exposures. As credit risk modeling efforts evolve, and banks increasingly embrace a MPT approach to credit risk management, banks increasingly may use credit derivatives to adjust portfolio risk profiles.

Size of the credit derivatives market and impediments to growth The first credit derivative transactions occurred in the early 1990s, as large derivative dealers searched for ways to transfer risk exposures on financial derivatives. Their objective was to be able to increase derivatives business with their largest counterparties. The market grew slowly at first. More recently, growth has accelerated as banks have begun to use credit derivatives to make portfolio adjustments and to reduce risk-based capital requirements. As discussed in greater detail below, there are four credit derivative products: credit default swaps (CDS), total return swaps (TRS), credit-linked notes (CLNs) and credit spread options. Default swaps, total return swaps and credit spread options are over-the-counter transactions, while credit-linked notes are cash market securities. Market participants estimate the current global market for credit derivatives will reach $740 billion by the year 2000.2 Bank supervisors in the USA began collecting credit derivative information in Call Reports as of 31 March 1997. Table 11.1 tracks the quarterly growth in credit derivatives for both insured US banks, and all institutions filing Call Reports (which includes uninsured US offices of foreign branches). The table’s data reflect substantial growth in credit derivatives. Over the two years US bank supervisors have collected the data, the compounded annual growth rate of notional credit derivatives for US insured banks, and all reporting entities (including foreign branches and agencies), were 216.2% and 137.2% respectively. Call Report data understates the size of the credit derivatives market. First, it includes only transactions for banks domiciled in the USA. It does not include the activities of banks domiciled outside the USA, or any non-commercial banks, such as investment firms. Second, the data includes activity only for off-balance sheet transactions; therefore, it completely excludes CLNs.

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Table 11.1 US credit derivatives market quarterly growth (billions) 31-3-97 30-6-97 30-9-97 31-12-97 31-3-98 30-6-98 30-9-98 31-12-98 31-3-99 Insured US banks US banks, foreign branches and agencies

$19.1

$25.6

$38.9

$54.7

$91.4

$129.2 $161.8 $144.1 $191.0

$40.7

$69.0

$72.9

$97.1

$148.3 $208.9 $217.1 $198.7 $229.1

Source: Call Reports

Activity in credit derivatives has grown rapidly over the past two years. Nevertheless, the number of institutions participating in the market remains small. Like financial derivatives, credit derivatives activity in the US banking system is concentrated in a small group of dealers and end-users. As of 31 March 1999, only 24 insured banking institutions, and 38 uninsured US offices (branches and agencies) of foreign banks reported credit derivatives contracts outstanding. Factors that account for the narrow institutional participation include: 1 2 3 4 5

Difficulty of measuring credit risk Application of risk-based capital rules Credit risk complacency and hedging costs Limited ability to hedge illiquid exposures and Legal and cultural issues.

An evaluation of these factors helps to set the stage for a discussion of credit derivative products and risk management issues, which are addressed in subsequent sections.

Difﬁculty of measuring credit risk Measuring credit risk on a portfolio basis is difficult. Banks traditionally measure credit exposures by obligor and industry. They have only recently attempted to define risk quantitatively in a portfolio context, e.g. a Value-at-Risk (VaR) framework.3 Although banks have begun to develop internally, or purchase, systems that measure VaR for credit, bank managements do not yet have confidence in the risk measures the systems produce. In particular, measured risk levels depend heavily on underlying assumptions (default correlations, amount outstanding at time of default, recovery rates upon default, etc.), and risk managers often do not have great confidence in those parameters. Since credit derivatives exist principally to allow for the effective transfer of credit risk, the difficulty in measuring credit risk and the absence of confidence in the results of risk measurement have appropriately made banks cautious about using credit derivatives. Such difficulties have also made bank supervisors cautious about the use of banks’ internal credit risk models for regulatory capital purposes. Measurement difficulties explain why banks have not, until very recently, tried to implement measures to calculate Value-at-Risk (VaR) for credit. The VaR concept, used extensively for market risk, has become so well accepted that bank supervisors allow such measures to determine capital requirements for trading portfolios.4 The models created to measure credit risk are new, and have yet to face the test of an economic downturn. Results of different credit risk models, using the same data, can

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vary widely. Until banks have greater confidence in parameter inputs used to measure the credit risk in their portfolios, they will, and should, exercise caution in using credit derivatives to manage risk on a portfolio basis. Such models can only complement, but not replace, the sound judgment of seasoned credit risk managers.

Application of risk-based capital rules Regulators have not yet settled on the most appropriate application of risk-based capital rules for credit derivatives, and banks trying to use them to reduce credit risk may find that current regulatory interpretations serve as disincentives.5 Generally, the current rules do not require capital based upon economic risk. For example, capital rules neither differentiate between high- and low-quality assets nor do they recognize diversification efforts. Transactions that pose the same economic risk may involve quite different regulatory capital requirements. While the Basel Committee has made the review of capital requirements for credit derivatives a priority, the current uncertainty of the application of capital requirements has made it difficult for banks to measure fully the costs of hedging credit risk.6

Credit risk complacency and hedging costs The absence of material domestic loan losses in recent years, the current strength of the US economy, and competitive pressures have led not only to a slippage in underwriting standards but also in some cases to complacency regarding asset quality and the need to reduce credit concentrations. Figure 11.1 illustrates the ‘lumpy’ nature of credit losses on commercial credits over the past 15 years. It plots charge-offs of commercial and industrial loans as a percentage of such loans.

Figure 11.1 Charge-offs: all commercial banks. *99Q1 has been annualized. (Data source: Bank Call Reports)

Over the past few years, banks have experienced very small losses on commercial credits. However, it is also clear that when the economy weakens, credit losses can become a major concern. The threat of large losses, which can occur because of credit concentrations, has led many larger banks to attempt to measure their credit risks on a more quantitative, ‘portfolio’, basis. Until recently, credit spreads on lower-rated, non-investment grade credits had

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contracted sharply. Creditors believe lower credit spreads indicate reduced credit risk, and therefore less need to hedge. Even when economic considerations indicate a bank should hedge a credit exposure, creditors often choose not to buy credit protection when the hedge cost exceeds the return from carrying the exposure. In addition, competitive factors and a desire to maintain customer relationships often cause banks to originate credit (funded or unfunded) at returns that are lower than the cost of hedging such exposures in the derivatives market. Many banks continue to have a book value, as opposed to an economic value, focus.

Limited ability to hedge illiquid exposures Credit derivatives can effectively hedge credit exposures when an underlying borrower has publicly traded debt (loans or bonds) outstanding that can serve as a reference asset. However, most banks have virtually all their exposures to firms that do not have public debt outstanding. Because banks lend to a large number of firms without public debt, they currently find it difficult to use credit derivatives to hedge these illiquid exposures. As a practical matter, banks are able to hedge exposures only for their largest borrowers. Therefore, the potential benefits of credit derivatives largely remain at this time beyond the reach of community banks, where credit concentrations tend to be largest.

Legal and cultural issues Unlike most financial derivatives, credit derivative transactions require extensive legal review. Banks that engage in credit derivatives face a variety of legal issues, such as: 1 Interpreting the meaning of terms not clearly defined in contracts and confirmations when unanticipated situations arise 2 The capacity of counterparties to contract and 3 Risks that reviewing courts will not uphold contractual arrangements. Although contracts have become more standardized, market participants continue to report that transactions often require extensive legal review, and that many situations require negotiation and amendments to the standardized documents. Until recently, very few default swap contracts were triggered because of the relative absence of default events. The recent increase in defaults has led to more credit events, and protection sellers generally have met their obligations without threat of litigation. Nevertheless, because the possibility for litigation remains a significant concern, legal risks and costs associated with legal transactional review remain obstacles to greater participation and market growth. Cultural issues also have constrained the use of credit derivatives. The traditional separation within banks between the credit and treasury functions has made it difficult for many banks to evaluate credit derivatives as a strategic risk management tool. Credit officers in many institutions are skeptical that the use of a portfolio model, which attempts to identify risk concentrations, can lead to more effective risk/reward decision making. Many resist credit derivatives because of a negative view of derivatives generally. Over time, bank treasury and credit functions likely will become more integrated, with each function contributing its comparative advantages to more effective risk

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management decisions. As more banks use credit portfolio models and credit derivatives, credit portfolio management may become more ‘equity-like’. As portfolio managers buy and sell credit risk in a portfolio context, to increase diversification and to make the portfolio more efficient, however, banks increasingly may originate exposure without maintaining direct borrower relationships. As portfolio management evolves toward this model, banks will face significant cultural challenges. Most banks report at least some friction between credit portfolio managers and line lenders, particularly with respect to loan pricing. Credit portfolio managers face an important challenge. They will attempt to capture the diversification and efficiency benefits offered by the use of more quantitative techniques and credit derivatives. At the same time, these risk managers will try to avoid diminution in their qualitative understanding of portfolio risks, which less direct contact with obligors may imply.

What are credit derivatives? Credit derivatives permit the transfer of credit exposure between parties, in isolation from other forms of risk. Banks can use credit derivatives both to assume or reduce (hedge) credit risk. Market participants refer to credit hedgers as protection purchasers, and to providers of credit protection (i.e. the party who assumes credit risk) as protection sellers. There are a number of reasons market participants have found credit derivatives attractive. First, credit derivatives allow banks to customize the credit exposure desired, without having a direct relationship with a particular client, or that client having a current funding need. Consider a bank that would like to acquire a twoyear exposure to a company in the steel industry. The company has corporate debt outstanding, but its maturity exceeds two years. The bank can simply sell protection for two years, creating an exposure that does not exist in the cash market. However, the flexibility to customize credit terms also bears an associated cost. The credit derivative is less liquid than an originated, directly negotiated, cash market exposure. Additionally, a protection seller may use only publicly available information in determining whether to sell protection. In contrast, banks extending credit directly to a borrower typically have some access to the entity’s nonpublic financial information. Credit derivatives allow a bank to transfer credit risk without adversely impacting the customer relationship. The ability to sell the risk, but not the asset itself, allows banks to separate the origination and portfolio decisions. Credit derivatives therefore permit banks to hedge the concentrated credit exposures that large corporate relationships, or industry concentrations created because of market niches, can often present. For example, banks may hedge existing exposures in order to provide capacity to extend additional credit without breaching internal, in-house limits. There are three principal types of credit derivative products: credit default swaps, total return swaps, and credit-linked notes. A fourth product, credit spread options, is not a significant product in the US bank market.

Credit default swaps In a credit default swap (CDS), the protection seller, the provider of credit protection, receives a payment in return for the obligation to make a payment that is contingent on the occurrence of a credit event for a reference entity. The size of the payment

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reflects the decline in value of a reference asset issued by the reference entity. A credit event is normally a payment default, bankruptcy or insolvency, failure to pay, or receivership. It can also include a restructuring or a ratings downgrade. A reference asset can be a loan, security, or any asset upon which a ‘dealer price’ can be established. A dealer price is important because it allows both participants to a transaction to observe the degree of loss in a credit instrument. In the absence of a credit event, there is no obligation for the protection seller to make any payment, and the seller collects what amounts to an option premium. Credit hedgers will receive a payment only if a credit event occurs; they do not have any protection against market value declines of the reference asset that occur without a credit event. Figure 11.2 shows the obligations of the two parties in a CDS.

Figure 11.2 Credit default swap.

In the figure the protection buyer looks to reduce risk of exposure to XYZ. For example, it may have a portfolio model that indicates that the exposure contributes excessively to overall portfolio risk. It is important to understand, in a portfolio context, that the XYZ exposure may well be a high-quality asset. A concentration in any credit risky asset, regardless of quality, can pose unacceptable portfolio risk. Hedging such exposures may represent a prudent strategy to reduce aggregate portfolio risk. The protection seller, on the other hand, may find the XYZ exposure helpful in diversifying its own portfolio risks. Though each counterparty may have the same qualitative view of the credit, their own aggregate exposure profiles may dictate contrary actions. If a credit event occurs, the protection seller must pay an amount as provided in the underlying contract. There are two methods of settlement following a credit event: (1) cash settlement; and (2) physical delivery of the reference asset at par value. The reference asset typically represents a marketable obligation that participants in a credit derivatives contract can observe to determine the loss suffered in the event of default. For example, a default swap in which a bank hedges a loan exposure to a company may designate a corporate bond from that same entity as the reference asset. Upon default, the decline in value of the corporate bond should approximate

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the loss in the value of the loan, if the protection buyer has carefully selected the reference asset. Cash-settled transactions involve a credit event payment (CEP) from the protection seller to the protection buyer, and can work in two different ways. The terms of the contract may call for a fixed dollar amount (i.e. a ‘binary’ payment). For example, the contract may specify a credit event payment of 50% upon default; this figure is negotiated and may, or may not, correspond to the expected recovery amount on the asset. More commonly, however, a calculation agent determines the CEP. If the two parties do not agree with the CEP determined by the calculation agent, then a dealer poll determines the payment. The dealer poll is an auction process in which dealers ‘bid’ on the reference asset. Contract terms may call for five independent dealers to bid, over a three-day period, 14 days after the credit event. The average price that the dealers bid will reflect the market expectation of a recovery rate on the reference asset. The protection seller then pays par value less the recovery rate. This amount represents the estimate of loss on assuming exposure to the reference asset. In both cases, binary payment or dealer poll, the obligation is cash-settled because the protection seller pays cash to settle its obligation. In the second method of settlement, a physical settlement, the protection buyer may deliver the reference asset, or other asset specified in the contract, to the protection seller at par value. Since the buyer collects the par value for the defaulted asset, if it delivers its underlying exposure, it suffers no credit loss. CDSs allow the protection seller to gain exposure to a reference obligor, but absent a credit event, do not involve a funding requirement. In this respect, CDSs resemble and are economically similar to standby letters of credit, a traditional bank credit product. Credit default swaps may contain a materiality threshold. The purpose of this is to avoid credit event payments for technical defaults that do not have a significant market impact. They specify that the protection seller make a credit event payment to the protection buyer, if a credit event has occurred and the price of the reference asset has fallen by some specified amount. Thus, a payment is conditional upon a specified level of value impairment, as well as a default event. Given a default, a payment occurs only if the value change satisfies the threshold condition. A basket default swap is a special type of CDS. In a basket default swap, the protection seller receives a fee for agreeing to make a payment upon the occurrence of the first credit event to occur among several reference assets in a basket. The protection buyer, in contrast, secures protection against only the first default among the specified reference assets. Because the protection seller pays out on one default, of any of the names (i.e. reference obligors), a basket swap represents a more leveraged transaction than other credit derivatives, with correspondingly higher fees. Basket swaps represent complicated risk positions due to the necessity to understand the correlation of the assets in the basket. Because a protection seller can lose on only one name, it would prefer the names in the basket to be as highly correlated as possible. The greater the number of names in the basket and the lower the correlation among the names, the greater the likelihood that the protection seller will have to make a payment. The credit exposure in a CDS generally goes in one direction. Upon default, the protection buyer will receive a payment from, and thus is exposed to, the protection seller. The protection buyer in a CDS will suffer a default-related credit loss only if both the reference asset and the protection seller default simultaneously. A default

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by either party alone should not result in a credit loss. If the reference entity defaults, the protection seller must make a payment. If the protection seller defaults, but the reference asset does not, the protection purchaser has no payment due. In this event, however, the protection purchaser no longer has a credit hedge, and may incur higher costs to replace the protection if it still desires a hedge. The protection seller’s only exposure to the protection buyer is for periodic payments of the protection fee. Dealers in credit derivatives, who may have a large volume of transactions with other dealers, should monitor this ‘receivables’ exposure.

Total return swaps In a total return swap (TRS), the protection buyer (‘synthetic short’) pays out cash flow received on an asset, plus any capital appreciation earned on that asset. It receives a floating rate of interest (usually LIBOR plus a spread), plus any depreciation on the asset. The protection seller (‘synthetic long’) has the opposite profile; it receives cash flows on the reference asset, plus any appreciation. It pays any depreciation to the protection buying counterparty, plus a floating interest rate. This profile establishes a TRS as a synthetic sale of the underlying asset by the protection buyer and a synthetic purchase by the protection seller. Figure 11.3 illustrates TRS cash flows.

Figure 11.3 Total return swap.

TRSs enable banks to create synthetic long or short positions in assets. A long position in a TRS is economically equivalent to the financed purchase of the asset. However, the holder of a long position in a TRS (protection seller) does not actually purchase the asset. Instead, the protection seller realizes all the economic benefits of ownership of the bond, but uses the protection buyer’s balance sheet to fund that ‘purchase’. TRSs enable banks to take short positions in corporate credit more easily than is possible in the cash markets. It is difficult to sell short a corporate bond (i.e. sell a bond and hope to repurchase, subsequently, the same security at a lower price), because the seller must deliver a specific bond to the buyer. To create a synthetic short in a corporate exposure with a TRS, an investor agrees to pay total return on an issue and receive a floating rate, usually LIBOR (plus a spread) plus any depreciation on the asset. Investors have found TRSs an effective means of creating short positions in emerging market assets. A TRS offers more complete protection to a credit hedger than does a CDS, because a TRS provides protection for market value deterioration short of an outright default.

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A credit default swap, in contrast, provides the equivalent of catastrophic insurance; it pays out only upon the occurrence of a credit event, in which case the default swap terminates. A TRS may or may not terminate upon default of the reference asset. Most importantly, unlike the one-way credit exposure of a CDS, the credit exposure in a TRS goes both ways. A protection buyer assumes credit exposure of the protection seller when the reference asset depreciates; in this case, the protection seller must make a payment to the protection buyer. A protection seller assumes credit exposure of the protection buyer, who must pay any appreciation on the asset to the protection seller. A protection buyer will suffer a loss only if the value of the reference asset has declined and simultaneously the protection seller defaults. A protection seller can suffer a credit loss if the protection buyer defaults and the value of the reference asset has increased. In practice, banks that buy protection use CDSs to hedge credit relationships, particularly unfunded commitments, typically with the objective to reduce risk-based capital requirements. Banks that sell protection seek to earn the premiums, while taking credit risks they would take in the normal course of business. Banks typically use TRSs to provide financing to investment managers and securities dealers. TRSs thus often represent a means of extending secured credit rather than a credit hedging activity. In such cases, the protection seller ‘rents’ the protection buyer’s balance sheet. The seller receives the total return of the asset that the buyer holds on its balance sheet as collateral for the loan. The spread over LIBOR paid by the seller compensates the buyer for its funding and capital costs. Credit derivative dealers also use TRSs to create structured, and leveraged, investment products. As an example, the dealer acquires $100 in high-yield loans and then passes the risk through to a special-purpose vehicle (SPV) by paying the SPV the total return on a swap. The SPV then issues $20 in investor notes. The yield, and thus the risk, of the $100 portfolio of loans is thus concentrated into $20 in securities, permitting the securities to offer very high yields.7

Credit-linked Notes A credit-linked note (CLN) is a cash market-structured note with a credit derivative, typically a CDS, embedded in the structure. The investor in the CLN sells credit protection. Should the reference asset underlying the CLN default, the investor (i.e. protection seller) will suffer a credit loss. The CLN issuer is a protection buyer. Its obligation to repay the par value of the security at maturity is contingent upon the absence of a credit event on the underlying reference asset. Figure 11.4 shows the cash flows of a CLN with an embedded CDS. A bank can use the CLN as a funded solution to hedging a company’s credit risk because issuing the note provides cash to the issuing bank. It resembles a loan participation but, as with other credit derivatives, the loan remains on the bank’s books. The investor in the CLN has sold credit protection and will suffer a loss if XYZ defaults, as the issuer bank would redeem the CLN at less than par to compensate it for its credit loss. For example, a bank may issue a CLN embedded with a fixed payout (binary) default swap that provides for a payment to investors of 75 cents on the dollar in the event a designated reference asset (XYZ) defaults on a specified obligation. The bank might issue such a CLN if it wished to hedge a credit exposure to XYZ. As with other credit derivatives, however, a bank can take a short position if

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Figure 11.4 A credit-linked note.

it has no exposure to XYZ, but issues a CLN using XYZ as the reference asset. Like the structured notes of the early 1990s, CLNs provide a cash market alternative to investors unable to purchase off-balance sheet derivatives, most often due to legal restrictions. CLNs are frequently issued through special-purpose vehicles (SPV), which use the sale proceeds from the notes to buy collateral assets, e.g. Treasury securities or money market assets. In these transactions, the hedging institution purchases default protection from the SPV. The SPV pledges the assets as collateral to secure any payments due to the credit hedger on the credit default swap, through which the sponsor of the SPV hedges its credit risk on a particular obligor. Interest on the collateral assets, plus fees on the default swap paid to the SPV by the hedger, generate cash flow for investors. When issued through an SPV, the investor assumes credit risk of both the reference entity and the collateral. When issued directly, the investor assumes two-name credit risk; it is exposed to both the reference entity and the issuer. Credit hedgers may choose to issue a CLN, as opposed to executing a default swap, in order to reduce counterparty credit risk. As the CLN investor pays cash to the issuer, the protection buying issuer eliminates credit exposure to the protection seller that would occur in a CDS. Dealers may use CLNs to hedge exposures they acquire by writing protection on default swaps. For example, a dealer may write protection on a default swap, with XYZ as the reference entity, and collect 25 basis points. The dealer may be able to hedge that exposure by issuing a CLN, perhaps paying LIBORò10 basis points, that references XYZ. The dealer therefore originates the exposure in one market and hedges it in another, arbitraging the difference between the spreads in the two markets.

Credit spread options Credit spread options allow investors to trade or hedge changes in credit quality. With a credit spread option, a protection seller takes the risk that the spread on a reference asset breaches a specified level. The protection purchaser buys the right to sell a security if the reference obligor’s credit spread exceeds a given ‘strike’ level. For example, assume a bank has placed a loan yielding LIBOR plus 15 basis points

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in its trading account. The bank may purchase an option on the borrower’s spread to hedge against trading losses should the borrower’s credit deteriorate. The bank may purchase an option, with a strike spread of 30 basis points, allowing it to sell the asset should the borrower’s current market spread rise to 30 basis points (or more) over the floating rate over the next month. If the borrower’s spread rises to 50 basis points, the bank would sell the asset to its counterparty, at a price corresponding to LIBOR plus 30 basis points. While the bank is exposed to the first 15 basis point movement in the spread, it does have market value (and thus default) protection on the credit after absorbing the first 15 basis points of spread widening. The seller of the option might be motivated by the view that a spread of LIBOR plus 30 basis points is an attractive price for originating the credit exposure. Unlike other credit derivative products, the US market for credit spread options currently is not significant; most activity in this product is in Europe. Until recently, current market spreads had been so narrow in the USA that investors appeared reluctant to sell protection against widening. Moreover, for dealers, hedging exposure on credit spread options is difficult, because rebalancing costs can be very high. Table 11.2 summarizes some of the key points discussed for the four credit derivative products. Table 11.2 The four credit derivative products Credit coverage for protection buyer

Product

Market

Principal bank uses

Credit default swaps

OTC

Default only; no payment for MTM losses unless a ‘credit event’ occurs and exceeds a materiality threshold

Protection buyers seek to: (1) reduce regulatory capital requirements on high-grade exposures; (2) make portfolio adjustments; (3) hedge credit exposure. Protection sellers seek to book income or acquire targeted exposures

Total return swaps

OTC

Protection buyer has MTM coverage

(1) Alternative to secured lending, typically to highly leveraged investors; (2) used to passthrough risk on high yield loans in structured (leveraged) investment transactions

Credit-linked notes

Cash

Typically default only

Hedge exposures: (1) owned in the banking book; or (2) acquired by a dealer selling protection on a CDS

Credit spread options

OTC

MTM coverage beyond a ‘strike’ level

Infrequently used in the USA

Risks of credit derivatives When used properly, credit derivatives can help diversify credit risk, improve earnings, and lower the risk profile of an institution. Conversely, the improper use of credit derivatives, as in poor lending practices, can result in an imprudent credit risk profile. Credit derivatives expose participants to the familiar risks in commercial banking; i.e. credit, liquidity, price, legal (compliance), foreign exchange, strategic, and reputa-

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tion risks. This section highlights these risks and discusses risk management practices that can help to manage and control the risk profile effectively.

Credit Risk The most obvious risk credit derivatives participants face is credit risk. Credit risk is the risk to earnings or capital of an obligor’s failure to meet the terms of any contract with the bank or otherwise to perform as agreed. For both purchasers and sellers of protection, credit derivatives should be fully incorporated within credit risk management processes. Bank management should integrate credit derivative activity in their credit underwriting and administration policies, and their exposure measurement, limit setting, and risk rating/classification processes. They should also consider credit derivative activity in their assessment of the adequacy of the allowance for loan and lease losses (ALLL) and their evaluation of concentrations of credit. There are a number of credit risks for both sellers and buyers of credit protection, each of which raises separate risk management issues. For banks selling credit protection (i.e. buying risk), the primary source of credit risk is the reference asset or entity. Table 11.3 highlights the credit protection seller’s exposures in the three principal types of credit derivative products seen in the USA. Table 11.3 Credit protection seller – credit risks Product

Reference asset risk

Counterparty risk

Credit default swaps (CDS)

If a ‘credit event’ occurs, the seller is required to make a payment based on the reference asset’s fall in value. The seller has contingent exposure based on the performance of the reference asset. The seller may receive physical delivery of the reference asset

Minimal exposure. Exposure represents the amount of deferred payments (fees) due from counterparty (risk protection buyer)

Total return swaps (TRS)

If the value of the reference asset falls, the seller must make a payment equal to the value change. The seller ‘synthetically’ owns and is exposed to the performance of the reference asset

If the value of the reference asset increases, the seller bank will have a payment due from the counterparty. The seller is exposed to the counterparty for the amount of the payment

Credit linked notes (CLN)

If the reference asset defaults, the seller (i.e. the bond investor) will not collect par value on the bond

If the issuer defaults, the investor may not collect par value. The seller is exposed to both the reference asset and the issuer (i.e. ‘two-name risk’). Many CLNs are issued through trusts, which buy collateral assets to pledge to investors. This changes the investor’s risk from issuer nonperformance to the credit risk of the collateral

Note: In CLNs the seller of credit protection actually buys a cash market security from the buyer of credit protection.

As noted in Table 11.3, the protection seller’s credit exposure will vary depending on the type of credit derivative used. In a CDS, the seller makes a payment only if a predefined credit event occurs. When investors sell protection through total rate-of-

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return swaps (i.e. receive total return), they are exposed to deterioration of the reference asset and to their counterparty for the amount of any increases in value of the reference asset. In CLN transactions, the investor (seller of credit protection) is exposed to default of the reference asset. Directly issued CLNs (i.e. those not issued through a trust) expose the investor to both the reference asset and the issuer. When banks buy credit protection, they also are exposed to counterparty credit risk as in other derivative products. Table 11.4 highlights the credit protection buyer’s exposures in the three principal types of credit derivative products. Table 11.4 Credit protection buyer – credit risks Product

Reference asset risk

Counterparty risk

Credit default swaps (CDS)

The buyer has hedged default risk on reference asset exposure if it has an underlying exposure. The extent of hedge protection may vary depending on the terms of the contract. Mismatched maturities result in forward credit exposures. If the terms and conditions of the reference asset differ from the underlying exposure, the buyer assumes residual or ‘basis’ risk. A CDS provides default protection only; the buyer retains market risk short of default.

If a credit event occurs, the counterparty will owe the buyer an amount normally determined by the amount of decline in the reference asset’s value.

Total return The buyer has ‘synthetically’ sold the asset. If the swaps (TRS): asset value increases, the buyer owes on the TRS pay total return but is covered by owning the asset.

The buyer has credit exposure of the counterparty if the reference asset declines in value.

Credit-linked notes (CLN)

Not applicable. The protection buyer in these transactions receives cash in exchange for the securities.

The protection buyer has obtained cash by issuing CLNs. The buyer may assume basis risk, depending upon the terms of the CLN.

As noted in Table 11.4, the protection buyer’s credit exposure also varies depending on the type of credit derivative. In a CDS, the buyer will receive a payment from the seller of protection when a default event occurs. This payment normally will equal the value decline of the CDS reference asset. In some transactions, however, the parties fix the amount in advance (binary). Absent legal issues, or a fixed payment that is less than the loss on the underlying exposure, the protection buyer incurs a credit loss only if both the underlying borrower (reference asset) and the protection seller simultaneously default. In a CDS transaction with a cash settlement feature, the hedging bank (protection buyer) receives a payment upon default, but remains exposed to the original balancesheet obligation. Such a bank can assure itself of complete protection against this residual credit risk by physically delivering the asset to the credit protection seller upon occurrence of a credit event. The physical delivery form of settlement has become more popular as the market has evolved. Absent a credit event, the protection buyer has no coverage against market value deterioration. If the term of the credit protection is less than the maturity of the exposure, the hedging bank will again become exposed to the obligation when the credit derivative matures. In that case, the bank has a forward credit risk. In a TRS, the protection buyer is exposed to its counterparty, who must make a payment when the value of the reference asset declines. Absent legal issues, a buyer

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will not incur a credit loss on the reference asset unless both the reference asset declines in value and the protection seller defaults. A bank that purchases credit protection by issuing a credit-linked note receives cash and thus has no counterparty exposure; it has simply sold bonds. It may have residual credit exposure to the underlying borrower if the recovery rate as determined by a bidding process is different than the value at which it can sell the underlying exposure. This differential is called ‘basis risk’. Managing credit risk: underwriting and administration For banks selling credit protection (buying risk) through a credit derivative, management should complete a financial analysis of both reference obligor(s) and the counterparty (in both default swaps and TRSs), establish separate credit limits for each, and assign appropriate risk ratings. The analysis of the reference obligor should include the same level of scrutiny that a traditional commercial borrower would receive. Documentation in the credit file should support the purpose of the transaction and creditworthiness of the reference obligor. Documentation should be sufficient to support the reference obligor’s risk rating. It is especially important for banks to use rigorous due diligence procedures in originating credit exposure via credit derivatives. Banks should not allow the ease with which they can originate credit exposure in the capital markets via derivatives to lead to lax underwriting standards, or to assume exposures indirectly that they would not originate directly. For banks purchasing credit protection through a credit derivative, management should review the creditworthiness of the counterparty, establish a credit limit, and assign a risk rating. The credit analysis of the counterparty should be consistent with that conducted for other borrowers or trading counterparties. Management should continue to monitor the credit quality of the underlying credits hedged. Although the credit derivative may provide default protection, in many instances (e.g. contracts involving cash settlement) the bank will retain the underlying credit(s) after settlement or maturity of the credit derivative. In the event the credit quality deteriorates, as legal owner of the asset, management must take actions necessary to improve the credit. Banks should measure credit exposures arising from credit derivative transactions and aggregate with other credit exposures to reference entities and counterparties. These transactions can create highly customized exposures and the level of risk/ protection can vary significantly between transactions. Management should document and support their exposure measurement methodology and underlying assumptions. Managing basis risk The purchase of credit protection through credit derivatives may not completely eliminate the credit risk associated with holding a loan because the reference asset may not have the same terms and conditions as the balance sheet exposure. This residual exposure is known as basis risk. For example, upon a default, the reference asset (often a publicly traded bond) might lose 25% of its value, whereas the underlying loan could lose 30% of its value. Should the value of the loan decline more than that of the reference asset, the protection buyer will receive a smaller payment on the credit default swap (derivative) than it loses on the underlying loan (cash transaction). Bonds historically have tended to lose more value, in default situations, than loans. Therefore, a bank hedging a loan exposure using a bond as a

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reference asset could benefit from the basis risk. The cost of protection, however, should reflect the possibility of benefiting from this basis risk. More generally, unless all the terms of the credit derivative match those of the underlying exposure, some basis risk will exist, creating an exposure for the protection buyer. Credit hedgers should carefully evaluate the terms and conditions of protection agreements to ensure that the contract provides the protection desired, and that the hedger has identified sources of basis risk. Managing maturity mismatches A bank purchasing credit protection is exposed to credit risk if the maturity of the credit derivative is less than the term of the exposure. In such cases, the bank would face a forward credit exposure at the maturity of the derivative, as it would no longer have protection. Hedging banks should carefully assess their contract maturities to assure that they do not inadvertently create a maturity mismatch by ignoring material features of the loan. For example, if the loan has a 15-day grace period in which the borrower can cure a payment default, a formal default can not occur until 15 days after the loan maturity. A bank that has hedged the exposure only to the maturity date of the loan could find itself without protection if it failed to consider this grace period. In addition, many credit-hedging transactions do not cover the full term of the credit exposure. Banks often do not hedge to the maturity of the underlying exposure because of cost considerations, as well as the desire to avoid short positions that would occur if the underlying obligor paid off the bank’s exposure. In such cases, the bank would continue to have an obligation to make fee payments on the default swap, but it would no longer have an underlying exposure. Evaluating counterparty risk A protection buyer can suffer a credit loss on a default swap only if the underlying obligor and the protection seller simultaneously default, an event whose probability is technically referred to as their ‘joint probability of default’. To limit risk, credit-hedging institutions should carefully evaluate the correlation between the underlying obligor and the protection seller. Hedgers should seek protection seller counterparties that have the lowest possible default correlation with the underlying exposure. Low default correlations imply that if one party defaults, only a small chance exists that the second party would also default. For example, a bank seeking to hedge against the default of a private sector borrower in an emerging market ordinarily would not buy protection from a counterparty in that same emerging market. Since the two companies may have a high default correlation, a default by one would imply a strong likelihood of default by the other. In practice, some credit hedging banks often fail to incorporate into the cost of the hedge the additional risk posed by higher default correlations. The lowest nominal fee offered by a protection seller may not represent the most effective hedge, given default correlation concerns. Banks that hedge through counterparties that are highly correlated with the underlying exposure should do so only with the full knowledge of the risks involved, and after giving full consideration to valuing the correlation costs. Evaluating credit protection Determining the amount of protection provided by a credit derivative is subjective, as the terms of the contract will allow for varying degrees of loss protection. Manage-

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ment should complete a full analysis of the reference obligor(s), the counterparty, and the terms of the underlying credit derivative contract and document its assessment of the degree of protection. Table 11.5 highlights items to consider. Table 11.5 Evaluating credit protection Factor

Issues

Reference asset

Is the reference asset an effective hedge for the underlying asset(s)? Same legal entity? Same level of seniority in bankruptcy? Same currency?

Default triggers

Do the ‘triggers’ in credit derivative match the default deﬁnition in the underlying assets (i.e. the cross-default provisions)?

Maturity mismatches

Does the maturity of the credit derivative match the maturity of the underlying asset(s)? Does the underlying asset have a grace period that would require the protection period to equal the maturity plus the grace period to achieve an effective maturity match? If a maturity mismatch exists, does the protection period extend beyond ‘critical’ payment/rollover points in the borrower’s debt structure? (As the difference between the protection period and the underlying asset maturity increases, the protection provided by the credit derivative decreases.)

Counterparty

Is there a willingness and ability to pay? Is there a concentration of credit exposure with the counterparty?

Settlement issues

Can the protection buyer deliver the underlying asset at its option? Must the buyer obtain permission of the borrower to physically deliver the asset? Are there any restrictions that preclude physical delivery of the asset? If the asset is required to be cash settled, how does the contract establish the payment amount? Dealer poll? Fixed payment? When does the protection-buying bank receive the credit derivative payment? (The longer the payment is deferred, the less valuable the protection.)

Materiality thresholds

Are thresholds low enough to effectively transfer all risk or must the price fall so far that the bank effectively has a deeply subordinated (large ﬁrst loss) position in the credit? Is the contract legally enforceable? Is the contract fully documented? Are there disputes over contract terms (e.g. deﬁnition of restructuring)? What events constitute a restructuring? How is accrued interest treated?

Legal issues

Banks selling credit protection assume reference asset credit risk and must identify the potential for loss, they should risk rate the exposure based on the financial condition and resources of the reference obligor. Banks face a number of less obvious credit risks when using credit derivatives. They include leverage, speculation, and pricing risks. Managing leverage considerations Most credit derivatives, like financial derivatives, involve leverage. If a bank selling credit protection does not fully understand the leverage aspects of some credit derivative structures, it may fail to receive an appropriate level of compensation for the risks assumed. A fixed payout (or binary) default swap can embed leverage into a credit transaction. In an extreme case, the contract may call for a 100% payment from the protection

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seller to the protection buyer in the event of default. This amount is independent of the actual amount of loss the protection buyer (lender) may suffer on its underlying exposure. Fixed payout swaps can allow the protection buyer to ‘over-hedge’, and achieve a ‘short’ position in the credit. By contracting to receive a greater creditevent payment than its expected losses on its underlying transaction, the protection buyer actually benefits from a default. Protection sellers receive higher fees for assuming fixed payment obligations that exceed expected credit losses and should always evaluate and manage those exposures prudently. For a protection seller, a basket swap also represents an especially leveraged credit transaction, since it suffers a loss if any one of the basket names defaults. The greater the number of names, the greater the chance of default. The credit quality of the transaction will ordinarily be less than that of the lowest rated name. For example, a basket of 10 names, all rated ‘A’ by a national rating agency, may not qualify for an investment grade rating, especially if the names are not highly correlated. Banks can earn larger fees for providing such protection, but increasing the number of exposures increases the risk that they will have to make a payment to a counterparty. Conceptually, protection sellers in basket swaps assume credit exposure to the weakest credit in the basket. Simultaneously, they write an option to the protection buyer, allowing that party to substitute another name in the basket should it become weaker than the originally identified weakest credit. Protection buyers in such transactions may seek to capitalize upon a protection seller’s inability to quantify the true risk of a default basket. When assuming these kinds of credit exposures, protection-selling banks should carefully consider their risk tolerance, and determine whether the leverage of the transaction represents a prudent risk/reward opportunity. Speculation Credit derivatives allow banks, for the first time, to sell credit risk short. In a short sale, a speculator benefits from a decline in the price of an asset. Banks can short credit risk by purchasing default protection in a swap, paying the total return on a TRS, or issuing a CLN, in each case without having an underlying exposure to the reference asset. Any protection payments the bank receives under these derivatives would not offset a balance sheet exposure, because none exists. Short positions inherently represent trading transactions. For example, a bank may pay 25 basis points per year to buy protection on a company to which it has no exposure. If credit spreads widen, the bank could then sell protection at the new market level; e.g. 40 basis points. The bank would earn a net trading profit of 15 basis points. The use of short positions as a credit portfolio strategy raises concerns that banks may improperly speculate on credit risk. Such speculation could cause banks to lose the focus and discipline needed to manage traditional credit risk exposures. Credit policies should specifically address the institution’s willingness to implement short credit risk positions, and also specify appropriate controls over the activity. Pricing Credit trades at different spread levels in different product sectors. The spread in the corporate bond market may differ from the loan market, and each may differ from the spread available in the credit derivatives market. Indeed, credit derivatives allow

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institutions to arbitrage the different sectors of the credit market, allowing for a more complete market. With an asset swap, an investor can synthetically transform a fixed-rate security into a floating rate security, or vice versa. For example, if a corporate bond trades at a fixed yield of 6%, an investor can pay a fixed rate on an interest rate swap (and receive LIBOR), to create a synthetic floater. If the swap fixed rate is 5.80%, the floater yields LIBOR plus 20 basis points. The spread over LIBOR on the synthetic floater is often compared to the market price for a default swap as an indicator of value. If the fee on the default swap exceeds, in this example, 20 basis points, the default swap is ‘cheap’ to the asset swap, thereby representing value. While benchmark indicators are convenient, they do not consider all the factors a bank should evaluate when selling protection. For example, when comparing credit derivative and cash market pricing levels, banks should consider the liquidity disadvantage of credit derivatives, their higher legal risks, and the lower information quality generally available when compared to a direct credit relationship. Using asset swap levels to determine appropriate compensation for selling credit protection also considers the exposure in isolation, for it ignores how the new exposure impacts aggregate portfolio risk, a far more important consideration. The protection seller should consider whether the addition of the exposure increases the diversification of the protection seller’s portfolio, or exacerbates an existing concern about concentration. Depending on the impact of the additional credit exposure on its overall portfolio risk, a protection seller may find that the benchmark pricing guide; i.e. asset swaps, fails to provide sufficient reward for the incremental risk taken. Banks face this same issue when extending traditional credit directly to a borrower. The increasing desire to measure the portfolio impacts of credit decisions has led to the development of models to quantify how incremental exposures could impact aggregate portfolio risk.

Liquidity risk Market participants measure liquidity risk in two different ways. For dealers, liquidity refers to the spread between bid and offer prices. The narrower the spread, the greater the liquidity. For end-users and dealers, liquidity risk refers to an institution’s ability to meet its cash obligations as they come due. As an emerging derivative product, credit derivatives have higher bid/offer spreads than other derivatives, and therefore lower liquidity. The wider spreads available in credit derivatives offer dealers profit opportunities which have largely been competed away in financial derivatives. These larger profit opportunities in credit derivatives explain why a number of institutions currently are, or plan to become, dealers. Nevertheless, the credit derivatives market, like many cash credit instruments, has limited depth, creating exposure to liquidity risks. Dealers need access to markets to hedge their portfolio of exposures, especially in situations in which a counterparty that provides an offset for an existing position defaults. The counterparty’s default could suddenly give rise to an unhedged exposure which, because of poor liquidity, the dealer may not be able to offset in a cost-effective manner. Like financial derivatives, credit and market risks are interconnected; credit risks becomes market risk, and vice versa. Both dealers and end-users of credit derivatives should incorporate the impact of these scenarios into regular liquidity planning and monitoring systems. Cash flow

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projections should consider all significant sources and uses of cash and collateral. A contingency funding plan should address the impact of any early termination agreements or collateral/margin arrangements.

Price risk Price risk refers to the changes in earnings due to changes in the value of portfolios of financial instruments; it is therefore a critical risk for dealers. The absence of historical data on defaults, and on correlations between default events, complicates the precise measurement of price risk and makes the contingent exposures of credit derivatives more difficult to forecast and fully hedge than a financial derivatives book. As a result, many dealers try to match, or perfectly offset, transaction exposures. Other dealers seek a competitive advantage by not running a matched book. For example, they might hedge a total return swap with a default swap, or hedge a senior exposure with a junior exposure. A dealer could also hedge exposure on one company with a contract referencing another company in the same industry (i.e. a proxy hedge). As dealers manage their exposures more on a portfolio basis, significant basis and correlation risk issues can arise, underscoring the importance of stress testing the portfolio. Investors seeking exposure to emerging markets often acquire exposures denominated in currencies different from their own reporting currency. The goal in many of these transactions is to bet against currency movements implied by interest rate differentials. When investors do not hedge the currency exposure, they clearly assume foreign exchange risk. Other investors try to eliminate the currency risk and execute forward transactions. To offset correlation risk which can arise, an investor should seek counterparties on the forward foreign exchange transaction who are not strongly correlated with the emerging market whose currency risk the investor is trying to hedge.

Legal (compliance) risks Compliance risk is the risk to earnings or capital arising from violations, or nonconformance with, laws, rules, regulations, prescribed practices, or ethical standards. The risk also arises when laws or rules governing certain bank products or activities of the bank’s clients may be ambiguous or untested. Compliance risk exposes the institution to fines, civil money penalties, payment of damages, and the voiding of contracts. Compliance risk can lead to a diminished reputation, reduced franchise value, limited business opportunities, lessened expansion potential, and an inability to enforce contracts. Since credit derivatives are new and largely untested credit risk management products, legal risks associated with them can be high. To offset such risks, it is critical for each party to agree to all terms prior to execution of the contract. Discovering that contracts have not been signed, or key terms have not been clearly defined, can jeopardize the protection that a credit risk hedger believes it has obtained. Banks acting in this capacity should consult legal counsel as necessary to ensure credit derivative contracts are appropriately drafted and documented. The Russian default on GKO debt in 1998 underscores the importance of understanding the terms of the contract and its key definitions. Most default swap contracts in which investors purchased protection on Russian debt referenced external debt obligations, e.g. Eurobond debt. When Russia defaulted on its internal GKO obliga-

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tions, many protection purchasers were surprised to discover, after reviewing their contracts, that an internal default did not constitute a ‘credit event’. As of July 1999, Russia has continued to pay its Eurobond debt. Although investors in Russia’s Eurobonds suffered significant mark-to-market losses when Russia defaulted on its internal debt, protection purchasers could not collect on the default swap contracts. Credit hedgers must assess the circumstances under which they desire protection, and then negotiate the terms of the contract accordingly. Although no standardized format currently exists for all credit derivatives, transactions are normally completed with a detailed confirmation under an ISDA Master Agreement. These documents will generally include the following transaction information: Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω

trade date maturity date business day convention reference price key definitions (credit events, etc.) conditions to payment materiality requirements notice requirements dispute resolution mechanisms reps and warranties designed to reduce legal risk credit enhancement terms or reference to an ISDA master credit annex effective date identification of counterparties reference entity reference obligation(s) payment dates payout valuation method settlement method (physical or cash) payment details

Documentation should also address, as applicable, the rights to obtain financial information on the reference asset or counterparty, restructuring or merger of the reference asset, method by which recovery values are determined (and any fallback procedures if a dealer poll fails to establish a recovery value), rights in receivership or bankruptcy, recourse to the borrower, and early termination rights. Moral hazards To date, no clear legal precedent governs the number of possible moral hazards that may arise in credit derivatives. The following examples illustrate potentially troubling issues that could pose legal risks for banks entering into credit derivative transactions. Access to material, nonpublic information Based on their knowledge of material, nonpublic information, creditors may attempt to buy credit protection and unfairly transfer their risk to credit protection sellers. Most dealers acknowledge this risk, but see it as little different from that faced in loan and corporate bond trading. These dealers generally try to protect themselves against the risk of information asymmetries by exercising greater caution about

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intermediating protection as the rating of the reference asset declines. They also may want to consider requiring protection purchasers to retain a portion of the exposure when buying protection so that the risk hedger demonstrates a financial commitment in the asset. Bank dealers also should adopt strict guidelines when intermediating risk from their bank’s own credit portfolios, for they can ill afford for market participants to suspect that the bank is taking advantage of nonpublic information when sourcing credit from its own portfolio. Implementation of firewalls between the public and nonpublic sides of the institution is an essential control. When the underlying instrument in a credit derivatives transaction is a security (as defined in the federal securities laws), credit protection sellers may have recourse against counterparties that trade on inside information and fail to disclose that information to their counterparties. Such transactions generally are prohibited as a form of securities fraud. Should the underlying instrument in a credit derivatives transaction not be a security and a credit protection seller suspects that its counterparty possessed and traded on the basis of material, nonpublic information, the seller would have to base a claim for redress on state law antifraud statutes and common law. Inadequate credit administration The existence of credit protection may provide an incentive for protection purchasers to administer the underlying borrower relationship improperly. For example, consider technical covenant violations in a loan agreement a bank may ordinarily waive. A bank with credit protection may be tempted to enforce the covenants and declare a default so that the timing of the default occurs during the period covered by the credit protection. It is unclear whether the protection seller has a cause of action against such a bank by charging that it acted improperly to benefit from the credit derivative. Another potential problem could involve the definition of a default event, which typically includes a credit restructuring. A creditor that has purchased protection on an exposure can simply restructure the terms of a transaction, and through its actions alone, declare a credit event. Most contracts require a restructuring to involve a material adverse change for the holder of the debt, but the legal definition of a material adverse change is subject to judgment and interpretation. All participants in credit derivative transactions need to understand clearly the operative definition of restructuring. In practice, credit derivative transactions currently involve reference obligors with large amounts of debt outstanding, in which numerous banks participate as creditors. As a result, any one creditor’s ability to take an action that could provide it with a benefit because of credit derivative protection is limited, because other participant creditors would have to affirm the actions. As the market expands, however, and a greater number of transactions with a single lender occur, these issues will assume increasing importance. Protection sellers may consider demanding voting rights in such cases. Optionality Credit derivative contracts often provide options to the protection purchaser with respect to which instruments it can deliver upon a default event. For example, the purchaser may deliver any instrument that ranks pari passu with the reference asset. Though two instruments may rank pari passu, they may not have the same

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value upon default. For example, longer maturities may trade at lower dollar prices. Protection purchasers can thus create greater losses for protection sellers by exploiting the value of these options, and deliver, from among all potentially deliverable assets, the one that maximizes losses for the protection seller. Protection sellers must carefully assess the potential that the terms of the contract could provide uncompensated, yet valuable, options to their counterparties. This form of legal risk results when one party to the contract and its legal counsel have greater expertise in credit derivatives than the counterparty and its counsel. This is particularly likely to be the case in transactions between a dealer and an end-user, underscoring the importance of end-users transacting with reputable dealer counterparties. These issues highlight the critical need for participants in credit derivatives to involve competent legal counsel in transaction formulation, structure, and terms.

Reputation Risk Banks serving as dealers in credit derivatives face a number of reputation risks. For example, the use of leveraged credit derivative transactions, such as basket default swaps and binary swaps, raises significant risks if the counterparty does not have the requisite sophistication to evaluate a transaction properly. As with leveraged financial derivatives, banks should have policies that call for heightened internal supervisory review of such transactions. A mismatched maturity occurs when the maturity of the credit derivative is shorter than the maturity of the underlying exposure the protection buyer desires to hedge. Some observers have noted that protection sellers on mismatched maturity transactions can face an awkward situation when they recognize a credit event may occur shortly, triggering a payment obligation. The protection seller might evaluate whether short-term credit extended to the reference obligor may delay a default long enough to permit the credit derivative to mature. Thinly veiled attempts to avoid a payment obligation on a credit derivative could have adverse reputation consequences. The desire many dealers have to build credit derivatives volume, and thus distinguish themselves in the marketplace as a leader, can easily lead to transactions of questionable merit and/or which may be inappropriate for client counterparties. Reputation risks are very difficult to measure and thus are difficult to manage.

Strategic Risk Strategic risk is the risk to earnings or capital from poorly conceived business plans and/or weak implementation of strategic initiatives. Before achieving material participation in the credit derivatives market, management should assess the impact on the bank’s risk profile and ensure that adequate internal controls have been established for the conduct of all trading and end-user activities. For example, management should assess: Ω The adequacy of personnel expertise, risk management systems, and operational capacity to support the activity. Ω Whether credit derivative activity is consistent with the bank’s overall business strategy. Ω The level and type of credit derivative activity in which the bank plans to engage (e.g. dealer versus end-user).

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Ω The types and credit quality of underlying reference assets and counterparties. Ω The structures and maturities of transactions. Ω Whether the bank has completed a risk/return analysis and established performance benchmarks. Banks should consider the above issues as part of a new product approval process. The new product approval process should include approval from all relevant bank offices or departments such as risk control, operations, accounting, legal, audit, and line management. Depending on the magnitude of the new product or activity and its impact on the bank’s risk profile, senior management, and in some cases the board, should provide the final approval.

Regulatory capital issues The Basel Capital Accord generally does not recognize differences in the credit quality of bank assets for purposes of allocating risk-based capital requirements. Instead, the Accord’s risk weights consider the type of obligation, or its issuer. Under current capital rules, a ‘Aaa’ corporate loan and a ‘B’ rated corporate loan have the same risk weight, thereby requiring banks to allocate the same amount of regulatory capital for these instruments. This differentiation between regulatory capital requirements and the perceived economic risk of a transaction has caused some banks to engage in ‘regulatory capital arbitrage’ (RCA) strategies to reduce their risk-based capital requirements. Though these strategies can reduce regulatory capital allocations, they often do not materially reduce economic risks. To illustrate the incentives banks have to engage in RCA, Table 11.6 summarizes the Accord’s risk weights for on-balance sheet assets and credit commitments. Table 11.6 Risk-based-capital risk weights for on-balance-sheet assets and commitments Exposure Claims on US government; OECD central governments; credit commitments less than one year Claims on depository institutions incorporated in OECD countries; US government agency obligations First mortgages on residential properties; loans to builders for 1–4 family residential properties; credit commitments greater than one year All other private sector obligations

Risk weight 0% 20% 50% 100%

Risk weighted assets (RWA) are derived by assigning assets to one of the four categories above. For example, a $100 commitment has no risk-based capital requirement if it matures in less than one year, and a $4 capital charge ($100î50%î8%) if greater than one year. If a bank makes a $100 loan to a private sector borrower with a 100% risk weight, the capital requirement is $8 ($100î8%). Under current capital rules, a bank incurs five times the capital requirement for a ‘Aaa’ rated corporate exposure (100% risk weight) than it does for a sub-investment grade exposure to an OECD government (20% risk weight). Moreover, within the 100% risk weight category, regulatory capital requirements are independent of asset quality. A sub-investment grade exposure and an investment grade exposure require the same regulatory capital.

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The current rules provide a regulatory incentive for banks to acquire exposure to lower-rated borrowers, since the greater spreads available on such assets provide a greater return on regulatory capital. When adjusted for risk, however, and after providing the capital to support that risk, banks may not economically benefit from acquiring lower quality exposures. Similarly, because of the low risk of high-quality assets, risk-adjusted returns on these assets may be attractive. Consequently, returns on regulatory and ‘economic’ capital can appear very different. Transactions attractive under one approach may not be attractive under the other. Banks should develop capital allocation models to assign capital based upon economic risks incurred. Economic capital allocation models attempt to ensure that a bank has sufficient capital to support its true risk profile, as opposed to the necessarily simplistic Basel paradigm. Larger banks have implemented capital allocation models, and generally try to manage their business based upon the economic consequences of transactions. While such models generally measure risks more accurately than the Basel paradigm, banks implementing the models often assign an additional capital charge for transactions that incur regulatory capital charges which exceed capital requirements based upon measured economic risk. These additional charges reflect the reality of the cost imposed by higher regulatory capital requirements.

Credit Derivatives 8 Under current interpretations of the Basel Accord, a bank may substitute the risk weight of the protection-selling counterparty for the weight of its underlying exposure. To illustrate this treatment, consider a $50 million, one year bullet loan to XYZ, a high-quality borrower. The loan is subject to a 100% risk-weight, and the bank must allocate regulatory capital for this commitment of $4 million ($50 millionî100% î8%). If the bank earns a spread over its funding costs of 25 basis points, it will net $125 000 on the transaction ($50 millionî0.0025). The bank earns a 3.125% return on regulatory capital (125 000/4 000 000). Because of regulatory capital constraints, the bank may decide to purchase protection on the exposure, via a default swap costing 15 basis points, from an OECD bank. The bank now earns a net spread of 10 basis points, or $50 000 per year. However, it can substitute the risk weight of its counterparty, which is 20%, for that of XYZ, which is 100%. The regulatory capital for the transaction becomes $800 000 ($50 millionî20%î8%), and the return on regulatory capital doubles to 6.25% (50 000/800 000).9 The transaction clearly improves the return on regulatory capital. Because of the credit strength of the borrower, however, the bank in all likelihood does not attribute much economic capital to the exposure. The default swap premium may reduce the return on the loan by more than the economic capital declines by virtue of the enhanced credit position. Therefore, as noted earlier, a transaction that increases the return on regulatory capital may simultaneously reduce the return on economic capital. As discussed previously, many credit derivative transactions do not cover the full maturity of the underlying exposure. The international supervisory community has concerns about mismatches because of the forward capital call that results if banks reduced the risk weight during the protection period. Consequently, some countries do not permit banks to substitute the risk weight of the protection provider for that

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of the underlying exposure when a mismatch exists. Recognizing that a failure to provide capital relief can create disincentives to hedge credit risk, the mismatch issue remains an area of active deliberation within the Basel Committee.

Securitization In an effort to reduce their regulatory capital costs, banks also use various specialpurpose vehicles (SPVs) and securitization techniques to unbundle and repackage risks to achieve more favorable capital treatment. Initially, for corporate exposures, these securitizations have taken the form of collateralized loan obligations (CLOs). More recently, however, banks have explored ways to reduce the high costs of CLOs, and have begun to consider synthetic securitization structures. Asset securitization allows banks to sell their assets and use low-level recourse rules to reduce RWA. Figure 11.5 illustrates a simple asset securitization, in which the bank retains a $50 ‘first loss’ equity piece, and transfers the remaining risk to bond investors. The result of the securitization of commercial credit is to convert numerous individual loans and/or bonds into a single security. Under low-level recourse rules,10 the bank’s capital requirements cannot exceed the level of its risk, which in this case is $50. Therefore, the capital requirement falls from $80 ($1000î8%) to $50, or 37.5%.

Figure 11.5 CLO/BBO asset conversions.

The originating bank in a securitization such as a CLO retains the equity (first loss) piece. The size of this equity piece will vary; it will depend primarily on the quality and diversification of the underlying credits and the desired rating of the senior and junior securities. For example, to obtain the same credit rating, a CLO collateralized by a diversified portfolio of loans with strong credit ratings will require a smaller equity piece than a structure backed by lower quality assets that are more concentrated. The size of the equity piece typically will cover some multiple of the pool’s expected losses. Statistically, the equity piece absorbs, within a certain confidence interval, the entire amount of credit risk. Therefore, a CLO transfers only catastrophic credit risk.11 The retained equity piece bears the expected losses. In this sense, the bank has not changed its economic risks, even though it has reduced its capital requirements. To reduce the cost of CLO issues, banks recently have explored new, lower-cost, ‘synthetic’ vehicles using credit derivatives. The objective of the synthetic structures is to preserve the regulatory capital benefits provided by CLOs, while at the same time lowering funding costs. In these structures, a bank attempting to reduce capital requirements tries to eliminate selling the full amount of securities corresponding to

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the credit exposures. It seeks to avoid paying the credit spread on senior tranches that makes traditional CLOs so expensive. In synthetic transactions seen to date, the bank sponsor retains a first loss position, similar to a traditional CLO. The bank sponsor then creates a trust that sells securities against a small portion of the total exposure, in sharp contrast to the traditional CLO, for which the sponsor sells securities covering the entire pool. Sponsors typically have sold securities against approximately 8% of the underlying collateral pool, an amount that matches the Basel capital requirement for credit exposure. The bank sponsor purchases credit protection from an OECD bank to reduce the risk weight on the top piece to 20%, subject to maturity mismatch limitations imposed by national supervisors. The purpose of these transactions is to bring risk-based capital requirements more in line with the economic capital required to support the risks. Given that banks securitize their highest quality exposures, management should consider whether their institutions have an adequate amount of capital to cover the risks of the remaining, higher-risk, portfolio.

A portfolio approach to credit risk management Since the 1980s, banks have successfully applied modern portfolio theory (MPT) to market risk. Many banks are now using earnings at risk (EaR) and Value-at-Risk (VaR)12 models to manage their interest rate and market risk exposures. Unfortunately, however, even through credit risk remains the largest risk facing most banks, the practical application of MPT to credit risk has lagged. The slow development toward a portfolio approach for credit risk results from the following factors: Ω The traditional view of loans as hold-to-maturity assets. Ω The absence of tools enabling the efficient transfer of credit risk to investors while continuing to maintain bank customer relationships. Ω The lack of effective methodologies to measure portfolio credit risk. Ω Data problems. Banks recognize how credit concentrations can adversely impact financial performance. As a result, a number of sophisticated institutions are actively pursuing quantitative approaches to credit risk measurement. While data problems remain an obstacle, these industry practitioners are making significant progress toward developing tools that measure credit risk in a portfolio context. They are also using credit derivatives to transfer risk efficiently while preserving customer relationships. The combination of these two developments has precipitated vastly accelerated progress in managing credit risk in a portfolio context over the past several years.

Asset-by-asset approach Traditionally, banks have taken an asset-by-asset approach to credit risk management. While each bank’s method varies, in general this approach involves periodically evaluating the credit quality of loans and other credit exposures, applying a credit risk rating, and aggregating the results of this analysis to identify a portfolio’s expected losses. The foundation of the asset-by-asset approach is a sound loan review and internal

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credit risk rating system. A loan review and credit risk rating system enables management to identify changes in individual credits, or portfolio trends, in a timely manner. Based on the results of its problem loan identification, loan review, and credit risk rating system, management can make necessary modifications to portfolio strategies or increase the supervision of credits in a timely manner. Banks must determine the appropriate level of the Allowance for Loan and Lease Losses (ALLL) on a quarterly basis. On large problem credits, they assess ranges of expected losses based on their evaluation of a number of factors, such as economic conditions and collateral. On smaller problem credits and on ‘pass’ credits, banks commonly assess the default probability from historical migration analysis. Combining the results of the evaluation of individual large problem credits and historical migration analysis, banks estimate expected losses for the portfolio and determine provision requirements for the ALLL. Migration analysis techniques vary widely between banks, but generally track the loss experience on a fixed or rolling population of loans over a period of years. The purpose of the migration analysis is to determine, based on a bank’s experience over a historical analysis period, the likelihood that credits of a certain risk rating will transition to another risk rating. Table 11.7 illustrates a one-year historical migration matrix for publicly rated corporate bonds. Notice that significant differences in risk exist between the various credit risk rating grades. For example, the transition matrix in Table 11.7 indicates the one-year historical transition of an AAA-rated credit to default is 0.0%, while for a B-rated credit the one-year transition to default is 6.81%. The large differences in default probabilities between high and low grade credits, given a constant 8% capital requirement, has led banks to explore vehicles to reduce the capital cost of higher quality assets, as previously discussed. Table 11.7 Moody’s investor service: one-year transition matrix Initial rating Aaa Aa a Baa Ba B Caa

Aaa

Aa

A

Baa

Ba

B

Caa

Default

93.40 1.61 0.07 0.05 0.02 0.00 0.00

5.94 90.55 2.28 0.26 0.05 0.04 0.00

0.64 7.46 92.44 5.51 0.42 0.13 0.00

0.00 0.26 4.63 88.48 5.16 0.54 0.62

0.02 0.09 0.45 4.76 86.91 6.35 2.05

0.00 0.01 0.12 0.71 5.91 84.22 2.05

0.00 0.00 0.01 0.08 0.24 1.91 69.20

0.00 0.02 0.00 0.15 1.29 6.81 24.06

Source: Lea Carty, Moody’s Investor Service from CreditMetrics – Technical Document

Default probabilities do not, however, indicate loss severity; i.e. how much the bank will lose if a credit defaults. A credit may default, yet expose a bank to a minimal loss risk if the loan is well secured. On the other hand, a default might result in a complete loss. Therefore, banks currently use historical migration matrices with information on recovery rates in default situations to assess the expected loss potential in their portfolios.

Portfolio approach While the asset-by-asset approach is a critical component to managing credit risk, it does not provide a complete view of portfolio credit risk, where the term ‘risk’ refers

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to the possibility that actual losses exceed expected losses. Therefore, to gain greater insights into credit risk, banks increasingly look to complement the asset-by-asset approach with a quantitative portfolio review using a credit model. A primary problem with the asset-by-asset approach is that it does not identify or quantify the probability and severity of unexpected losses. Historical migration analysis and problem loan allocations are two different methods of measuring the same variable; i.e. expected losses. The ALLL absorbs expected losses. However, the nature of credit risk is that there is a small probability of very large losses. Figure 11.6 illustrates this fundamental difference between market and credit portfolios. Market risk returns follow a normal distribution, while credit risk returns exhibit a skewed distribution.

Figure 11.6 Comparison of distribution of credit returns and market returns. (Source: J. P. Morgan)

The practical consequence of these two return distributions is that, while the mean and variance fully describe (i.e. they define all the relevant characteristics of) the distributions of market returns, they do not fully describe the distribution of credit risk returns. For a normal distribution, one can say that the portfolio with the larger variance has greater risk. With a credit risk portfolio, a portfolio with a larger variance need not automatically have greater risk than one with a smaller variance, because the skewed distribution of credit returns does not allow the mean and variance to describe the distribution fully. Credit returns are skewed to the left and exhibit ‘fat tails’; i.e. a probability, albeit very small, of very large losses. While banks extending credit face a high probability of a small gain (payment of interest and return of principal), they face a very low probability of large losses. Depending upon risk tolerance, an investor may consider a credit portfolio with a larger variance less risky than one with a smaller variance, if the smaller variance portfolio has some probability of an unacceptably large loss. Credit risk, in a statistical sense, refers to deviations from expected losses, or unexpected losses. Capital covers unexpected losses, regardless of the source; therefore, the measurement of unexpected losses is an important concern.

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Figure 11.7 Probability of distribution of loss.

Figure 11.7 shows a small probability of very large losses. Banks hold capital to cover ‘unexpected’ loss scenarios consistent with their desired debt rating.13 While the probability of very large losses is small, such scenarios do occur, usually due to excessive credit concentrations, and can create significant problems in the banking system. Banks increasingly attempt to address the inability of the asset-by-asset approach to measure unexpected losses sufficiently by pursuing a ‘portfolio’ approach. One weakness with the asset-by-asset approach is that it has difficulty identifying and measuring concentration risk. Concentration risk refers to additional portfolio risk resulting from increased exposure to a borrower, or to a group of correlated borrowers. For example, the high correlation between energy and real estate prices precipitated a large number of failures of banks that had credit concentrations in those sectors in the mid-1980s. Traditionally, banks have relied upon arbitrary concentration limits to manage concentration risk. For example, banks often set limits on credit exposure to a given industry, or to a geographic area. A portfolio approach helps frame concentration risk in a quantitative context, by considering correlations. Even though two credit exposures may not come from the same industry, they could be highly correlated because of dependence upon common economic factors. An arbitrary industry limit may not be sufficient to protect a bank from unwarranted risk, given these correlations. A model can help portfolio managers set limits in a more risk-focused manner, allocate capital more effectively, and price credit consistent with the portfolio risks entailed.14 It is important to understand what diversification can and cannot do for a portfolio. The goal of diversification in a credit portfolio is to shorten the ‘tail’ of the loss distribution; i.e. to reduce the probability of large, unexpected, credit losses. Diversification cannot transform a portfolio of poor quality assets, with a high level of expected losses, into a higher quality portfolio. Diversification efforts can reduce the uncertainty of losses around the expectation (i.e. credit ‘risk’), but it cannot change the level of expected loss, which is a function of the quality of the constituent assets.

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A low-quality portfolio will have higher expected losses than a high quality portfolio. Because all credit risky assets have exposure to macro-economic conditions, it is impossible to diversify a portfolio completely. Diversification efforts focus on eliminating the issuer specific, or ‘unsystematic’, risk of the portfolio. Credit managers attempt to do this by spreading the risk out over a large number of obligors, with cross correlations as small as possible. Figure 11.8 illustrates how the goal of diversification is to reduce risk to its ‘systematic’ component; i.e. the risks that a manager cannot diversify away because of the dependence of the obligors on the macro economy.

Figure 11.8 Diversiﬁcation beneﬁts. (Source: CIBC World Markets)

While the portfolio management of credit risk has intuitive appeal, its implementation in practice is difficult because of the absence of historical data and measurement problems (e.g. correlations). Currently, while most large banks have initiatives to measure credit risk more quantitatively, few currently manage credit risk based upon the results of such measurements. Fundamental differences between credit and market risks make application of MPT problematic when applied to credit portfolios. Two important assumptions of portfolio credit risk models are: (1) the holding period or planning horizon over which losses are predicted (e.g. one year) and (2) how credit losses will be reported by the model. Models generally report either a default or market value distribution. If a model reports a default distribution, then the model would report no loss unless a default is predicted. If the model uses a market value distribution, then a decline in the market value of the asset would be reflected even if a default did not occur.15 To employ a portfolio model successfully the bank must have a reliable credit risk rating system. Within most credit risk models, the internal risk rating is a critical statistic for summarizing a facility’s probability of defaulting within the planning horizon. The models use the credit risk rating to predict the probability of default by comparing this data against: (1) publicly available historical default rates of similarly rated corporate bonds; (2) the bank’s own historical internal default data; and (3) default data experienced by other banks. A sufficiently stratified (‘granular’) credit risk rating system is also important to credit risk modeling. The more stratified the system, the more precise the model’s predictive capability can be. Moreover, greater granularity in risk ratings assists banks in risk-based pricing and can offer a competitive advantage. The objective of credit risk modeling is to identify exposures that create an

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unacceptable risk/reward profile, such as might arise from credit concentrations. Credit risk management seeks to reduce the unsystematic risk of a portfolio by diversifying risks. As banks gain greater confidence in their portfolio modeling capabilities, it is likely that credit derivatives will become a more significant vehicle to manage portfolio credit risk. While some banks currently use credit derivatives to hedge undesired exposures, much of that activity only involves a desire to reduce regulatory capital requirements, particularly for higher-grade corporate exposures that incur a high regulatory capital tax. But as credit derivatives are used to allow banks to customize their risk exposures, and separate the customer relationship and exposure functions, banks will increasingly find them helpful in applying MPT.

Overreliance on statistical models The asymmetric distribution of credit returns makes it more difficult to measure credit risk than market risk. While banks’ efforts to measure credit risk in a portfolio context can represent an improvement over existing measurement practices, portfolio managers must guard against over-reliance on model results. Portfolio models can complement, but not replace, the seasoned judgment that professional credit personnel provide. Model results depend heavily on the validity of assumptions. Banks must not become complacent as they increase their use of portfolio models, and cease looking critically at model assumptions. Because of their importance in model output, credit correlations in particular deserve close scrutiny. Risk managers must estimate credit correlations since they cannot observe them from historical data. Portfolio models use different approaches to estimating correlations, which can lead to very different estimated loss distributions for the same portfolio. Correlations are not only difficult to determine but can change significantly over time. In times of stress, correlations among assets increase, raising the portfolio’s risk profile because the systematic risk, which is undiversifiable, increases. Credit portfolio managers may believe they have constructed a diversified portfolio, with desired risk and return characteristics. However, changes in economic conditions may cause changes to default correlations. For example, when energy and Texas real estate prices became highly correlated, those correlation changes exposed banks to significant unanticipated losses. It remains to be seen whether portfolio models can identify changes in default correlation early enough to allow risk managers to take appropriate risk-reducing actions. In recent years, there have been widely publicized incidents in which inaccurate price risk measurement models have led to poor trading decisions and unanticipated losses. To identify potential weaknesses in their price risk models, most banks use a combination of independent validation, calibration, and backtesting. However, the same data limitations that make credit risk measurement difficult in the first place also make implementation of these important risk controls problematic. The absence of credit default data and the long planning horizon makes it difficult to determine, in a statistical sense, the accuracy of a credit risk model. Unlike market risk models, for which many data observations exist, and for which the holding period is usually only one day, credit risk models are based on infrequent default observations and a much longer holding period. Backtesting, in particular, is problematic and would involve an impractical number of years of analysis to reach statistically valid conclu-

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sions. In view of these problems, banks must use other means, such as assessing model coverage, verifying the accuracy of mathematical algorithms, and comparing the model against peer group models to determine its accuracy. Stress testing, in particular, is important because models use specified confidence intervals. The essence of risk management is to understand the exposures that lie outside a model’s confidence interval.

Future of credit risk management Credit risk management has two basic processes: transaction oversight and portfolio management. Through transaction oversight, banks make credit decisions on individual transactions. Transaction oversight addresses credit analysis, deal structuring, pricing, borrower limit setting, and account administration. Portfolio management, on the other hand, seeks to identify, measure, and control risks. It focuses on measuring a portfolio’s expected and unexpected losses, and making the portfolio more efficient. Figure 11.9 illustrates the efficient frontier, which represents those portfolios having the maximum return, for any given level of risk, or, for any given level of return, the minimum risk. For example, Portfolio A is inefficient because, given the level of risk it has taken, it should generate an expected return of E(REF ). However, its actual return is only E(RA).

Figure 11.9 The efﬁcient frontier.

Credit portfolio managers actively seek to move their portfolios to the efficient frontier. In practice, they find their portfolios lie inside the frontier. Such portfolios are ‘inefficient’ because there is some combination of the constituent assets that either would increase returns given risk constraints, or reduce risk given return requirements. Consequently, they seek to make portfolio adjustments that enable the portfolio to move closer toward the efficient frontier. Such adjustments include eliminating (or hedging) risk positions that do not, in a portfolio context, exhibit a satisfactory risk/reward trade-off, or changing the size (i.e. the weights) of the

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exposures. It is in this context that credit derivatives are useful, as they can allow banks to shed unwanted credit risk, or acquire more risk (without having to originate a loan) in an efficient manner. Not surprisingly, banks that have the most advanced portfolio modeling efforts tend to be the most active end-users of credit derivatives. As banks increasingly manage credit on a portfolio basis, one can expect credit portfolios to show more market-like characteristics; e.g. less direct borrower contact, fewer credit covenants, and less nonpublic information. The challenge for bank portfolio managers will be to obtain the benefits of diversification and use of more sophisticated risk management techniques, while preserving the positive aspects of more traditional credit management techniques.

Author’s note Kurt Wilhelm is a national bank examiner in the Treasury & Market Risk unit of the Office of the Comptroller of the Currency (OCC). The views expressed in this chapter are those of the author and do not necessarily reflect official positions of either the OCC or the US Department of the Treasury. The author acknowledges valuable contributions from Denise Dittrich, Donald Lamson, Ron Pasch and P. C. Venkatesh.

Notes 1

1997/1998 British Bankers Association Credit Derivatives Survey. Source: 1997/1998 British Bankers Association Credit Derivatives Survey. 3 For a discussion of the range of practice in the conceptual approaches to modeling credit risk, see the Basel Committee on Banking Supervision’s 21 April 1999 report. It discusses the choice of time horizon, the definition of credit loss, the various approaches to aggregating credits and measuring the connection between default events. 4 Banks may use VaR models to determine the capital requirements for market risk provided that such models/systems meet certain qualitative and quantitative standards. 5 Banks generally should not, however, base their business decisions solely on regulatory capital ramifications. If hedging credit risk makes economic sense, regulatory capital considerations should represent a secondary consideration. 6 On 3 June 1999, the Basel Committee issued a consultative paper proposing a new capital adequacy framework to replace the previous Capital Accord, issued in 1988. The proposal acknowledges that ‘the 1988 Accord does not provide the proper incentives for credit risk mitigation techniques’. Moreover, ‘the Accord’s structure may not have favoured the development of specific forms of credit risk mitigation by placing restrictions on both the type of hedges acceptable for achieving capital reduction and the amount of capital relief’. The Committee proposes to expand the scope for eligible collateral, guarantees, and onbalance sheet netting. 7 In this example, the dealer has exposure to credit losses exceeding the $20 cushion supplied by investors, and must implement procedures to closely monitor the value of the loans and take risk-reducing actions if losses approach $20. 8 This section describes capital requirements for end-users. Dealers use the market risk rule to determine capital requirements. Some institutions may achieve regulatory capital reduction by transferring loans from the banking book to the trading book, if they meet the quantitative and qualitative requirements of the market risk rule. 9 Note that this transaction has no impact on leverage capital (i.e. capital/assets ratio). A 2

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credit derivative hedge does not improve the leverage ratio because the asset remains on the books. 10 Low-level recourse rules are only applicable to US banks. 11 CLO transactions typically carry early amortization triggers that protect investors against rapid credit deterioration in the pool. Such triggers ensure that the security amortizes more quickly and, as a practical matter, shield investors from exposure to credit losses. 12 EAR and VaR represents an estimate of the maximum losses in a portfolio, over a specified horizon, with a given probability. One might say the VaR of a credit portfolio is $50 million over the next year, with 99% confidence, i.e. there is a 1% probability that losses will exceed $50 million in the next 12 months. 13 Banks do not hold capital against outcomes worse than required by their desired debt ratings, as measured by VaR. These scenarios are so extreme that a bank could not hold enough capital against them and compete effectively. 14 For a discussion of setting credit risk limits within a portfolio context see CreditMetricsTechnical document. 15 For an excellent discussion of credit risk modeling techniques, see Credit Risk Models at Major US Banking Institutions: Current State of the Art and Implications for Assessments of Capital Adequacy, Federal Reserve Systems Task Force on Internal Models, May 1998.

12

Operational risk MICHEL CROUHY, DAN GALAI and BOB MARK

Introduction Operational risk (OR) has not been a well-defined concept. It refers to various potential failures in the operation of the firm, unrelated to uncertainties with regard to the demand function for the products and services of the firm. These failures can stem from a computer breakdown, a bug in a major computer software, an error of a decision maker in special situations, etc. The academic literature generally relates operational risk to operational leverage (i.e. to the shape of the production cost function) and in particular to the relationship between fixed and variable cost. OR is a fuzzy concept since it is often hard to make a clear-cut distinction between OR and ‘normal’ uncertainties faced by the organization in its daily operations. For example, if a client failed to pay back a loan, is it then due to ‘normal’ credit risk, or to a human error of the loan officers that should have known better all the information concerning the client and should have declined to approve the loan? Usually all credit-related uncertainties are classified as part of business risk. However, if the loan officer approved a loan against the bank’s guidelines, and maybe he was even given a bribe, this will be classified as an OR. Therefore the management of a bank should first define what is included in OR. In other words, the typology of OR must be clearly articulated and codified. A key problem lies in quantifying operational risk. For example, how can one quantify the risk of a computer breakdown? The risk is a product of the probability and the cost of a computer breakdown. Often OR is in the form of discrete events that don’t occur frequently. Therefore, a computer breakdown today (e.g. a network related failure) is different in both probability and the size of the damage from a computer breakdown 10 years ago. How can we quantify the damage of a computer failure? What historical event can we use in order to make a rational assessment? The problems in assessing OR does not imply that they should be ignored and neglected. On the contrary, management should pay a lot of attention to understanding OR and its potential sources in the organization precisely because it is hard to quantify OR. Possible events or scenarios leading to OR should be analyzed. In the next section we define OR and discuss its typology. In some cases OR can be insured or hedged. For example, computer hardware problems can be insured or the bank can have a backup system. Given the price of insurance or the cost of hedging risks, a question arises concerning the economic rationale of removing the risks. There is the economic issue of assessing the potential loss against the certain insurance cost for each OR event. Regulators require a minimum amount of regulatory capital for price risk in the

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trading book (BIS 98) and credit risk in the banking book (BIS 88), but there are currently no formal capital requirements against operational risk. Nevertheless, the 1999 Basel conceptual paper on a comprehensive framework for arriving at the minimum required regulatory capital includes a requirement for capital to be allocated against operational risk. Previous chapters of the book are devoted to the challenges associated with capital allocation for credit and market risk. This chapter examines the challenges associated with the allocation of capital for OR. In this chapter we look at how to meet these present and future challenges by constructing a framework for operational risk control. After explaining what we think of as a key underlying rule – the control functions of a bank need to be carefully harmonized – we examine the typology of operational risk. We describe four key steps in implementing bank operational risk, and highlight some means of risk reduction. Finally, we look at how a bank can extract value from enhanced operational risk management by improving its capital attribution methodologies. Failure to identify an operational risk, or to defuse it in a timely manner, can translate into a huge loss. Most notoriously, the actions of a single trader at Barings Bank (who was able to take extremely risky positions in a market without authority or detection) led to losses ($1.5 billion) that brought about the liquidation of the bank. The Bank of England report on Barings revealed some lessons about operational risk. First, management teams have the duty to understand fully the businesses they manage. Second, responsibility for each business activity has to be clearly established and communicated. Third, relevant internal controls, including independent risk management, must be established for all business activities. Fourth, top management and the Audit Committee must ensure that significant weaknesses are resolved quickly. Looking to the future, banks are becoming aware that technology is a doubleedged sword. The increasing complexity of instruments and information systems increase the potential for operational risk. Unfamiliarity with instruments may lead to their misuse, and raise the chances of mispricing and wrong hedging; errors in data feeds may also distort the bank’s assessment of its risks. At the same time, advanced analytical techniques combined with sophisticated computer technology create new ways to add value to operational risk management. The British Bankers’ Association (BBA) and Coopers & Lybrand conducted a survey among the BBA’s members during February and March 1997. The results reflect the views of risk directors and managers and senior bank management in 45 of the BBA’s members (covering a broad spectrum of the banking industry in the UK). The survey gives a good picture of how banks are currently managing operational risk and how they are responding to it. Section I of the report indicated that many banks have some way to go to formalize their approach in terms of policies and generally accepted definitions. They pointed out that it is difficult for banks to manage operational risk on a consistent basis without an appropriate framework in place. Section II of the report indicated that experience shows that it is all too easy for different parts of a bank inadvertently to duplicate their efforts in tackling operational risk or for such risks to fall through gaps because no one has been made responsible for them. Section III of the report revealed that modeling operational risk generates the most interest of all operational risk topic areas. However, the survey results suggest that banks have not managed to progress very far in terms of arriving at generally accepted models for operations risk. The report emphasized that this may

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well be because they do not have the relevant data. The survey also revealed that data collection is an area that banks will be focusing on. Section IV revealed that more than 67% of banks thought that operational risk was as (or more) significant as either market or credit risk and that 24% of banks had experienced losses of more than £1 million in the last 3 years. Section VI revealed that the percentage of banks that use internal audit recommendations as the basis of their response to operational risk may appear high, but we suspect this is only in relation to operational risk identified by internal audit rather than all operational risks. Section VII revealed that almost half the banks were satisfied with their present approach to operational risk. However, the report pointed out that there is no complacency among the banks. Further, a majority of them expect to make changes in their approach in the next 2 years. For reasons that we discuss towards the end of the chapter, it is important that the financial industry develop a consistent approach to operational risk. We believe that our approach is in line with the findings of a recent working group of the Basel committee in autumn 1998 as well as with the 20 best-practice recommendations on derivative risk management put forward in the seminal Group of Thirty (G30) report in 1993 (see Appendix 1).

Typology of operational risks What is operational risk? Operational risk is the risk associated with operating the business. One can subdivide operational risk into two components: operational failure risk and operational strategic risk. Operational failure risk arises from the potential for failure in the course of operating the business. A firm uses people, process, and technology to achieve business plans, and any one of these factors may experience a failure of some kind. Accordingly, operational failure risk is the risk that exists within the business unit caused by the failure of people, process or technology. A certain level of the failures may be anticipated and should be built into the business plan. It is the unanticipated and therefore uncertain failures that give rise to risk. These failures can be expected to occur periodically, although both their impact and their frequency may be uncertain. The impact or the financial loss can be divided into the expected amount, the severe unexpected amount and the catastrophic unexpected amount. The firm may provide for the losses that arise from the expected component of these failures by charging revenues with a sufficient amount of reserve. The firm should set aside sufficient economic capital to cover the severe unexpected component. Operational strategic risk arises from environmental factors such as a new competitor that changes the business paradigm, a major political and regulatory regime change, earthquakes and other factors that are generally outside the control of the firm. It also arises from a major new strategic initiative, such as getting into a new line of business or redoing how current business is to be done in the future. All businesses also rely on people, processes and technology outside their business unit, and the same potential for failure exists there. This type of risk will be referred to as external dependencies. In summary, operational failure risk can arise due to the failure of people, process

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or technology and external dependencies (just as market risk can be due to unexpected changes in interest rates, foreign exchange rates, equity prices and commodity prices). In short, operational failure risk and operational strategic risk, as illustrated in Figure 12.1, are the two main categories of operational risks. They can also be defined as ‘internal’ and ‘external’ operational risks.

Figure 12.1 Two broad categories of operational risk.

This chapter focuses on operational failure risk, i.e. on the internal factors that can and should be controlled by management. However, one should observe that a failure to address a strategic risk issue could translate into an operational failure risk. For example, a change in the tax laws is an operational failure risk. Furthermore, from a business unit perspective it might be argued that external dependencies include support groups within the bank, such as information technology. In other words, the two types of operational risk are interrelated and tend to overlap.

Beginning to End Operational risk is often thought to be limited to losses that can occur in operations or processing centers (i.e. where transaction processing errors can occur). This type of operational risk, sometimes referred to as operations risk, is an important component but by no means all of the operational risks facing the firm. Operational risk can arise before, during and after a transaction is processed. Risks exist before processing, while the potential transaction is being designed, during negotiation with the client, regardless whether the negotiation is a lengthy structuring exercise or a routine electronic negotiation, and continues after the negotiation through various continual servicing of the original transaction. A complete picture of operational risk can only be obtained if the activity is analyzed from beginning to end. Take the example of a derivatives sales desk shown in Figure 12.2. Before a transaction can be negotiated several things have to be in place, and

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each exposes the firm to risk. First, sales may be highly dependent on a valued relationship between a particular sales person and the client. Second, sales are usually dependent on the highly specialized skills of the product designer to come up with both a structure and a price that the client finds more attractive than all the other competing offers. These expose the institution to key people risks. The risk arises from the uncertainty as to whether these key people continue to be available. In addition, do they have the capacity to deal with an increase in client needs or are they at full capacity dealing with too many clients to be able to handle increases in client needs? Also do the people have the capability to respond to evolving and perhaps more complex client needs? BEFORE Identify client need RISK: • key people in key roles, esp. for valued client

DURING Structure transaction RISK: • models risk • disclosure • appropriateness

AFTER Deliver product RISK: • limit monitoring • model risk • key person continuity

Figure 12.2 Where operational risk occurs.

The firm is exposed to several risks during the processing of the transaction. First, the sales person may either willingly or unwillingly not fully disclose the full range of the risk of the transaction to a client. This may be a particular high risk during periods of intense pressure to meet profit and therefore bonus targets for the desk. Related to this is the risk that the sales person persuades the client to engage in a transaction that is totally inappropriate for the client, exposing the firm to potential lawsuits and regulatory sanctions. This is an example of people risk. Second, the sales person may rely on sophisticated financial models to price the transaction, which creates what is commonly, called model risk. The risk arises because the model may be used outside its domain of applicability, or the wrong inputs may be used. Once the transaction is negotiated and a ticket is written, several errors may occur as the transaction is recorded in the various systems or reports. For example, an error may result in delayed settlement giving rise to late penalties, it may be misclassified in the risk reports, understating the exposure and lead to other transactions that would otherwise not have been performed. These are examples of process risk. The system which records the transaction may not be capable of handling the transaction or it may not have the capacity to handle such transactions, or it may not be available (i.e. it may be down). If any one of the steps is outsourced, such as phone transmission, then external dependency risk arises. The list of what can go wrong before, during, and after the transaction, is endless. However, each type of risk can be broadly captured as a people, a process, a technology risk, or an external dependency risk and in turn each can be analyzed in terms of capacity, capability or availability

Who manages operational risk? We believe that a partnership between business, infrastructure, internal audit and risk management is the key to success. How can this partnership be constituted? In

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particular, what is the nature of the relationship between operational risk managers and the bank audit function? The essentials of proper risk management require that (a) appropriate policies be in place that limit the amount of risk taken and (b) authority be provided to change the risk profile, to those who can take action, and (c) that timely and effective monitoring of the risk is in place. No one group can be responsible for setting policies, taking action, and monitoring the risk taken, for to do so would give rise to all sorts of conflict of interest Policy setting remains the responsibility of senior management, even though the development of those policies may be delegated, and submitted to the board of directors for approval (see Figure 12.3).

Figure 12.3 Managing operational risk.

The authority to take action rests with business management, who are responsible for controlling the amount of operational risk taken within their business. Business management often relies on expert areas such as information technology, operations, legal, etc. to supply it with services required to operate the business. These infrastructure and governance groups share with business management the responsibility for managing operational risk. The responsibility for the development of the methodology for measuring operational risk resides with risk management. Risk management also needs to make risks transparent through monitoring and reporting. Risk management should also portfolio manage the firm’s operational risk. Risk management can actively manage residual risk through using tools such as insurance. Portfolio management adds value by ensuring that operational risk is adequately capitalized as well as analyzed for operational risk concentration. Risk management is also responsible for providing a regular review of trends, and needs to ensure that proper operational risk reward analysis is performed in the review of existing business as well as before the introduction of new initiatives and products. In this regard risk management works very closely but is independent of the business infrastructure, and the other governance groups. Operational risk is often managed on an ad hoc basis. and banks can suffer from a lack of coordination among functions such as risk management, internal audit,

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and business management. Most often there are no common bank-wide policies, methodologies or infrastructure. As a result there is also often no consistent reporting on the extent of operational risk within the bank as a whole. Furthermore, most bank-wide capital attribution models rarely incorporate sophisticated measures of operational risk. Senior management needs to know if the delegated responsibilities are actually being followed and if the resulting processes are effective. Internal audit is charged with this responsibility. Audit determines the effectiveness and integrity of the controls that business management puts in place to keep risk within tolerable levels. At regular intervals the internal audit function needs to ensure that the operational risk management process has integrity, and is indeed being implemented along with the appropriate controls. In other words, auditors analyze the degree to which businesses are in compliance with the designated operational risk management process. They also offer an independent assessment of the underlying design of the operational risk management process. This includes examining the process surrounding the building of operational risk measurement models, the adequacy and reliability of the operations risk management systems and compliance with external regulatory guidelines, etc. Audit thus provides an overall assurance on the adequacy of operational risk management. A key audit objective is to evaluate the design and conceptual soundness of the operational value-at-risk (VaR) measure, including any methodologies associated with stress testing, and the reliability of the reporting framework. Audit should also evaluate the operational risks that affect all types of risk management information systems – whether they are used to assess market, credit or operational risk itself – such as the processes used for coding and implementation of the internal models. This includes examining controls concerning the capture of data about market positions, the accuracy and completeness of this data, as well as controls over the parameter estimation processes. Audit would typically also review the adequacy and effectiveness of the processes for monitoring risk. and the documentation relating to compliance with the qualitative/quantitative criteria outlined in any regulatory guidelines. Regulatory guidelines typically also call for auditors to examine the approval process for vetting risk management models and valuation models used by frontand back-office personnel (for reasons made clear in Appendix 2). Auditors also need to examine any significant change in the risk measurement process. Audit should verify the consistency, timeliness and reliability of data sources used to run internal models, including the independence of such data sources. A key role is to examine the accuracy and appropriateness of volatility and correlation assumptions as well as the accuracy of the valuation and risk transformation calculations. Finally, auditors should examine the verification of the model’s accuracy through an examination of the backtesting process.

The key to implementing bank-wide operational risk management In our experience, eight key elements (Figure 12.4) are necessary to successfully implement such a bank-wide operational risk management framework. They involve

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setting policy and establishing a common language of risk identification. One would need to construct business process maps as well as to build a best-practice measurement methodology One would also need to provide exposure management as well as to allocate a timely reporting capability Finally, one wouyld need to perform risk analysis (inclusive of stress testing) as well as to allocate economic capital. Let’s look at these in more detail.

Figure 12.4 Eight key elements to achieve best practice operational risk management.

1 Develop well-defined operational risk policies. This includes articulating explicitly the desired standards for risk measurement. One also needs to establish clear guidelines for practices that may contribute to a reduction of operational risks. For example, the bank needs to establish policies on model vetting, off-hour trading, off-premises trading, legal document vetting, etc. 2 Establish a common language of risk identification. For example, people risk would include a failure to deploy skilled staff. Process risk would include execution errors. Technology risk would include system failures, etc. 3 Develop business process maps of each business. For example, one should map the business process associated with the bank’s dealing with a broker so that it becomes transparent to management and auditors. One should create an ‘operational risk catalogue’ as illustrated in Table 12.1 which categorizes and defines the various operational risks arising from each organizational unit This includes analyzing the products and services that each organizational unit offers, and the action one needs to take to manage operational risk. This catalogue should be a tool to help with operational risk identification and assessment. Again, the catalogue should be based on common definitions and language (as Reference Appendix 3). 4 Develop a comprehensible set of operational risk metrics. Operational risk assessment is a complex process and needs to be performed on a firm-wide basis at regular intervals using standard metrics. In the early days, as illustrated in Figure 12.5, business and infrastructure groups performed their own self-assessment of operational risk. Today, self-assessment has been discredited – the self-assessment of operational risk at Barings Bank contributed to the build-up of market risk at that institution – and is no longer an acceptable approach. Sophisticated financial institutions are trying to develop objective measures of operational risk that build significantly more reliability into the quantification of operational risk. To this end, operational risk assessment needs to include a review of the likelihood of a particular operational risk occurring as well as the severity or magnitude of

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The Professional’s Handbook of Financial Risk Management Table 12.1 Types of operational failure risks 1 People risk:

Incompetency Fraud etc.

2 Process risk: A Model risk (see Appendix 2)

Mark-to-model error Model/methodology error etc. Execution error Product complexity Booking error Settlement error Documentation/contract risk etc. Exceeding limits Security risks Volume risk etc.

B Transaction risk

C Operational control risk

3 Technology risk:

5

6 7

8

System failure Programming error Information risk (see Appendix 4) Telecommunication failure etc.

the impact that the operational risk will have on business objectives. This is no easy task. It can be challenging to assess the probability of a computer failure (or of a programming bug in a valuation model) and to assign a potential loss to any such event. We will examine this challenge in more detail in the next section of this chapter. Decide how one will manage operational risk exposure and take appropriate action to hedge the risks. For example, a bank should address the economic question of the cost–benefit of insuring a given risk for those operational risks that can he insured. Decide on how one will report exposure. For example, an illustrative summary report for the Tokyo equity arbitrage business is shown in Table 12.2. Develop tools for risk analysis and procedures for when these tools should be deployed. For example, risk analysis is typically performed as part of a new product process, periodic business reviews, etc. Stress testing should be a standard part of the risk analyst process. The frequency of risk assessment should be a function of the degree to which operational risks are expected to change over time as businesses undertake new initiatives, or as business circumstances evolve. A bank should update its risk assessment more frequently (say, semiannually) following the initial assessment of operational risk. Further, one should reassess the operational risk whenever the operational risk profile changes significantly (e.g. implementation of a new system, entering a new service, etc). Develop techniques to translate the calculation of operational risk into a required amount of economic capital. Tools and procedures should be developed to enable one to make decisions about operational risk based on incorporating operational

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Figure 12.5 The process of implementing operational risk management.

risk capital into the risk reward analyses, as we discuss in more detail later in the chapter. Clear guiding principles for the operational risk process should be set to ensure that it provides an appropriate measure of operational risk across all business units throughout the bank. These principles are illustrated in Figure 12.6. Objectivity refers to the principle that operational risk should be measured using standard objective criteria. ‘Consistency’ refers to ensuring that similar operational risk profiles in different business units result in similar reported operational risks. Relevance refers to the idea that risk should be reported in a way that makes it easier to take action to address the operational risk. ‘Transparency’ refers to ensuring that all material operational risks are reported and assessed in a way that makes the risk transparent to senior managers. ‘Bank-wide’ refers to the principle that operational risk measures should be designed so that the results can be aggregated across the entire organization. Finally, ‘completeness’ refers to ensuring that all material operational risks are identified and captured.

A four-step measurement process for operational risk As pointed out earlier, one can assess the amount of operational risk in terms of the likelihood of operational failure (net of mitigating controls) and the severity of potential financial loss (given that a failure occurs). This suggests that one should measure operational risk using the four-step operational risk process illustrated in Figure 12.7. We discuss each step below.

Input (step 1) The first step in the operational risk measurement process is to gather the information needed to perform a complete assessment of all significant operational risks. A key

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The Professional’s Handbook of Financial Risk Management Table 12.2 Operational risk reporting worksheet The overall operational risk of the Tokyo Equity Arbitrage Trading desk is Low Risk proﬁle 1. People risk Incompetency Fraud

Low Low

2. Process risk A. Model risk Mark-to-model error Model/methodology error

Low Low

B. Transaction risk: Execution error Product complexity Booking error Settlement error Documentation/contract risk

Low Low Low Low Medium

C. Operational control risk Exceeding limits Security risk Volume risk

Low Low Low/medium

3. Technology risk System failure Programming error Information risk Telecommunication failure

Low Low Low Low

Total operational failure risk measurement Strategic risk Political risk Taxation risk Regulatory risk Total strategic risk measurement

Low Low Low Low/medium Low

source of this information is often the finished products of other groups. For example. a unit that supports a business group often publishes reports or documents that may provide an excellent starting point for the operational risk assessment. Relevant and useful reports (e.g. Table 12.3) include audit reports, regulatory reports, etc. The degree to which one can rely on existing documents for control assessment varies. For example, if one is relying on audit documents as an indication of the degree of control, then one needs to ask if the audit assessment is current and sufficient. Have there been any significant changes made since the last audit assessment? Did the audit scope include the area of operational risk that is of concern to the present risk assessment? Gaps in information are filled through discussion with the relevant managers. Information from primary sources needs to be validated, and updated as necessary. Particular attention should be paid to any changes in the business or operating environment since the information was first produced. Typically, sufficient reliable historical data is not available to project the likelihood

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Guiding Principles

Objectivity

Consistency

Relevance

Transparency

Bankwide

Completeness

Risk measured using standard criteria

Same risk profiles result in same reported

Reported risk is actionable

All material risks are reported

Risk can be aggregated across entire

All material risks are identified and captured

organization

Figure 12.6 Guiding principles for the operational risk measurement.

Figure 12.7 The operational risk measurement process.

or severity of operational losses with confidence. One often needs to rely on the expertise of business management. The centralized operational risk management group (ORMG) will need to validate any such self-assessment by a business unit in a disciplined way. Often this amounts to a ‘reasonableness’ check that makes use of historical information on operational losses within the business and within the industry as a whole. The time frame employed for all aspects of the assessment process is typically one year. The one-year time horizon is usually selected to align with the business planning cycle of the bank. Nevertheless, while some serious potential operational failures may not occur until after the one-year time horizon, they should be part of the current risk assessment. For example, in 1998 one may have had key employees

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The Professional’s Handbook of Financial Risk Management Table 12.3 Sources of information in the measurement process of operational risk – the input Assessment for: Likelihood of occurrence Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω

Severity

Audit reports Ω Management interviews Regulatory reports Ω Loss history Management reports Ω etc. Expert opinion BRP (Business Recovery Plan) Y2K (year 2000) reports Business plans Budget plans Operations plans etc.

Figure 12.8 Second step in the measurement process of operational risk: risk assessment framework. VH: very high, H: high, M: medium, L: low, VL: very low.

under contract working on the year 2000 problem – the risk that systems will fail on 1 January 2000. These personnel may be employed under contracts that terminate more than 12 months into the future. However, while the risk event may only occur beyond the end of the current one-year review period, current activity directed at mitigating the risk of that future potential failure should be reviewed for the likelihood of failure as part of the current risk assessment.

Risk assessment framework (step 2) The ‘input’ information gathered in step 1 needs to be analyzed and processed through the risk assessment framework sketched in Figure 12.8. The risk of unexpec-

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ted operational failure, as well as the adequacy of management processes and controls to manage this risk, needs to be identified and assessed. This assessment leads to a measure of the net operational risk, in terms of likelihood and severity. Risk categories We mentioned earlier that operational risk can be broken down into four headline risk categories (representing the risk of unexpected loss) due to operational failures in people, process and technology deployed within the business – collectively the internal dependencies and external dependencies. Internal dependencies should each be reviewed according to a common set of factors. Assume, for illustrative purposes, that the common set of factors consist of three key components of capacity, capability and availability. For example, if we examine operational risk arising from the people risk category then one can ask: Ω Does the business have enough people (capacity) to accomplish its business plan? Ω Do the people have the right skills (capability)? Ω Are the people going to be there when needed (availability)? External dependencies are also analyzed in terms of the specific type of external interaction. For example, one would look at clients (external to the bank, or an internal function that is external to the business unit under analysis). Net operational risk Operational risks should be evaluated net of risk mitigants. For example, if one has insurance to cover a potential fraud then one needs to adjust the degree of fraud risk by the amount of insurance. We expect over time that insurance products will play an increasingly larger role in the area of mitigating operational risk. Connectivity and interdependencies The headline risk categories cannot be viewed in isolation from one another. Figure 12.9 illustrates the idea that one needs to examine the degree of interconnected risk exposure across the headline operational risk categories in order to understand the full impact of any risk. For example, assume that a business unit is introducing a new computer technology. The implementation of that new technology may generate

Figure 12.9 Connectivity of operational risk exposure.

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a set of interconnected risks across people, process and technology. For example, have the people who are to work with the new technology been given sufficient training and support? All this suggests that the overall risk may be higher than that accounted for by each of the component risks considered individually. Change, complexity, complacency One should also examine the sources that drive the headline categories of operational risk. For example, one may view the drivers as falling broadly under the categories of change, complexity, and complacency. Change refers to such items as introducing new technology or new products, a merger or acquisition, or moving from internal supply to outsourcing, etc. Complexity refers to such items as complexity in products, process or technology. Complacency refers to ineffective management of the business, particularly in key operational risk areas such as fraud, unauthorized trading, privacy and confidentiality, payment and settlement, model use, etc. Figure 12.10 illustrates how these underlying sources of a risk connect to the headline operational risk categories.

Figure 12.10 Interconnection of operational risks.

Net likelihood assessment The likelihood that an operational failure may occur within the next year should be assessed (net of risk mitigants such as insurance) for each identified risk exposure and for each of the four headline risk categories (i.e. people, process and technology, and external dependencies). This assessment can be expressed as a rating along a

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five-point likelihood continuum from very low (VL) to very high (VH) as set out in Table 12.4. Table 12.4 Five-point likelihood continuum Likelihood that an operational failure will occur within the next year VL L M H VH

Very low (very unlikely to happen: less than 2%) Low (unlikely: 2-5%) Medium (may happen: 5–10%) High (likely to happen: 10–20%) Very high (very likely: greater than 20%)

Severity assessment Severity describes the potential loss to the bank given that an operational failure has occurred. Typically, this will be expressed as a range of dollars (e.g. $50–$100 million), as exact measurements will not usually be possible. Severity should be assessed for each identified risk exposure. As we mentioned above, in practice the operational risk management group is likely to rely on the expertise of business management to recommend appropriate severity amounts. Combining likelihood and severity into an overall operational risk assessment Operational risk measures are not exact in that there is usually no easy way to combine the individual likelihood of loss and severity assessments into an overall measure of operational risk within a business unit. To do so, the likelihood of loss would need to be expressed in numerical terms – e.g. a medium risk represents a 5–10% probability of occurrence. This cannot be accomplished without statistically significant historical data on operational losses. The financial industry for the moment measures operational risk using a combination of both quantitative and qualitative points of view. To be sure, one should strive to take a quantitative approach based on statistical data. However, where the data is unavailable or unreliable – and this is the case for many risk sources – a qualitative approach can be used to generate a risk rating. Neither approach on its own tells the whole story: the quantitative approach is often too rigid, while the qualitative approach is often too vague. The hybrid approach requires a numerical assignment of the amount at risk based on both quantitative and qualitative data. Ideally, one would also calculate the correlation between the various risk exposures and incorporate this into the overall measure of business or firm-wide risk. Given the difficulty of doing this, for the time being risk managers are more likely to simply aggregate individual seventies assessed for each operational risk exposure. Deﬁning cause and effect One should analyze cause and effect of an operational loss. For example, failure to have an independent group vet all mathematical models is a cause, and a loss event arising from using erroneous models is the effect (see Table 12.5). Loss or effect data is easier to collect than the causes of loss data. There may be many causes to one loss. The relationship can be highly subjective and the importance of each cause difficult to assess. Most banks start by collecting the losses and then try to fit the causes to them. The methodology is typically developed later, after

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The Professional’s Handbook of Financial Risk Management Table 12.5 Risk categories: causes, effects and source

Risk category

The cause The effect

People (human resource)

Ω Loss of key staff due to defection of key staff to competitor

Variance in revenues/proﬁts (e.g. cost of recruiting replacements, costs of training, disruption to existing staff)

Process

Ω Declining productivity as volume grows

Variance in process costs from predicted levels (excluding process malfunctions)

Technology

Ω Year 2000 upgrade expenditure Ω Application development

Variance in technology running costs from predicted levels

Source: Extracted from a table by Duncan Wilson, Risk Management Consulting, IBM Global Financial Markets

the data has been collected. One needs to develop a variety of empirical analyses to test the link between cause and effect. Sample risk assessment report What does this approach lead to when put into practice? Assume we have examined Business Unit A and have determined that the sources of operational risk are related to: Ω Ω Ω Ω Ω Ω

outsourcing privacy compliance fraud downsizing and the political environment.

The sample report, as illustrated in Table 12.6, shows that the business has an overall ‘low’ likelihood of operational loss within the next 12 months. Observe that the assessment has led to an overall estimate of the severity as ranging from $150 to $300 million. One typically could display for each business unit a graph showing the relationship between severity and likelihood across each operational risk type (see Appendix 4). The summary report typically contains details of the factors considered in making a likelihood assessment for each operational risk exposure (broken down by people, process, technology and external dependencies) given an operational failure.

Review and validation (step 3) What happens after such a report has been generated? First. the centralized operational risk management group (ORMG) reviews the assessment results with senior business unit management and key officers in order to finalize the proposed operational risk rating. Key officers include those with responsibility for the management and control of operational activities (such as internal audit, compliance, IT, human resources, etc.). Second, ORMG can present its recommended rating to an operational risk rating review committee – a process similar that followed by credit rating agencies

Operational risk

359 Table 12.6 Example of a risk assessment report for Business Unit A Likelihood of event (in 12 months)

Operational risk scenarios

Outsourcing Privacy Compliance Fraud Downsizing Political environment Overall assessment

Internal dependencies People

Process

Technology

L L L L I VL L

VL M VI L VL M M

VL VL VL VL VL VL VL

External dependencies

Overall assessment

Severity ($million)

M L VL VL L VL L

M L L L L L L

50–100 50–100 35–70 5–10 5–10 5–10 150–300

such as Standard & Poors. The operational risk committee comments on the ratings prior to publication. ORMG may clarify or amend its original assessment based on feedback from the committee. The perational risk committee reviews the individual risk assessments to ensure that the framework has been consistently applied across businesses. The committee should have representation from business management, audit, functional areas, and chaired by risk management. Risk management retains the right to veto.

Output (step 4) The final assessment of operational risk should be formally reported to business management, the centralized Raroc group, and the partners in corporate governance (such as internal audit. compliance, etc.). As illustrated in Figure 12.11, the output of the assessment process has two main uses. First, the assessment provides better operational risk information to management for use in improving risk management decisions. Second, the assessment improves the allocation of economic capital to better reflect the extent of operational risk being taken by a business unit (a topic we discuss in more detail below). Overall, operational risk assessment guides management action – for example, in deciding whether to purchase insurance to mitigate some of the risks.

Figure 12.11 Fourth step in the measurement process of operational risk: output.

The overall assessment of the likelihood of operational risk and severity of loss for a business unit can be plotted to provide relative information on operational risk exposures across the bank (or a segment of the bank) as shown in Figure 12.12 (see

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Figure 12.12 Summary risk reporting.

also Appendix 4). Of course, Figure 12.12 is a very simplified way of representing risk, but presenting a full probability distribution for many operational risks is too complex to be justified – and may even be misleading given the lack of historical evidence. In Figure 12.12, one can see very clearly that if a business unit falls in the upper right-hand quadrant then the business unit has a high likelihood of operational risk and a high severity of loss (if failure occurs). These units would be the focus of management’s attention. A business unit may address its operational risks in several ways. First, one can avoid the risk by withdrawing from a business activity. Second, one can transfer the risk to another party (e.g. through more insurance or outsourcing). Third, one can accept and manage the risk, say, through more effective management. Fourth, one can put appropriate fallback plans in place in order to reduce the impact should an operational failure occur. For example, management can ask several insurance companies to submit proposals for insuring key risks. Of course, not all operational risks are insurable, and in the case of those that are insurable the required premium may be prohibitive.

Capital attribution for operational risks One should make sure that businesses that take on operational risk incur a transparent capital charge. The methodology for translating operational risk into capital is typically developed by the Raroc group in partnership with the operational risk management group. Operational risks can be divided into those losses that are expected and those that are unexpected. Management, in the ordinary course of business, knows that certain operational activities will fail. There will be a ‘normal’ amount of operational loss that the business is willing to absorb as a cost of doing business (such as error correction, fraud, etc.). These failures are explicitly or implicitly budgeted for in the annual business plan and are covered by the pricing of the product or service. The focus of this chapter, as illustrated in Figure 12.13, has been on unexpected

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U n expected

E xpe cted

C atastro ph ic

Likeliho od o f L oss

Severe

S everity of Loss Figure 12.13 Distribution of operational losses.

failures, and the associated amount of economic capital that should be attributed to business units to absorb the losses related to the unexpected operational failures. However, as the figure suggests, unexpected failures can themselves be further subdivided: Ω Severe but not catastrophic losses. Unexpected severe operational failures, as illustrated in Table 12.7, should be covered by an appropriate allocation of operational risk capital These kinds of losses will tend to be covered by the measurement processes described in the sections above. Table 12.7 Distribution of operational losses

Operational losses Covered by

Expected event (high probability, low losses) Business plan

Unexpected event (low probability, high losses) Severe ﬁnancial impact

Catastrophic ﬁnancial impact

Operational risk capital

Insurable (risk transfer) or ‘risk ﬁnancing’

Ω Catastrophic losses. These are the most extreme but also the rarest forms of operational risk events – the kind that might destroy the bank entirely. Value-at-Risk (VaR) and Raroc models are not meant to capture catastrophic risk, since potential losses are calculated up to a certain confidence level and catastrophic risks are by their very nature extremely rare. Banks will attempt to find insurance coverage to hedge catastrophic risk since capital will not protect a bank from these risks. Although VaR/Raroc models may not capture catastrophic loss, banks can use these approaches to assist their thought process about insurance. For example, it might be argued that one should retain the risk if the cost of capital to support the asset is less than the cost of insuring it. This sort of risk/reward approach can bring discipline to an insurance program that has evolved over time into a rather ad hoc

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set of policies – often where one type of risk is insured while another is not, with very little underlying rationale. Banks have now begun to develop databases of historical operational risk events in an effort to quantify unexpected risks of various types. They are hoping to use the databases to develop statistically defined ‘worst case’ estimates that may be applicable to a select subset of a bank’s businesses – in the same way that many banks already use historical loss data to drive credit risk measurement. A bank’s internal loss database will most likely be extremely small relative to the major losses in certain other banks. Hence, the database should also reflect the experience of others. Blending internal and external data requires a heavy dose of management judgement. This is a new and evolving area of risk measurement. Some banks are moving to an integrated or concentric approach to the ‘financing’ of operational risks. This financing can be achieved via a combination of external insurance programs (e.g. with floors and caps), capital market tools and self-insurance. If the risk is self-insured, then the risk should be allocated economic capital. How will the increasing emphasis on operational risk and changes in the financial sector affect the overall capital attributions in banking institutions? In the very broadest terms, we would guess that the typical capital attributions in banks now stand at around 20% for operational risk, 10% for market risk, and 70% for credit risk (Figure 12.14). We would expect that both operational risk and market risk might evolve in the future to around 30% each – although, of course, much depends on the nature of the institution. The likely growth in the weighting of operational risk can be attributed to the growing risks associated with people, process, technology and external dependencies. For example, it seems inevitable that financial institutions will experience higher worker mobility, growing product sophistication, increases in business volume, rapid introduction of new technology and increased merger/ acquisitions activity – all of which generate operational risk.

Figure 12.14 Capital attribution: present and future.

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Self-assessment versus risk management assessment Some would argue that the enormity of the operational risk task implies that the only way to achieve success in terms of managing operational risk without creating an army of risk managers is to have business management self-assess the risks. However, this approach is not likely to elicit the kind of necessary information to effectively control operational risk. It is unlikely that a Nick Leeson would have selfassessed his operational risk accurately. In idealized circumstances senior management aligns, through the use of appropriate incentives, the short- and perhaps long-term interest of the business manager with those of the corporation as a whole. If we assume this idealized alignment then business management is encouraged to share their view of both the opportunities and the risk with senior management. Self-assessment in this idealized environment perhaps would produce an accurate picture of the risk. However, a business manager in difficult situations (that is, when the risks are high) may view high risk as temporary and therefore may not always be motivated towards an accurate selfassessment. In other words, precisely when an accurate measurement of the operational risk would be most useful is when self-assessment would give the most inaccurate measurement. Risk management should do the gathering and processing of this data to ensure objectivity, consistency and transparency. So how is this to be done without the army of risk management personnel? First, as described earlier, a reasonable view of the operational risk can be constructed from the analysis of available information, business management interviews, etc. This can be accomplished over a reasonable timeframe with a small group of knowledgeable risk managers. Risk managers (who have been trained to look for risk and have been made accountable for obtaining an accurate view of the risk at a reasonable cost) must manage this trade-off between accuracy, granularity and timeliness. Second, risk managers must be in the flow of all relevant business management information. This can be accomplished by having risk managers sit in the various regular business management meetings, involved in the new product approval process, and be the regular recipient of selected management reports, etc. This is the same as how either a credit risk manager or a market risk manager keeps a timely and a current view of their respective risks. A second argument often used in favor of self-assessment is that an operational risk manager cannot possibly know as much about the business as the business manager, and therefore a risk assessment by a risk manager will be incomplete or inaccurate. This, however, confuses their respective roles and responsibilities. The business manager should know more about the business than the risk manager, otherwise that itself creates an operational risk and perhaps the risk manager should be running the business. The risk manager is trained in evaluating risk, much like a life insurance risk manager is trained to interpret the risk from a medical report and certain statistics. The risk manager is neither expected to be a medical expert nor even to be able to produce the medical report, only to interpret and extract risk information. This, by the way, is the same with a credit risk manager. A credit risk manager is expected to observe, analyze, interpret information about a company so as to evaluate the credit risk of a company, not be able to manage that company. To demand more from an operational risk manager would be to force that risk manager to lose focus and therefore reduce their value added. Operational risk can be

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mitigated by training personnel on how to use the tools associated with best practice risk management. (see Appendix 5.)

Integrated operational risk At present, most financial institutions have one set of rules to measure market risk, a second set of rules to measure credit risk, and are just beginning to develop a third set of rules to measure operational risk. It seems likely that the leading banks will work to integrate these methodologies (Figure 12.15). For example, they might attempt to first integrate market risk VaR and credit risk VaR and subsequently work to integrate an operational risk VaR measure.

Regulatory Capital

Economic Capital INTEGRATED FRAMEWORK

Market Risk VaR

Credit Risk VaR Operational Risk VaR Figure 12.15 Integrated risk models.

Developing an integrated risk measurement model will have important implications from both a risk transparency and a regulatory capital perspective. For example, if one simply added a market risk VaR plus an operational risk VaR plus a credit risk VaR to obtain a total VaR (rather than developing an integrated model) then one would overstate the amount of risk. The summing ignores the interaction or correlation between market risk, credit risk and operational risk. The Bank for International Settlement (1988) rules for capital adequacy are generally recognized to be quite flawed. We would expect that in time regulators will allow banks to use their own internal models to calculate a credit risk VaR to replace the BIS (1988) rules, in the same way that the BIS 1998 Accord allowed banks to adopt an internal models approach for determining the minimum required regulatory capital for trading market risk. For example, we would expect in the near term that BIS would allow banks to use their own internal risk-grading system for purposes of arriving at the minimum required regulatory capital for credit risk. The banking industry, rather than the regulators, sponsored the original market VaR methodology. (In particular, J. P. Morgan’s release of its RiskMetrics product.) Industry has also sponsored the new wave of credit VaR methodologies such as the

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J. P. Morgan CreditMetrics offering, and CreditRiskò from Credit Suisse Financial Products. Similarly, vendor-led credit VaR packages include a package developed by KMV (which is now in use at 60 financial institutions). The KMV model is based on an expanded version of the Merton model to allow for an empirically accurate approximation in lieu of a theoretically precise approach. All this suggests that, in time the banking industry will sponsor some form of operational risk VaR methodology. We can push the parallel a little further. The financial community, with the advent of products such as credit derivatives, is increasingly moving towards valuing loan products on a mark-to-model basis. Similarly, with the advent of insurance products we will see increased price discovery for operational risk. Moreover, just as we have seen an increasing trend toward applying market-risk-style quantification techniques to measure the credit VaR, we can also expect to see such techniques applied to develop an operational risk VaR. Accounting firms (such as Arthur Anderson) are encouraging the development of a common taxonomy of risk (see Appendix 6). Consulting firms (such as Net Risk) are facilitating access to operational risk data (see Appendix 7). A major challenge for banks is to produce comprehensible and practical approaches to operational risk that will prove acceptable to the regulatory community. Ideally, the integrated risk model of the future will align the regulatory capital approach to operational risk with the economic capital approach.

Conclusions An integrated goal-congruent risk management process that puts all the elements together, as illustrated in Figure 12.16, will open the door to optimal firm-wide management of risk. ‘Integrated’ refers to the need to avoid a fragmented approach to risk management – risk management is only as strong as the weakest link. ‘Goalcongruent’ refers to the need to ensure that policies and methodologies are consistent with each other. Infrastructure includes having the right people, operations technology and data to appropriately control risk. One goal is to have an ‘apple-to-apple’ risk measurement scheme so that one can compare risk across all products and aggregate risk at any level. The end product is a best-practice management of risk that is also consistent with business strategies. This is a ‘one firm, one view’ approach that also recognizes the complexity of each business within the firm. In this chapter we have stressed that operational risk should be managed as a partnership among business units, infrastructure groups, corporate governance units, internal audit and risk management. We should also mention the importance of establishing a risk-aware business culture. Senior managers play a critical role in establishing a corporate environment in which best-practice operational risk management can flourish. Personnel will ultimately behave in a manner dependent on how senior management rewards them. Indeed, arguably the key single challenge for senior management is to harmonize the behavior patterns of business units, infrastructure units, corporate governance units, internal audit and risk management and create an environment in which all sides ‘sink or swim’ together in terms of managing operational risk.

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Hardware

ion licat App ware Soft

Business Strategies

Technology

Risk Tolerance

Accurate Data

Operations

Best Practice Infrastructure

Independent First Class Active Risk Management • • • • • • •

People (Skills)

Best Practice Policies Authorities

Limits Management Risk Analysis Capital attribution Pricing Risk Portfolio Management Risk Education etc.

Disclosure

Best Practice Methodologies (Formulas)

RARO RA C

Pricing and Valuation

Market and Credit Risk

Operational Risk

Figure 12.16 Best practice risk management.

Appendix 1: Group of Thirty recommendations Derivatives and operational risk In 1993 the Group of Thirty (G30) provided 20 best-practice risk management recommendations for dealers and end-users of derivatives. These have proved seminal for many banks structuring their derivatives risk management functions, and here we offer a personal selection of some key findings for operational risk managers in institutions who may be less familiar with the report. The G30 working group was composed of a diverse cross-section of end-users, dealers, academics, accountants, and lawyers involved in derivatives. Input also came from a detailed survey of industry practice among 80 dealers and 72 end-users worldwide, involving both questionnaires and in-depth interviews. In addition, the G30 provides four recommendations for legislators, regulators, and supervisors. The G30 report noted that the credit, market and legal risks of derivatives capture

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most of the attention in public discussion. Nevertheless, the G30 emphasized that the successful implementation of systems operations, and controls is equally important for the management of derivatives activities. The G30 stressed that the complexity and diversity of derivatives activities make the measurement and control of those risks more difficult. This difficulty increases the importance of sophisticated risk management systems and sound management and operating practices These are vital to a firm’s ability to execute, record, and monitor derivatives transactions, and to provide the information needed by management to manage the risks associated with these activities. Similarly, the G30 report stressed the importance of hiring skilled professionals: Recommendation 16 states that one should ‘ensure that derivatives activities are undertaken by professionals in sufficient number and with the appropriate experience, skill levels, and degrees of specialization’. The G30 also stressed the importance of building best-practice systems. According to Recommendation 17, one should ‘ensure that adequate systems for data capture, processing, settlement, and management reporting are in place so that derivatives transactions are conducted in an orderly and efficient manner in compliance with management policies’. Furthermore, ‘one should have risk management systems that measure the risks incurred in their derivatives activities based on their nature, size and complexity’. Recommendation 19 emphasized that accounting practices should highlight the risks being taken. For example, the G30 pointed out that one ‘should account for derivatives transactions used to manage risks so as to achieve a consistency of income recognition treatment between those instruments and the risks being managed’.

People The survey of industry practices examined the involvement in the derivatives activity of people at all levels of the organization and indicated a need for further development of staff involved in back-office administration, accounts, and audit functions, etc. Respondents believed that a new breed of specialist, qualified operational staff, was required. It pointed out that dealers (large and small) and end-users face a common challenge of developing the right control culture for their derivatives activity. The survey highlighted the importance of the ability of people to work in cross functional teams. The survey pointed out that many issues require input from a number of disciplines (e.g. trading, legal and accounting) and demand an integrated approach.

Systems The survey confirmed the view that dealing in derivatives can demand integrated systems to ensure adequate information and operational control. It indicated that dealers were moving toward more integrated systems, between front- and back-office (across types of transactions). The industry has made a huge investment in systems, and almost all large dealers are extensive users of advanced technology. Many derivative groups have their own research and technology teams that develop the mathematical algorithms and systems necessary to price new transactions and to monitor their derivatives portfolios. Many dealers consider their ability to manage the development of systems capabilities an important source of competitive strength. For large dealers there is a requirement that one develop systems that minimize

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manual intervention as well as enhance operating efficiency and reliability, the volume of activity, customization of transactions, number of calculations to be performed, and overall complexity. Systems that integrate the various tasks to be performed for derivatives are complex Because of the rapid development of the business, even the most sophisticated dealers and users often rely on a variety of systems, which may be difficult to integrate in a satisfactory manner. While this situation is inevitable in many organizations, it is not ideal and requires careful monitoring to ensure sufficient consistency to allow reconciliation of results and aggregation of risks where required. The survey results indicated that the largest dealers, recognizing the control risks that separate systems pose and the expense of substantial daily reconciliations, are making extensive investments to integrate back-office systems for derivatives with front-office systems to derivatives as well as other management information.

Operations The role of the back-office is to perform a variety of functions in a timely fashion. This includes recording transactions, issuing and monitoring confirmations, ensuring legal documentation for transactions is completed, settling transactions, producing information for management and control purposes. This information includes reports of positions against trading and counterparty limits, reports on profitability, and reports on exceptions. There has been significant evolution in the competence of staff and the adequacy of procedures and systems in the back office. Derivatives businesses, like other credit or securities businesses, give the back-office the principal function of recording, documenting, and confirming the actions of the dealers. The wide range of volume and complexity that exists among dealers and end-users has led to a range of acceptable solutions The long timescales between the trade date and the settlement date, which is a feature of some products, means that errors not detected by the confirmation process may not be discovered for some time. While it is necessary to ensure that the systems are adequate for the organization’s volume and the complexity of derivatives activities, there can be no single prescriptive solution to the management challenges that derivatives pose to the back office. This reflects the diversity in activity between different market participants.

Controls Derivative activities, by their very nature, cross many boundaries of traditional financial activity. Therefore the control function must be necessarily broad, covering all aspects of activity. The primary element of control lies in the organization itself. Allocation of responsibilities for derivatives activities, with segregation of authority where appropriate, should be reflected in job descriptions and organization charts. Authority to commit the institution to transactions is normally defined by level or position. It is the role of management to ensure that the conduct of activity is consistent with delegated authority. There is no substitute for internal controls; however, dealers and end-users should communicate information that clearly indicates which individuals within the organization have the authority to make commitments. At the same time, all participants should fully recognize that the legal doctrine

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of ‘apparent authority’ may govern the transactions to which individuals within their organization commit. Definition of authority within an organization should also address issues of suitability of use of derivatives. End-users of derivatives transactions are usually institutional borrowers and investors and as such should possess the capability to understand and quantify risks inherent in their business. Institutional investors may also be buyers of structured securities exhibiting features of derivatives. While the exposures to derivatives will normally be similar to those on institutional balance sheets, it is possible that in some cases the complexity of such derivatives used might exceed the ability of an entity to understand fully the associated risks. The recommendations provide guidelines for management practice and give any firm considering the appropriate use of derivatives a useful framework for assessing suitability and developing policy consistent with its over-all risk management and capital policies. Organizational controls can then be established to ensure activities are consistent with a firm’s needs and objectives.

Audit The G30 pointed out that internal audit plays an important role in the procedures and control framework by providing an independent, internal assessment of the effectiveness of this framework. The principal challenge for management is to ensure that internal audit staff has sufficient expertise to carry out work in both the front and back office. Able individuals with the appropriate financial and systems skills are required to carry out the specialist aspects of the work. Considerable investment in training is needed to ensure that staff understand the nature and characteristics of the instruments being transacted and the models that are used to price them. Although not part of the formal control framework of the organization, external auditors and regulatory examiners provide a check on procedures and controls. They also face the challenge of developing and maintaining the appropriate degree of expertise in this area.

Appendix 2: Model risk Model risk relates to the risks involved in the erroneous use of models to value and hedge securities and is typically defined as a component of operational risk. It may seem to be insignificant for simple instruments (such as stocks and straight bonds) but can become a major operational risk for institutions that trade sophisticated OTC derivative products and execute complex arbitrage strategies. The market price is (on average) the best indicator of an asset’s value in liquid (and more or less efficient) securities markets. However, in the absence of such a price discovery mechanism, theoretical valuation models are required to ‘mark-to-model’ the position. In these circumstances the trader and the risk manager are like the pilot and co-pilot of a plane which flies under Instrument Flight Rules (IFR), relying only on sophisticated instruments to land the aircraft. An error in the electronics on board can be fatal to the plane.

Pace of model development The pace of model development over the past several years has accelerated to support the rapid growth of financial innovations such as caps, floors, swaptions, spread

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options and other exotic derivatives. These innovations were made possible by developments in financial theory that allow one to efficiently capture the many facets of financial risk. At the same time these models could never have been implemented on the trading floor had the growth in computing power not accelerated so dramatically. In March 1995, Alan Greenspan commented, ‘The technology that is available has increased substantially the potential for creating losses’. Financial innovations, model development and computing power are engaged in a sort of leapfrog, whereby financial innovations call for more model development, which in turn requires more computing power, which in turn results in more complex models. The more sophisticated the instrument, the larger the profit margin – and the greater the incentive to innovate. If the risk management function does not have the authority to approve (vet) new models, then this dynamic process can create significant operational risk. Models need to be used with caution. In many instances, too great a faith in models has led institutions to make unwitting bets on the key model parameters – such as volatilities or correlations – which are difficult to predict and often prove unstable over time. The difficulty of controlling model risk is further aggravated by errors in implementing the theoretical models, and by inexplicable differences between market prices and theoretical values. For example, we still have no satisfactory explanation as to why investors in convertible bonds do not exercise their conversion option in a way that is consistent with the predictions of models.

Different types of model risk Model risk, as illustrated in Figure 12A.1, has a number of sources: Ω Ω Ω Ω

The data input can be wrong One may wrongly estimate a key parameter of the model The model may be flawed or incorrect Models may give rise to significant hedging risk.

Figure 12A.1 Various levels of model risks.

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In fact, when most people talk about model risk they are referring to the risk of flawed or incorrect models. Modern traders often rely heavily on the use of mathematical models that involve complex equations and advanced mathematics. Flaws may be caused by mistakes in the setting of equations, or wrong assumptions may have been made about the underlying asset price process. For example, a model may be based on a flat and fixed term structure, while the actual term structure of interest rates is steep and unstable.

Appendix 3: Types of operational risk losses Operational risk is multifaceted. The type of loss can take many different forms such as damage to physical assets, unauthorized activity, unexpected taxation, etc. These various operational risk types need to be tightly defined. For example, an illustrative table of definitions such as illustrated in Table 12A.1 should be developed. This list is not meant to be exhaustive. It is critical that operational risk management groups are clear when they communicate with line management (in one direction) and senior managers (in the other).

Appendix 4: Operational risk assessment The process of operational risk assessment needs to include a review of the likelihood (or frequency) of a particular operational risk occurring as well as the magnitude (or severity) of the effect that the operational risk will have on the business. The assessment should include the options available to manage and take appropriate action to reduce operational risk. One should regularly publish graphs as shown in Figure 12A.2 displaying the relationship between the potential severity and frequency for each operational risk.

Appendix 5: Training and risk education One major source of operational risk is people – the human factor. Undoubtedly, operational risk due to people can be mitigated through better educated and trained staff. First-class risk education is a key component of any optimal firm-wide risk management program. Staff should be aware of why they may have to change the way they do things. Staff are more comfortable if they know new risk procedures exist for a good business reason. Staff need to clearly understand more than basic limit monitoring techniques (i.e. the lowest level of knowledge illustrated in Figure 12A.3). Managers need to be provided with the necessary training to understand the mathematics behind risk analysis. Business units, infrastructure units, corporate governance units and internal audit should also be educated on how risk can be used as the basis for allocating economic capital. Staff should also learn how to utilize measures of risk as a basis for pricing transactions. Finally, as illustrated in the upper-right corner of the figure, one should educate business managers and risk managers on how to utilize the risk measurement tools to enhance their portfolio management skills.

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The Professional’s Handbook of Financial Risk Management Table 12A.1 Illustrative deﬁnitions of operational risk loss

Nature of loss

Deﬁnition

Asset loss or damage

Ω Risk of either an uninsured or irrecoverable loss or damage to bank assets caused by ﬁre, ﬂooding, power supply, weather, natural disaster, physical accident, etc. Ω Risk of having to use bank assets to compensate clients for uninsured or irrecoverable loss or damage to client assets under bank custody Note: Excludes loss or damage due to either theft, fraud or malicious damage (see below for separate category) Ω Risk of projects or other initiatives costing more than budgeted Ω Risk of operational failure (failure of people, process, technology, external dependencies) resulting in credit losses Ω This is an internal failure unrelated to the creditworthiness of the borrower or guarantor e.g. inexperienced credit adjudicator assigns higher than appropriate risk rating – loan priced incorrectly and not monitored as it should be for the risk that it is, with greater risk of credit loss than should have been Ω Risk of losing current customers and being unable to attract new customers, with a consequent loss of revenue. This deﬁnition would include reputation risk as it applies to clients Ω Risk of having to make payments to settle disputes either through lawsuit or negotiated settlement (includes disputes with clients, employees, suppliers, competitors, etc.) Ω Risk of operational failure (failure of people, process, technology, external dependencies) resulting in market losses Ω This is an internal failure unrelated to market movements – e.g. incomplete or inaccurate data used in calculating VaR – true exposures not known and decisions made based on inaccurate VaR, with greater risk of market loss than should have been Ω Models used for risk measurement and valuation are wrong Ω Risk of inaccurate reporting of positions and results. If the true numbers were understood, then action could have been taken to stem losses or otherwise improve results. Risk of potential losses where model not programmed correctly or inappropriate or incorrect inputs to the model, or inappropriate use of model results, etc. Ω Risk of regulatory ﬁnes, penalties, client restitution payments or other ﬁnancial cost to be paid Ω Risk of regulatory sanctions (such as restricting or removal of one’s license, increased capital requirements, etc.) resulting in reduced ability to generate revenue or achieve targeted proﬁtability Ω Risk of incurring greater tax liabilities than anticipated Ω Risk of uninsured and irrecoverable loss of bank assets due to either theft, fraud or malicious damage. The loss may be caused by either internal or external persons. Ω Risk of having to use bank assets to compensate clients for either uninsured or irrecoverable loss of their assets under bank custody due to either theft, fraud or malicious damage Note: Excludes rogue trading (see below for separate category) Ω Risk of loss to the bank where unable to process transactions. This includes cost of correcting the problem which prevented transactions from being processed Ω Risk of a cost to the bank to correct errors made in processing transactions, or in failing to complete a transaction. This includes cost of making client whole for transactions which were processed incorrectly (e.g. restitution payments) Ω Risk of a loss of or Increased expenses as a result of unauthorized activity. For example, this includes the risk of trading loss caused by unauthorized or rogue trading activities

Cost management Credit losses due to operational failures

Customer satisfaction Disputes

Market losses due to operational failures

Model risk (see Appendix 2)

Regulatory/ compliance

Taxation Theft/fraud/ malicious damage

Transaction processing, errors and omissions

Unauthorized activity (e.g. rogue trading)

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Figure 12A.2 Severity versus frequency. A1–A10 are symbolic of 10 key risks.

Figure 12A.3 Increased operational risk knowledge required.

Appendix 6: Taxonomy for risk Arthur Anderson has developed a useful taxonomy for risk. Anderson divides risk into ‘environmental risk’, ‘process risk’, and ‘information for decision making risk’ (Figure 12A.4). These three broad categories of risk are further divided. For example, process risk is divided into operations risk, empowerment risk, information processing/technology risk, integrity risk and financial risk. Each of these risks are further subdivided. For example, financial risk is further subdivided into price, liquidity and credit risk.

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ENVIRONMENTAL RISK Competitor Catastrophic Loss

Sensitivity Sovereign /Political

Shareholder Relations

Legal

Regulatory

Capital Availability Financial Markets

Industry

PROCESS RISK EMPOWERMENT RISK

OPERATIONS RISK Customer Satisfaction Human Resources Product Development Efficiency Capacity Performance Gap Cycle Time Sourcing Obsolescence/ Shrinkage Compliance Business Interruption Product/Service Failure Environmental Health and Safety Trademark/ Brand Name Erosion

Leadership Authority/Limit Outsourcing Performance Incentives Change Readiness Communications

FINANCIAL RISK

Price

Interest Rate Currency Equity Commodity Financial Instrument

INFORMATION PROCESSING/ TECHNOLOGY RISK Relevance Integrity Access Availability Infrastructure INTEGRITY RISK

Liquidity

Credit

Cash Flow Opportunity Cost Concentration Default Concentration Settlement Collateral

Management Fraud Employee Fraud Illegal Acts Unauthorized Use Reputation

INFORMATION FOR DECISION MAKING RISK OPERATIONAL

FINANCIAL

STRATEGIC

Pricing Contract Commitment Performance Measurement Alignment Regulatory Reporting

Budget and Planning Accounting Information Financial Reporting Evaluation Taxation Pension Fund Investment Evaluation Regulatory Reporting

Environmental Scan Business Portfolio Valuation Performance Measurement Organization Structure Resource Allocation Planning Life Cycle

Figure 12A.4 A taxonomy for cataloguing risk. (Source: Arthur Anderson)

Appendix 7: Identifying and quantifying operational risk Consulting firms are providing value added operational risk services. For example, Net Risk has developed a tool which allows the user to identify operational risk causes and quantify them (Figure 12A.5). For example, the RiskOps product offered by Net Risk enables the user to utilize a ‘cause hierarchy’ to arrive at a pie chart of

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loss amount by cause as well as a frequency histogram of loss amounts. The RiskOps product also provides a description of specific losses. For example, as shown on the bottom of Figure 12A.5, RiskOps indicates that Prudential settled a class action suit for $2 million arising from improper sales techniques. Further, as shown in the middle of Figure 12A.6, one can see under the ‘personnel cause’ screen that Prudential had six different operational risk incidents ranging from firing an employee who reported improper sales practices to failure to supervise the operations of its retail CMO trading desk.

Figure 12A.5 RiskOpsTM identiﬁes operational risk causes and impacts and quantiﬁes them.

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Figure 12A.6.

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Operational risk DUNCAN WILSON

Introduction The purpose of this chapter is initially to identify and explain the reasons why banks are focusing on operational risk management in relation to the following key issues: Ω Ω Ω Ω Ω Ω Ω

Why invest in operational risk management? Defining operational risk Measuring operational risk Technology risk What is best practice? Regulatory guidance Operational risk systems

The main objective of the chapter is to highlight the pros and cons of some of the alternative approaches taken by financial institutions to address the issues and to recommend the most practical route to take in addressing them.

Why invest in operational risk management? This section will explain the reason why operational risk has become such an important issue. Over the past five years there have been a series of financial losses in financial institutions which have caused them to rethink their approach to the management of operational risk. It has been argued that mainstream methods such as control self-assessment and internal audit have failed to provide management with the tools necessary to manage operational risk. It is useful to note The Economist’s comments in their 17 October 1998 issue on Long Term Capital Management which caused some banks to provide for over US$1billion each due to credit losses: The fund, it now appears, did not borrow more than a typical investment bank. Nor was it especially risky. What went wrong was the firm’s risk-management model – which is similar to those used by the best and brightest bank.

The Economist states further that ‘Regulators have criticized LTCM and banks for not stress-testing risk models against extreme market movements’. This confusing mixture of market risks, credit risks and liquidity risks is not assisted by many banks insistence to ‘silo’ the management of these three risks into different departments (market risk management, credit risk management and treasury). This silo mentality results in many banks arguing about who is to blame

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about the credit losses suffered because of their exposures to LTCM. Within LTCM itself the main risk appears to be operational according to The Economist: lack of stress-testing the risk models. This lack of a key control is exactly the issue being addressed by regulators in their thinking about operational risks. Although much of the recent focus has been on improving internal controls this is still associated with internal audit and control self-assessment. Perhaps internal controls are the best place to start in managing operational risk because of this emphasis. The Basel Committee in January 1998 published Internal Controls and this has been welcomed by most of the banking industry, as its objective was to try to provide a regulatory framework for regulators of banks. Many regulators are now reviewing and updating their own supervisory approaches to operational risk. Some banking associations such as the British Bankers Association have conducted surveys to assess what the industry consider to be sound practice. The benefits (and therefore the goals) of investing in an improved operational risk framework are: Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω

Avoidance of large unexpected losses Avoidance of a large number of small losses Improved operational efficiency Improved return on capital Reduced earnings volatility Better capital allocation Improved customer satisfaction Improved awareness of operational risk within management Better management of the knowledge and intellectual capital within the firm Assurance to senior management and the shareholders that risks are properly being addressed.

Avoidance of unexpected loss is one of the most common justifications of investing in operational risk management. Such losses are the high-impact low-frequency losses like those caused by rogue traders. In order to bring attention to senior management and better manage a firm’s exposure to such losses it is now becoming best practice to quantify the potential for such events. The difficulty and one of the greatest challenges for firms is to assess the magnitude and likelihood of a wide variety of such events. This has led some banks to investigate the more quantitative aspects of operational risk management. This will be addressed below. The regulators in different regions of the world have also started to scrutinize the approach of banks to operational risk management. The papers on Operational Risk and Internal Control from the Basel Committee on Banking Supervision are instructive and this new focus on operational risk implies that regulatory guidance or rules will have to be complied within the next year or two. This interest by the regulators and in the industry as a whole has caused many banks to worry about their current approach to operational risk. One of the first problems that banks are unfortunately encountering is in relation to the definition of this risk.

Deﬁning operational risk The problem of defining operational risk is perplexing financial institutions. Many banks have adopted the approach of listing categories of risk, analyzing what they

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are and deciding whether they should be reporting and controlling them as a separate risk ‘silo’ within their risk management framework as many of them have done for market and credit risk. It is also important to note that operational risk is not confined to financial institutions and useful examples of approaches to defining and measuring operational risk can be gained from the nuclear, oil, gas, construction and other industries. Not surprisingly, operational risk is already being managed locally within each business area with the support of functions such as legal, compliance and internal audit. It is at the group level where the confusion is taking place on defining operational risk. Therefore a good place to start is to internally survey ‘local’ practice within each business unit. Such surveys will invariably result in a risk subcategorisation of operational risk as follows: Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω

Control risk Process risk Reputational risk Human resources risk Legal risk Takeover risk Marketing risk Systems outages Aging technology Tax changes Regulatory changes Business capacity Legal risk Project risk Security Supplier management

The above may be described in the following ways:

Control risk This is the risk that an unexpected loss occurs due to both the lack of an appropriate control or the effectiveness of an appropriate control and may be split into two main categories: Ω Inherent risk is the risk of a particular business activity, irrespective of related internal controls. Complex business areas only understood by a few key people contain higher inherent risk such as exotic derivatives trading. Ω Control risk is the risk that a financial loss or misstatement would not be prevented or detected and corrected on a timely basis by the internal control framework. Inherent risk and control risk are mixed together in many banks but it useful to make the distinction because it enables an operational risk manager to assess the degree of investment required in the internal control systems. This assessment of the relative operational risk of two different business may result in one being more inherently risky than the other and may require a higher level of internal control.

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Optimal control means that unexpected losses can happen but their frequency and severity are significantly reduced.

Process risk This is the risk that the business process is inefficient and causes unexpected losses. Process risk is closely related to internal control as internal control itself, according to COSO, should be seen as a process. It is differentiated from internal control clearly when a process is seen as a continuing activity such as risk management but the internal controls within the risk management process are depicted as ‘control points’.

Reputational risk This is the risk of an unexpected loss in share price or revenue due to the impact upon the reputation of the firm. Such a loss in reputation could occur due to mis-selling of derivatives for example. A good ‘control’ for or mitigating action for reputational risk is strong ethical values and integrity of the firm’s employees and a good public relations machine when things do go wrong.

Human resources risk Human resources risk is not just the activities of the human resources department although they do contribute to controlling the risk. However, there are particular conditions which reside within the control of the business areas themselves which the operational risk manager should be aware or when making an assessment. For example, the firm’s performance may hinge on a small number of key teams or people or the age of the teams may be skewed to young or old without an appropriate mix of skills and experience to satisfy the business objectives which have been set. Given the rogue trader problems which some banks have suffered it is also important that the operational risk manager checks that the human resources department has sufficient controls in relation to personnel security. Key items the manager should assess on personnel security are as follows: Hiring procedures: Ω references and work credentials Ω existing/ongoing security training and awareness program Ω job descriptions defining security roles and responsibilities Termination procedures: Ω the extent of the termination debriefing: reaffirm non-competition and nondisclosure agreements, Ω ensure revocation of physical access: cards, keys, system access authority, IDs, timescales Human Resources can help mitigate these risks by setting corporate standards and establishing an infrastructure such as ‘knowledge management’ databases and appropriate training and career progression. However, higher than average staff turnover or the ratio of temporary contractors to permanent staff is one indication that things are not working. Another indication of human resources risk is evidence of clashing management styles or poor morale.

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Legal risk Legal risk can be split into four areas: Ω The risk of suffering legal claims due to product liability or employee actions Ω The risk that a legal opinion on a matter of law turns out to be incorrect in a court of law. This latter risk is applicable to netting or new products such as credit derivatives where the enforceability of the agreements may not be proven in particular countries Ω Where the legal agreement covering the transaction is so complicated that the cash flows cannot be incorporated into the accounting or settlement systems of the company Ω Ability to enforce the decision in one jurisdiction in a different jurisdiction.

Takeover risk Takeover risk is highly strategic but can be controllable by making it uneconomic for a predator to take over the firm. This could be done by attaching ‘golden parachutes’ to the directors’ contracts which push up the price of the firm.

Marketing risk Marketing risk can occur in the following circumstances: Ω The benefits claimed about a products are misrepresented in the marketing material or Ω The product fails due to the wrong marketing strategy. Marketing risk is therefore at the heart of business strategy as are many of the risk subcategories.

Technology risk Systems risk in the wide definition will include all systems risks including external pressure such as the risk of not keeping up with the progress of changing technology when a company insists on developing risk management applications in-house. Technology risk is at the heart of a business such as investment banking.

Tax changes If tax changes occur, particularly retrospectively, they may make a business immediately unprofitable. A good example of this are changes in the deductibility of expenses such as depreciation of fixed assets. Normally the business should address the possibility of tax changes by making the customer pay. However, it normally comes down to a business decision of whether the firm or the customer takes the risk.

Regulatory changes Changes in regulations need to be monitored closely by firms. The effect on a business can be extremely important and the risk of volatility of returns high. A good example of this is the imminent changes in risk weighted asset percentages to be implemented.

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Business capacity If the processes, people and IT infrastructure cannot support a growing business the risks of major systems failure is high.

Project risk Project failure is one of the biggest causes for concern in most firms, particularly with the impact of some of the current project (year 2000 testing on the business).

Security The firms assets need to be secure from both internal and external theft. Such assets include not just the firm’s money or other securities/loans but also customer assets and the firm’s intellectual property.

Supplier management risk If your business is exposed to the performance of third parties you are exposed to this risk.

Natural catastrophe risk Natural catastrophes are one of the main causes of financial loss. The operational risk manager should asses whether the building is likely to be affected by: major landslide/mudslide, snow storm/blizzard, subsidence faulting, thunder/electrical storm, seasonal/local/tidal flooding, volcano, geomorphic erosion (landslip), or be located in an earthquake zone. Past history is normally used to assess such risks.

Man-made catastrophe risks There may also be man-made catastrophes such as those caused by activities inherently risky located nearby such as a prison, airport, transportation route (rail, road), chemical works, landfill site, nuclear plant, military base, defence plant, foreign embassy, petrol station, terrorist target, tube/rail station, exclusion zone. There may be other factors which need to be taken into account based on historical experience such as whether the area is likely to be affected by: epidemic, radioactive/ toxic contamination, gas, bomb threat, arson, act of war, political/union/religious/ activism, a high incidence of criminal activity. The questions above are similar to those that would be asked by any insurer of the buildings against the events described.

Other approaches Dr Jack King of Algorithmics has developed a general operational risk approach and proposes the following definition and criteria for operational risk: ‘Operational risk is the uncertainty of loss due to the failure in processing of the firms goods and services.’ For a full discussion of the rationale and consequences of adopting this definition, see his article in the January 1999 edition of the Algorithmics Research Quarterly. Peter Slater, Head of Operations Risk of Warburg Dillon Read, recently spoke at

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the IBC conference on Operational risk in London (December 1998). At that conference Peter explained that his bank split risks into the following categories: Ω Ω Ω Ω Ω Ω Ω Ω Ω

Credit and Settlement Market Operations Funding Legal IT Tax Physical and crime Compliance

He defined operations risk narrowly to be the risk of a unexpected losses due to deficiencies in internal controls or information systems caused by human error, s

The Professional’s Handbook of Financial Risk Management Edited by Marc Lore and Lev Borodovsky Endorsed by the Global Association of Risk Professionals

OXFORD AUCKLAND BOSTON JOHANNESBURG MELBOURNE NEW DELHI

Butterworth-Heinemann Linacre House, Jordan Hill, Oxford OX2 8DP 225 Wildwood Avenue, Woburn, MA 01801–2041 A division of Reed Educational and Professional Publishing Ltd A member of the Reed Elsevier plc group First published 2000 © Reed Educational and Professional Publishing Ltd 2000 All rights reserved. No part of this publication may be reproduced in any material form (including photocopying or storing in any medium by electronic means and whether or not transiently or incidentally to some other use of this publication) without the written permission of the copyright holder except in accordance with the provisions of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London, England W1P 9HE. Applications for the copyright holder’s written permission to reproduce any part of this publication should be addressed to the publishers

British Library Cataloguing in Publication Data The professional’s handbook of financial risk management 1 Risk management 2 Investments – Management I Lore, Marc II Borodovsky, Lev 332.6 Library of Congress Cataloguing in Publication Data The professional’s handbook of financial risk management/edited by Marc Lore and Lev Borodovsky. p.cm. Includes bibliographical references and index. ISBN 0 7506 4111 8 1 Risk management 2 Finance I Lore, Marc II Borodovsky, Lev. HD61 1.P76 [email protected]—dc21 99–088517 ISBN 0 7506 4111 8

Typeset by AccComputing, Castle Cary, Somerset Printed and bound in Great Britain

Contents FOREWORD PREFACE ABOUT GARP LIST OF CONTRIBUTORS ACKNOWLEDGEMENTS INTRODUCTION

xi xiii xiv xv xviii xix

PART 1 FOUNDATION OF RISK MANAGEMENT 1. DERIVATIVES BASICS Allan M. Malz Introduction Behavior of asset prices Forwards, futures and swaps Forward interest rates and swaps Option basics Option markets Option valuation Option risk management The volatility smile Over-the-counter option market conventions

3 3 4 7 14 16 21 24 29 34 37

2. MEASURING VOLATILITY Kostas Giannopoulos Introduction Overview of historical volatility methods Assumptions Conditional volatility models ARCH models: a review Using GARCH to measure correlation Asymmetric ARCH models Identification and diagnostic tests for ARCH An application of ARCH models to risk management Conclusions

42 42 42 43 45 46 50 52 53 55 67

3. THE YIELD CURVE P. K. Satish Introduction Bootstrapping swap curve Government Bond Curve Model review Summary

75 75 77 100 106 108

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4. CHOOSING APPROPRIATE VaR MODEL PARAMETERS AND RISK MEASUREMENT METHODS Ian Hawkins Choosing appropriate VaR model parameters Applicability of VaR Uses of VaR Risk measurement methods Sources of market risk Portfolio response to market changes Market parameter estimation Choice of distribution Volatility and correlation estimation Beta estimation Yield curve estimation Risk-aggregation methods Covariance approach Historical simulation VaR Monte Carlo simulation VaR Current practice Specific risk Concentration risk Conclusion

111 112 114 115 115 117 124 128 128 130 133 134 134 138 144 145 146 147 148 148

PART 2 MARKET RISK, CREDIT RISK AND OPERATIONAL RISK 5. YIELD CURVE RISK FACTORS: DOMESTIC AND GLOBAL CONTEXTS Wesley Phoa Introduction: handling multiple risk factors Principal component analysis International bonds Practical implications

155 155 158 168 174

6. IMPLEMENTATION OF A VALUE-AT-RISK SYSTEM Alvin Kuruc Introduction Overview of VaR methodologies Variance/covariance methodology for VaR Asset-flow mapping Mapping derivatives Gathering portfolio information from source systems Translation tables Design strategy summary Covariance data Heterogeneous unwinding periods and liquidity risk Change of base currency Information access Portfolio selection and reporting

185 185 185 187 191 194 196 199 200 200 201 201 202 203

Contents

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7. ADDITIONAL RISKS IN FIXED-INCOME MARKETS Teri L. Geske Introduction Spread duration Prepayment uncertainty Summary

215 215 216 223 231

8. STRESS TESTING Philip Best Does VaR measure risk? Extreme value theory – an introduction Scenario analysis Stressing VaR – covariance and Monte Carlo simulation methods The problem with scenario analysis Systematic testing Credit risk stress testing Determining risk appetite and stress test limits Conclusion

233 233 237 239 242 244 244 247 251 254

9. BACKTESTING Mark Deans Introduction Comparing risk measurements and P&L Profit and loss calculation for backtesting Regulatory requirements Benefits of backtesting beyond regulatory compliance Systems requirements Review of backtesting results in annual reports Conclusion

261 261 263 265 269 271 282 285 286

10. CREDIT RISK MANAGEMENT MODELS Richard K. Skora Introduction Motivation Functionality of a good credit risk management model Review of Markowitz’s portfolio selection theory Adapting portfolio selection theory to credit risk management A framework for credit risk management models Value-at-Risk Credit risk pricing model Market risk pricing model Exposure model Risk calculation engine Capital and regulation Conclusion

290 290 290 291 293 294 295 296 299 301 301 302 302 304

11. RISK MANAGEMENT OF CREDIT DERIVATIVES Kurt S. Wilhelm Introduction Size of the credit derivatives market and impediments to growth What are credit derivatives? Risks of credit derivatives Regulatory capital issues

307 307 308 312 318 330

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The Professional’s Handbook of Financial Risk Management

A portfolio approach to credit risk management Overreliance on statistical models Future of credit risk management

333 338 339

12. OPERATIONAL RISK Michel Crouhy, Dan Galai and Bob Mark Introduction Typology of operational risks Who manages operational risk? The key to implementing bank-wide operational risk management A four-step measurement process for operational risk Capital attribution for operational risks Self-assessment versus risk management assessment Integrated operational risk Conclusions

342 342 344 346 348 351 360 363 364 365

13. OPERATIONAL RISK Duncan Wilson Introduction Why invest in operational risk management? Defining operational risk Measuring operational risk Technology risk Best practice Regulatory guidance Operational risk systems/solutions Conclusion

377 377 377 378 386 396 399 403 404 412

PART 3 ADDITIONAL RISK TYPES 14. COPING WITH MODEL RISK Franc¸ois-Serge Lhabitant Introduction Model risk: towards a definition How do we create model risk? Consequences of model risk Model risk management Conclusions

415 415 416 417 426 431 436

15. LIQUIDITY RISK Robert E. Fiedler Notation First approach Re-approaching the problem Probabilistic measurement of liquidity – Concepts Probabilistic measurement of liquidity – Methods Dynamic modeling of liquidity Liquidity portfolios Term structure of liquidity Transfer pricing of liquidity

441 441 442 449 451 455 464 468 469 471

Contents

9

16. ACCOUNTING RISK Richard Sage Definition Accounting for market-makers Accounting for end-users Conclusion

473 473 474 486 490

17. EXTERNAL REPORTING: COMPLIANCE AND DOCUMENTATION RISK Thomas Donahoe Introduction Defining compliance risk Structuring a compliance unit Creating enforceable policies Implementing compliance policies Reporting and documentation controls Summary

491 491 492 493 499 508 513 520

18. ENERGY RISK MANAGEMENT Grant Thain Introduction Background Development of alternative approaches to risk in the energy markets The energy forward curve Estimating market risk Volatility models and model risk Correlations Energy options – financial and ‘real’ options Model risk Value-at-Risk for energy Stress testing Pricing issues Credit risk – why 3000% plus volatility matters Operational risk Summary

524 524 524 525 526 536 542 543 543 545 546 547 548 548 551 555

19. IMPLEMENTATION OF PRICE TESTING Andrew Fishman Overview Objectives and defining the control framework Implementing the strategy Managing the price testing process Reporting Conclusion

557 557 559 563 573 574 578

PART 4 CAPITAL MANAGEMENT, TECHNOLOGY AND REGULATION 20. IMPLEMENTING A FIRM-WIDE RISK MANAGEMENT FRAMEWORK Shyam Venkat Introduction Understanding the risk management landscape Establishing the scope for firm-wide risk management Defining a firm-wide risk management framework Conclusion

581 581 583 585 587 612

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The Professional’s Handbook of Financial Risk Management

21. SELECTING AND IMPLEMENTING ENTERPRISE RISK MANAGEMENT TECHNOLOGIES Deborah L. Williams Introduction: enterprise risk management, a system implementation like no other The challenges The solution components Enterprise risk technology market segments Different sources for different pieces: whom to ask for what? The selection process Key issues in launching a successful implementation Conclusions

614 615 618 623 627 629 631 633

22. ESTABLISHING A CAPITAL-BASED LIMIT STRUCTURE Michael Hanrahan Introduction Purpose of limits Economic capital Types of limit Monitoring of capital-based limits Summary

635 635 635 637 644 654 655

23. A FRAMEWORK FOR ATTRIBUTING ECONOMIC CAPITAL AND ENHANCING SHAREHOLDER VALUE Michael Haubenstock and Frank Morisano Introduction Capital-at-risk or economic capital A methodology for computing economic capital Applications of an economic capital framework Applying economic capital methodologies to improve shareholder value Conclusion

614

657 657 658 659 675 680 687

24. INTERNATIONAL REGULATORY REQUIREMENTS FOR RISK MANAGEMENT (1988–1998) Mattia L. Rattaggi Introduction Quantitative capital adequacy rules for banks Risk management organization of financial intermediaries and disclosure recommendations Cross-border and conglomerates supervision Conclusion

716 723 726

25. RISK TRANSPARENCY Alan Laubsch Introduction Risk reporting External risk disclosures

740 740 740 764

INDEX

690 690 691

777

Foreword The role and importance of the risk management process (and by definition the professional risk manager) has evolved dramatically over the past several years. Until recently risk management was actually either only risk reporting or primarily a reactive function. The limited risk management tasks and technology support that did exist were usually assigned to ex-traders or product controllers with little or no support from the rest of business. The term professional risk manager was virtually unheard of in all but the largest and most sophisticated organizations. Only after a series of well-publicised losses and the accompanying failure of the firms involved did the need for sophisticated, proactive and comprehensive financial risk management processes become widely accepted. The new world of the professional risk manager is one that begins in the boardroom rather than the back office. The risk management process and professionals are now recognized as not only protecting the organization against unexpected losses, but also fundamental to the efficient allocation of capital to optimize the returns on risk. The professional risk manager, when properly supported and utilized, truly provides added value to the organization. A number of risk books were published in the latter half of the 1990s. They addressed the history of risk, how it evolved, the psychological factors that caused individuals to be good or bad risk takers and a myriad of other topics. Unfortunately, few books were written on the proper management of the growing population and complexity of risks confronting institutions. Marc Lore, Lev Borodovsky and their colleagues in the Global Association of Risk Professionals recognized this void and this book is their first attempt to fill in some of the blank spaces. KPMG is pleased to the be the primary sponsor of GARP’s The Professional’s Handbook of Financial Risk Management. We believe that this volume offers the reader practical, real world insights into leading edge practices for the management of financial risk regardless of the size and sophistication of their own organization. For those contemplating a career in risk management, the authors of this text are practising financial risk managers who provide knowledgeable insights concerning their rapidly maturing profession. No one volume can ever hope to be the ultimate last word on a topic that is evolving as rapidly as the field of financial risk management. However, we expect that this collection of articles, written by leading industry professionals who understand the risk management process, will become the industry standard reference text. We hope that after reviewing their work you will agree. Martin E. Titus, Jr Chairman, KPMG GlobeRisk

Preface Risk management encompasses a broad array of concepts and techniques, some of which may be quantified, while others must be treated in a more subjective manner. The financial fiascos of recent years have made it clear that a successful risk manager must respect both the intuitive and technical aspects (the ‘art’ and the ‘science’) of the discipline. But no matter what types of methods are used, the key to risk management is delivering the risk information in a timely and succinct fashion, while ensuring that key decision makers have the time, the tools, and the incentive to act upon it. Too often the key decision makers receive information that is either too complex to understand or too large to process. In fact, Gerald Corrigan, former President of the New York Federal Reserve, described risk management as ‘getting the right information to the right people at the right time’. History has taught us time and time again that senior decision makers become so overwhelmed with VaR reports, complex models, and unnecessary formalism that they fail to account for the most fundamental of risks. An integral part of the risk manager’s job therefore is to present risk information to the decision maker in a format which not only highlights the main points but also directs the decision maker to the most appropriate course of action. A number of financial debacles in 1998, such as LTCM, are quite representative of this problem. Risk managers must work proactively to discover new ways of looking at risk and embrace a ‘common sense’ approach to delivering this information. As a profession, risk management needs to evolve beyond its traditional role of calculating and assessing risk to actually making effective use of the results. This entails the risk manager examining and presenting results from the perspective of the decision maker, bearing in mind the knowledge base of the decision maker. It will be essential over the next few years for the risk manager’s focus to shift from calculation to presentation and delivery. However, presenting the right information to the right people is not enough. The information must also be timely. The deadliest type of risk is that which we don’t recognize in time. Correlations that appear stable break down, and a VaR model that explains earnings volatility for years can suddenly go awry. It is an overwhelming and counterproductive task for risk managers to attempt to foresee all the potential risks that an organization will be exposed to before they arise. The key is to be able to separate those risks that may hurt an institution from those that may destroy it, and deliver that information before it is too late. In summary, in order for risk management to truly add value to an organization, the risk information must be utilized in such a way as to influence or alter the business decision-making process. This can only be accomplished if the appropriate information is presented in a concise and well-defined manner to the key decision makers of the firm on a timely basis. Editors: Marc Lore and Lev Borodovsky Co-ordinating Editor: Nawal K. Roy Assistant Editors: Lakshman Chandra and Michael Hanrahan

About GARP The Global Association of Risk Professionals (GARP) is a not-for-profit, independent organization of over 10 000 financial risk management practitioners and researchers from over 90 countries. GARP was founded by Marc Lore and Lev Borodovsky in 1996. They felt that the financial risk management profession should extend beyond the risk control departments of financial institutions. GARP is now a diverse international association of professionals from a variety of backgrounds and organizations who share a common interest in the field. GARP’s mission is to serve its members by facilitating the exchange of information, developing educational programs, and promoting standards in the area of financial risk management. GARP members discuss risk management techniques and standards, critique current practices and regulation, and help bring forth potential risks in the financial markets to the attention of other members and the public. GARP seeks to provide open forums for discussion and access to information such as events, publications, consulting and software services, jobs, Internet sites, etc. To join GARP visit the web site at www.garp.com

Contributors EDITORS Ω Marc Lore Executive Vice President and Head of Firm-Wide Risk Management and Control, Sanwa Bank International, City Place House, PO Box 245, 55 Basinghall St, London EC2V 5DJ, UK Ω Lev Borodovsky Director, Risk Measurement and Management Dept, Credit Suisse First Boston, 11 Madison Avenue, New York, NY 10010-3629, USA Co-ordinating Editor Ω Nawal K. Roy Associate Vice President, Credit Suisse First Boston, 11 Madison Avenue, New York, NY10010-3629, USA Assistant Editors Ω Lakshman Chandra Business Manager, Risk Management Group, Sanwa Bank International, City Place House, PO Box 245, 55 Basinghall St, London EC2V 5DJ, UK Ω Michael Hanrahan Assistant Vice President, Head of Risk Policy, Sanwa Bank International, City Place House, PO Box 245, 55 Basinghall St, London EC2V 5DJ, UK

CONTRIBUTORS Ω Philip Best Risk specialist, The Capital Markets Company, Clements House, 14–18 Gresham St, London, EC2V 7JE, UK Ω Michel Crouhy Senior Vice President, Market Risk Management, Canadian Imperial Bank of Commerce, 161 Bay Street, Toronto, Ontario M5J 2S8, Canada Ω Mark Deans Head of Risk Management and Regulation, Sanwa Bank International, 55 Basinghall Street, London, EC2V 5DJ, UK Ω Thomas Donahoe Director, MetLife, 334 Madison Avenue, Area 2, Convent Station, NJ 07961, USA Ω Robert E. Fiedler Head of Treasury and Liquidity Risk, Methodology and Policy Group Market Risk Management, Deutsche Bank AG, D-60262 Frankfurt, Germany Ω Andrew Fishman Principal Consultant, The Capital Markets Company, Clements House, 14–18 Gresham St, London, EC2V 7JE, UK

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The Professional’s Handbook of Financial Risk Management

Ω Dan Galai Abe Gray Professor, Finance and Administration, Hebrew University, School of Business Administration in Jerusalem, Israel Ω Teri L. Geske Senior Vice President, Product Development, Capital Management Sciences, 11766 Wilshire Blvd, Suite 300, Los Angeles, CA 90025, USA Ω Kostas Giannopoulos Senior Lecturer in Finance, Westminster Business School, University of Westminster, 309 Regent St, London W1R 8AL, UK Ω Michael Haubenstock PricewaterhouseCoopers LLP, 1177 Avenue of the Americas, New York, NY 10036, USA Ω Ian Hawkins Assistant Director, Global Derivatives and Fixed Income, Westdeutsche Landesbank Girozentrale, 1211 Avenue of the Americas, New York, NY 10036, USA Ω Alvin Kuruc Senior Vice President, Infinity, a SunGard Company, 640 Clyde Court, Mountain View, CA 04043, USA Ω Alan Laubsch Partner, RiskMetrics Group, 44 Wall Street, New York, NY 10005, USA Ω Franc¸ois-Serge Lhabitant Director, UBS Ag, Aeschenplatz 6, 4002 Basel, Switzerland, and Assistant Professor of Finance, Thunderbird, the American Graduate School of International Management, Glendale, USA Ω Allan M. Malz Partner, RiskMetrics Group, 44 Wall St, NY 10005, USA Ω Bob Mark Executive Vice President, Canadian Imperial Bank of Commerce, 161 Bay Street, Toronto, Ontario, M5J 2S8, Canada Ω Frank Morisano Director, PricewaterhouseCoopers LLP, 1177 Avenue of the Americas, New York, NY 10036, USA Ω Wesley Phoa Associate, Quantitative Research, Capital Strategy Research, 11100 Santa Monica Boulevard, Los Angeles, CA 90025, USA Ω Mattia L. Rattaggi Corporate Risk Control, UBS, AG, Pelikanstrasse 6, PO Box 8090, Zurich, Switzerland Ω Richard Sage, FRM Director, Enron Europe, Flat 1, 25 Bedford Street, London WC2E 9EQ Ω P. K. Satish, CFA Managing Director, Head of Financial Engineering and Research, Askari Risk Management Solutions, State St Bank & Trust Company, 100 Avenue of the Americas, 5th Floor, New York, NY 10013, USA Ω Richard K. Skora President, Skora & Company Inc., 26 Broadway, Suite 400, New York, NY 10004, USA Ω Grant Thain Senior Vice President, Risk Management, Citizens Power LLC, 160 Federal Street, Boston, MA 02110, USA

Contributors

17

Ω Shyam Venkat Partner, PricewaterhouseCoopers LLP, 1177 Avenue of the Americas, New York, NY 10036, USA Ω Kurt S. Wilhelm, FRM, CFA National Bank Examiner, Comptroller of the Currency, 250 E St SW, Washington, DC 20219, USA Ω Deborah L. Williams Co-founder and Research Director, Meridien Research, 2020 Commonwealth Avenue, Newton, MA 02466, USA Ω Duncan Wilson Partner, Global Risk Management Practice, Ernst & Young, Rolls House, 7 Rolls Building, Fetter Lane, London EC4A 1NH, UK

Acknowledgements We would like to thank our wives Carolyn and Lisa for all their tremendous help, support, and patience. We also wish to thank all the authors who have contributed to this book. Special thanks go to Nawal Roy for his superb effort in pulling this project together and always keeping it on track, as well as his remarkable assistance with the editing process. We wish to thank Michael Hanrahan and Lakshman Chandra for writing the Introduction and their help in editing. Finally we would like to thank all GARP’s members for their continued support. Marc Lore Lev Borodovsky

Introduction The purpose of this book is to provide risk professionals with the latest standards that represent best practice in the risk industry. The book has been created with the risk practitioner in mind. While no undertaking of this size can be devoid of theory, especially considering the ongoing changes and advances within the profession itself, the heart of this book is aimed at providing practising risk managers with usable and sensible information that will assist them in their day-to-day work. The successful growth of GARP, the Global Association of Risk Professionals, has brought together thousands of risk professionals and has enabled the sharing of ideas and knowledge throughout the risk community. The existence of this forum has also made apparent that despite the growing size and importance of risk management in the financial world, there is no book in the marketplace that covers the wide array of topics that a risk manager can encounter on a daily basis in a manner that suits the practitioner. Rather, the practitioner is besieged by books that are theoretical in nature. While such books contain valuable insights that are critical to the advancement of our profession, most risk professionals are never able to utilize and test the concepts within them. Consequently, a familiar theme has emerged at various GARP meetings and conferences that a risk handbook needs to be created to provide risk practitioners with knowledge of the practices that other risk professionals have employed at their own jobs. This is especially important considering the evolving nature of risk management that can be characterized by the continuous refinement and improvement of risk management techniques, which have been driven by the increasingly complex financial environment. One of the challenges of this book has been to design its contents so that it can cover the vast area that a risk manager encounters in his or her job and at the same time be an aid to both the experienced and the more novice risk professional. Obviously, this is no easy task. While great care has been taken to include material on as many topics that a risk manager might, and even should, encounter at his or her job, it is impossible to provide answers to every single question that one might have. This is especially difficult considering the very nature of the risk management profession, as there are very few single answers that can automatically be applied to problems with any certainty. The risk management function in an organization should be a fully integrated one. While it is independent in its authority from other areas within the bank, it is at the same time dependent on them in that it receives and synthesizes information, information which is critical to its own operations, from these other areas. Consequently, the decisions made by risk managers can impact on the entire firm. The risk manager, therefore, must tailor solutions that are appropriate considering the circumstances of the institution in which he or she is working, the impact that the solution might have on other areas of the organization, and the practical considerations associated with implementation that must be factored into any chosen decision.

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The Professional’s Handbook of Financial Risk Management

This handbook has thus been designed to delve into the various roles of the risk management function. Rather than describing every possible role in exhaustive detail, the authors have attempted to provide a story line for each of the discussed topics, including practical issues that a risk manager needs to consider when tackling the subject, possible solutions to difficulties that might be encountered, background knowledge that is essential to know, and more intricate practices and techniques that are being used. By providing these fundamentals, the novice risk professional can gain a thorough understanding of the topic in question while the more experienced professional can use some of the more advanced concepts within the book. Thus the book can be used to broaden one’s own knowledge of the risk world, both by familiarizing oneself with areas in which the risk manager lacks experience and by enhancing one’s knowledge in areas in which one already has expertise. When starting this project we thought long and hard as to how we could condense the myriad ideas and topics which risk management has come to represent. We realized early on that the growth in risk management ideas and techniques over the last few years meant that we could not possibly explain them all in detail in one book. However, we have attempted to outline all the main areas of risk management to the point where a risk manager can have a clear idea of the concepts being explained. It is hoped that these will fire the thought processes to the point where a competent risk manager could tailor the ideas to arrive at an effective solution for their own particular problem. One of the obstacles we faced in bringing this book together was to decide on the level of detail for each chapter. This included the decision as to whether or not each chapter should include practical examples of the ideas being described and what it would take practically to implement the ideas. It was felt, however, that the essence of the book would be best served by not restricting the authors to a set format for their particular chapter. The range of topics is so diverse that it would not be practical in many cases to stick to a required format. It was also felt that the character of the book would benefit from a ‘free form’ style, which essentially meant giving the authors a topic and letting them loose. This also makes the book more interesting from the reader’s perspective and allowed the authors to write in a style with which they were comfortable. Therefore as well as a book that is diverse in the range of topics covered we have one with a range of perspectives towards the topics being covered. This is a facet that we think will distinguish this book from other broad-ranging risk management books. Each author has taken a different view of how his or her topic should be covered. This in turn allows us to get a feel for the many ways in which we can approach a problem in the risk management realm. Some authors have taken a high-level approach, which may befit some topics, while others have gone into the detail. In addition, there are a number of chapters that outline approaches we should take to any risk management problem. For example, Deborah Williams’ excellent chapter on enterprise risk management technologies gives us a good grounding on the approach to take in implementing a systems-based solution to a risk management problem. As well as providing practical solutions this book also covers many topics which practitioners in the financial sector would not necessarily ever encounter. We are sure that readers will find these insights into areas outside their normal everyday environments to be both interesting and informative. For any practitioners who think the subtleties of interest rate risk are complicated, we would recommend a read of Grant Thain’s chapter on energy risk management!

Introduction

21

As mentioned earlier, we could not hope to impose limits on the authors of this book while allowing them free reign to explore their particular topic. For this reason we feel that a glossary of terms for this book would not necessarily be useful. Different authors may interpret the same term in many different ways, and therefore we would ask that the reader be careful to understand the context in which a particular phrase is being used. All authors have been quite clear in defining ambiguous words or phrases, whether formally or within the body of the text, so the reader should not have too much difficulty in understanding the scope in which phrases are being used. Rather than one writer compiling the works of various authors or research papers it was felt that the best approach to producing a practical risk management guide was to let the practitioners write it themselves. Using the extensive base of contacts that was available from the GARP membership, leaders in each field were asked to produce a chapter encapsulating their knowledge and giving it a practical edge. Condensing their vast knowledge into one chapter was by no means an easy feat when we consider that all our contributors could quite easily produce a whole book on their specialist subject. Naturally The Professional’s Handbook of Financial Risk Management would not have come about without their efforts and a willingness or even eagerness to share their ideas and concepts. It should be noted that every effort was made to select people from across the risk management spectrum, from insurance and banking to the regulatory bodies and also the corporations and utility firms who are the main end-users of financial products. Each sector has its own view on risk management and this diverse outlook is well represented throughout the book. All authors are leaders in their field who between them have the experience and knowledge, both practical and theoretical, to produce the definitive risk management guide. When asking the contributors to partake in this project we were quite overwhelmed by the enthusiasm with which they took up the cause. It is essential for those of us in the risk management arena to share knowledge and disseminate what we know in order to assist each other in our one common aim of mitigating risk. This book demonstrates how the risk management profession has come of age in realizing that we have to help each other to do our jobs effectively. This is best illustrated by the manner in which all our authors contrived to ensure we understand their subject matter, thus guaranteeing that we can use their solutions to our problems. Editorial team

Part 1

Foundation of risk management

1

Derivatives basics ALLAN M. MALZ

Introduction Derivative assets are assets whose values are determined by the value of some other asset, called the underlying. There are two common types of derivative contracts, those patterned on forwards and on options. Derivatives based on forwards have linear payoffs, meaning their payoffs move one-for-one with changes in the underlying price. Such contracts are generally relatively easy to understand, value, and manage. Derivatives based on options have non-linear payoffs, meaning their payoffs may move proportionally more or less than the underlying price. Such contracts can be quite difficult to understand, value, and manage. The goal of this chapter is to describe the main types of derivatives currently in use, and to provide some understanding of the standard models used to value these instruments and manage their risks. There is a vast and growing variety of derivative products – among recent innovations are credit and energy-related derivatives. We will focus on the most widely used instruments and on some basic analytical concepts which we hope will improve readers’ understanding of any derivatives issues they confront. Because a model of derivative prices often starts out from a view of how the underlying asset price moves over time, the chapter begins with an introduction to the ‘standard view’ of asset price behavior, and a survey of how asset prices actually behave. An understanding of these issues will be helpful later in the chapter, when we discuss the limitations of some of the benchmark models employed in derivatives pricing. The chapter then proceeds to a description of forwards, futures and options. The following sections provide an introduction to the Black–Scholes model. Rather than focusing primarily on the theory underlying the model, we focus on the option market conventions the model has fostered, particularly the use of implied volatility as a metric for option pricing and the use of the so-called ‘greeks’ as the key concepts in option risk management. In recent years, much attention has focused on differences between the predictions of benchmark option pricing models and the actual patterns of option prices, particularly the volatility smile, and we describe these anomalies. This chapter concludes with a sections discussing certain option combinations, risk reversals and strangles, by means of which the market ‘trades’ the smile.

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The Professional’s Handbook of Financial Risk Management

Behavior of asset prices Efﬁcient markets hypothesis The efficient market approach to explaining asset prices views them as the present values of the income streams they generate. Efficient market theory implies that all available information regarding future asset prices is impounded in current asset prices. It provides a useful starting point for analyzing derivatives. One implication of market efficiency is that asset returns follow a random walk. The motion of the asset price has two parts, a drift rate, that is, a deterministic rate at which the asset price is expected to change over time, and a variance rate, that is, a random change in the asset price, also proportional to the time elapsed, and also unobservable. The variance rate has a mean of zero and a per-period variance equal to a parameter p, called the volatility. This assumption implies that the percent changes in the asset price are normally distributed with a mean equal to the drift rate and a variance equal to p 2. The random walk hypothesis is widely used in financial modeling and has several implications: Ω The percent change in the asset price over the next time interval is independent of both the percent change over the last time interval and the level of the asset price. The random walk is sometimes described as ‘memoryless’ for this reason. There is no tendency for an up move to be followed by another up move, or by a down move. That means that the asset price can only have a non-stochastic trend equal to the drift rate, and does not revert to the historical mean or other ‘correct’ level. If the assumption were true, technical analysis would be irrelevant. Ω Precisely because of this lack of memory, the asset price tends over time to wander further and further from any starting point. The proportional distance the asset price can be expected to wander randomly over a discrete time interval q is the volatility times the square root of the time interval, pYq. Ω Asset prices are continuous; they move in small steps, but do not jump. Over a given time interval, they may wander quite a distance from where they started, but they do it by moving a little each day. Ω Asset returns are normally distributed with a mean equal to the drift rate and a standard deviation equal to the volatility. The return distribution is the same each period. The Black–Scholes model assumes that volatility can be different for different asset prices, but is a constant for a particular asset. That implies that asset prices are homoskedastic, showing no tendency towards ‘volatility bunching’. A wild day in the markets is as likely to be followed by a quiet day as by another wild day. An asset price following geometric Brownian motion can be thought of as having an urge to wander away from any starting point, but not in any particular direction. The volatility parameter can be thought of as a scaling factor for that urge to wander. Figure 1.1 illustrates its properties with six possible time paths over a year of an asset price, the sterling–dollar exchange rate, with a starting value of USD 1.60, an annual volatility of 12%, and an expected rate of return of zero.

Empirical research on asset price behavior While the random walk is a perfectly serviceable first approximation to the behavior of asset prices, in reality, it is only an approximation. Even though most widely

Derivatives basics

5

ST 2.00

1.80

1.60

1.40 0

50

100

150

200

250

Τ

Figure 1.1 Random walk.

traded cash asset returns are close to normal, they display small but important ‘nonnormalities’. In particular, the frequency and direction of large moves in asset prices, which are very important in risk management, can be quite different in real-life markets than the random walk model predicts. Moreover, a few cash assets behave very differently from a random walk. The random walk hypothesis on which the Black–Scholes model is based is a good first approximation to the behavior of most asset prices most of the time. However, even nominal asset returns that are quite close to normally distributed display small but important deviations from normality. The option price patterns discussed below reveal how market participants perceive the distribution of future asset prices. Empirical studies of the stochastic properties of nominal returns focus on the behavior of realized asset prices. The two approaches largely agree. Kurtosis The kurtosis or leptokurtosis (literally, ‘fat tails’) of a distribution is a measure of the frequency of large positive or negative asset returns. Specifically, it measures the frequency of large squared deviations from the mean. The distribution of asset returns will show high kurtosis if asset returns which are far above or below the mean occur relatively often, regardless of whether they are mostly above, mostly below, or both above and below the mean return. Kurtosis is measured in comparison with the normal distribution, which has a coefficient of kurtosis of exactly 3. If the kurtosis of an asset return distribution is significantly higher than 3, it indicates that large-magnitude returns occur more frequently than in a normal distribution. In other words, a coefficient of kurtosis well over 3 is inconsistent with the assumption that returns are normal. Figure 1.2 compares a kurtotic distribution with a normal distribution with the same variance. Skewness The skewness of a distribution is a measure of the frequency with which large returns in a particular direction occur. An asset which displays large negative returns more

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The Professional’s Handbook of Financial Risk Management

kurtotic

normal

0.50

return

0.25

0

0.25

0.50

Figure 1.2 Kurtosis.

frequently than large positive returns is said to have a return distribution skewed to the left or to have a ‘fat left tail’. An asset which displays large positive returns more frequently than large negative returns is said to have a return distribution skewed to the right or to have a ‘fat right tail’. The normal distribution is symmetrical, that is, its coefficient of skewness is exactly zero. Thus a significantly positive or negative skewness coefficient is inconsistent with the assumption that returns are normal. Figure 1.3 compares a skewed, but non-kurtotic, distribution with a normal distribution with the same variance. Table 1.1 presents estimates of the kurtosis and skewness of some widely traded assets. All the assets displayed have significant positive or negative skewness, and most also have a coefficient of kurtosis significantly greater than 3.0. The exchange rates of the Mexican peso and Thai baht vis-a`-vis the dollar have the largest coefficients of kurtosis. They are examples of intermittently fixed exchange

skewed normal

0.75

0.50

0.25

return 0

0.25

Figure 1.3 Skewness.

0.50

0.75

Derivatives basics

7

rates, which are kept within very narrow fluctuation limits by the monetary authorities. Typically, fixed exchange rates are a temporary phenomenon, lasting decades in rare cases, but only a few years in most. When a fixed exchange rate can no longer be sustained, the rate is either adjusted to new fixed level (for example, the European Monetary System in the 1980s and 1990s and the Bretton Woods system until 1971) or permitted to ‘float’, that is, find a free-market price (for example, most emerging market currencies). In either case, the return pattern of the currency is one of extremely low returns during the fixed-rate period and extremely large positive or negative returns when the fixed rate is abandoned, leading to extremely high kurtosis. The return patterns of intermittently pegged exchange rates also diminishes the forecasting power of forward exchange rates for these currencies, a phenomenon known as regime-switching or the peso problem. The term ‘peso problem’ has its origin in experience with spot and forward rates on the Mexican peso in the 1970s. Observers were puzzled by the fact that forward rates for years ‘predicted’ a significant short-term depreciation of the peso vis-a`-vis the US dollar, although the peso–dollar exchange rate was fixed. One proposed solution was that the exchange rate peg was not perfectly credible, so market participants expected a switch to a new, lower value of the peso with a positive probability. In the event, the peso has in fact been periodically permitted to float, invariably depreciating sharply. Autocorrelation of returns The distribution of many asset returns is not only kurtotic and skewed. The return distribution may also change over time and successive returns may not be independent of one another. These phenomena will be reflected in the serial correlation or autocorrelation of returns. Table 1.1 displays evidence that asset returns are not typically independently and identically distributed. The rightmost column displays a statistic which measures the likelihood that there is serial correlation between returns on a given day and returns on the same asset during the prior five trading days. High values of this statistic indicate a high likelihood that returns are autocorrelated. Table 1.1 Statistical properties of selected daily asset returns Asset

Standard deviation

Skewness

Kurtosis

Autocorrelation

0.0069 0.0078 0.0132 0.0080 0.0204 0.0065 0.0138 0.0087

0.347 0.660 ñ3.015 ñ0.461 0.249 ñ0.165 0.213 ñ0.578

2.485 6.181 65.947 25.879 4.681 4.983 3.131 8.391

6.0 8.0 56.7 87.4 41.1 21.3 26.3 25.6

Dollar–Swiss franc Dollar–yen Dollar–Mexican peso Dollar–Thai baht Crude oil Gold Nikkei 225 average S&P 500 average

Forwards, futures and swaps Forwards and forward prices In a forward contract, one party agrees to deliver a specified amount of a specified commodity – the underlying asset – to the other at a specified date in the future (the

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maturity date of the contract) at a specified price (the forward price). The commodity may be a commodity in the narrow sense, e.g. gold or wheat, or a financial asset, e.g. foreign exchange or shares. The price of the underlying asset for immediate (rather than future) delivery is called the cash or spot price. The party obliged to deliver the commodity is said to have a short position and the party obliged to take delivery of the commodity and pay the forward price for it is said to have a long position. A party with no obligation offsetting the forward contract is said to have an open position. A party with an open position is sometimes called a speculator. A party with an obligation offsetting the forward contract is said to have a covered position. A party with a closed position is sometimes called a hedger. The market sets forward prices so there are no cash flows – no money changes hands – until maturity. The payoff at maturity is the difference between forward price, which is set contractually in the market at initiation, and the future cash price, which is learned at maturity. Thus the long position gets S T ñFt,T and the short gets Ft,T ñS T , where Tñt is the maturity, in years, of the forward contract (for example, Tó1/12 for a one-month forward), S T is the price of the underlying asset on the maturity date, and Ft,T is the forward price agreed at time t for delivery at time T. Figure 1.4 illustrates with a dollar forward against sterling, initiated at a forward outright rate (see below) of USD 1.60. Note that the payoff is linearly related to the terminal value S T of the underlying exchange rate, that is, it is a constant multiple, in this case unity, of S T .

payoff 0.10

0.05

forward rate

1.45

1.50

1.55

1.60

ST

0.05 0.10 Figure 1.4 Payoff on a long forward.

No-arbitrage conditions for forward prices One condition for markets to be termed efficient is the absence of arbitrage. The term ‘arbitrage’ has been used in two very different senses which it is important to distinguish: Ω To carry out arbitrage in the first sense, one would simultaneously execute a set of transactions which have zero net cash flow now, but have a non-zero probability

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of a positive payoff without risk, i.e. with a zero probability of a negative payoff in the future. Ω Arbitrage in the second sense is related to a model of how asset prices behave. To perform arbitrage in this sense, one carries out a set of transactions with a zero net cash flow now and a positive expected value at some date in the future. Derivative assets, e.g. forwards, can often be constructed from combinations of underlying assets. Such constructed assets are called synthetic assets. Covered parity or cost-of-carry relations are relations are between the prices of forward and underlying assets. These relations are enforced by arbitrage and tell us how to determine arbitrage-based forward asset prices. Throughout this discussion, we will assume that there are no transactions costs or taxes, that markets are in session around the clock, that nominal interest rates are positive, and that unlimited short sales are possible. These assumptions are fairly innocuous: in the international financial markets, transactions costs typically are quite low for most standard financial instruments, and most of the instruments discussed here are not taxed, since they are conducted in the Euromarkets or on organized exchanges. Cost-of-carry with no dividends The mechanics of covered parity are somewhat different in different markets, depending on what instruments are most actively traded. The simplest case is that of a fictitious commodity which has no convenience value, no storage and insurance cost, and pays out no interest, dividends, or other cash flows. The only cost of holding the commodity is then the opportunity cost of funding the position. Imagine creating a long forward payoff synthetically. It might be needed by a dealer hedging a short forward position: Ω Buy the commodity with borrowed funds, paying S t for one unit of the commodity borrowed at rt,T , the Tñt-year annually compounded spot interest rate at time t. Like a forward, this set of transactions has a net cash flow of zero. Ω At time T, repay the loan and sell the commodity. The net cash flow is S T ñ[1òrt,T (Tñt)]St . This strategy is called a synthetic long forward. Similarly, in a synthetic short forward, you borrow the commodity and sell it, lending the funds at rate rt,T ,: the net cash flow now is zero. At time T, buy the commodity at price S T and return it: the net cash flow is [1òrt,T (Tñt)]St ñS T . The payoff on this synthetic long or short forward must equal that of a forward contract: S T ñ[1òrt,T (Tñt)]S T óS T ñFt,T . If it were greater (smaller), one could make a riskless profit by taking a short (long) forward position and creating a synthetic long (short) forward. This implies that the forward price is equal to the future value of the current spot price, i.e. the long must commit to paying the financing cost of the position: Ft,T ó[1òrt,T (Tñt)]St . Two things are noteworthy about this cost-of-carry formula. First, the unknown future commodity price is irrelevant to the determination of the forward price and has dropped out. Second, the forward price must be higher than the spot price, since the interest rate rt,T is positive. Short positions can be readily taken in most financial asset markets. However, in some commodity markets, short positions cannot be taken and thus synthetic short

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forwards cannot be constructed in sufficient volume to eliminate arbitrage entirely. Even, in that case, arbitrage is only possible in one direction, and the no-arbitrage condition becomes an inequality: Ft,T O[1òrt,T (Tñt)]St . Cost-of-carry with a known dividend If the commodity pays dividends or a return d T (expressed as a percent per period of the commodity price, discretely compounded), which is known in advance, the analysis becomes slightly more complicated. You can think of d t,T as the dividend rate per ‘share’ of the asset: a share of IBM receives a dividend, an equity index unit receives a basket of dividends, $100 of par value of a bond receives a coupon, etc. The d T may be negative for some assets: you receive a bill for storage and insurance costs, not a dividend check, on your 100 ounces of platinum. The amount of dividends received over Tñt years in currency units is d t,T St (Tñt). The synthetic long forward position is still constructed the same way, but in this case the accrued dividend will be received at time T in addition to the commodity price. The net cash flow is S T òd t,T St (Tñt)ñ[1òrt,T (Tñt)]St . The no-arbitrage condition is now S T ñFt,T óS T ñ[1ò(rt,T ñd t,T )(Tñt)]St . The forward price will be lower, the higher the dividends paid: Ft,T ó[1ò(rt,T ñd t,T )(Tñt)St . The forward price may be greater than, less than or equal to than the spot price if there is a dividend. The long’s implied financing cost is reduced by the dividend received. Foreign exchange Forward foreign exchange is foreign currency deliverable in the future. Its price is called forward exchange rate or the forward outright rate, and the differential of the forward minus the spot exchange rate is called the swap rate (not to be confused with the rate on plain-vanilla interest rate swaps). To apply the general mechanics of a forward transaction described above to this case, let rt,T and r*t ,T represent the domestic and foreign money-market interest rates. The spot and forward outright exchange rates are S t and Ft,T , expressed in domestic currency units per foreign currency unit. To create a synthetic long forward, Ω Borrow St /(1òr*t ,T q) domestic currency units at rate rt,T and buy 1/(1òr*t ,T q) foreign currency units. Deposit the foreign currency proceeds at rate r*t ,T . There is no net cash outlay now. Ω At time T, the foreign currency deposit has grown to one foreign currency unit, and you must repay the borrowed St (1òrt,T q) 1òr*t ,T q including interest. This implies that the forward rate is Ft,T ó

1òrt,T q St 1òr*t ,T q

Here is a numerical example of this relationship. Suppose the Euro-dollar spot

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exchange rate today is USD 1.02 per Euro. Note that we are treating the US dollar as the domestic and the Euro as the foreign currency. Suppose further that the US 1-year deposit rate is 5.75% and that the 1-year Euro deposit rate is 3.0%. The 1-year forward outright rate must then be 1.0575 1.02ó1.0472 1.03 Typically, forward foreign exchange rates are quoted not as outright rates but in terms of forward points. The points are a positive or negative quantity which is added to the spot rate to arrive at the forward outright rate, usually after dividing by a standard factor such as 10 000. In our example, the 1-year points amount to 10 000 · (1.0472ñ1.02)ó272. If Euro deposit rates were above rather than below US rates, the points would be negative. Gold leasing Market participants can borrow and lend gold in the gold lease market. Typical lenders of gold in the lease market are entities with large stocks of physical gold on which they wish to earn a rate of return, such as central banks. Typical borrowers of gold are gold dealing desks. Suppose you are a bank intermediating in the gold market. Let the spot gold price (in US dollars) be St ó275.00, let the US dollar 6-month deposit rate be 5.6% and let the 6-month gold lease rate be 2% per annum. The 6-month forward gold price must then be

Ft,T ó 1ò

0.056ñ0.02 · 275ó279.95 2

The gold lease rate plays the role of the dividend rate in our framework. A mining company sells 1000 ounces gold forward for delivery in 6 months at the market price of USD 279.95. You now have a long forward position to hedge, which you can do in several ways. You can use the futures market, but perhaps the delivery dates do not coincide with the forward. Alternatively, you can lease gold from a central bank for 6 months and sell it immediately in the spot market at a price of USD 275.00, investing the proceeds (USD 275 000) in a 6-month deposit at 5.6%. Note that there is no net cash flow now. In 6 months, these contracts are settled. First, you take delivery of forward gold from the miner and immediately return it to the central bank along with a wire transfer of USD 2750. You redeem the deposit, now grown to USD 282 700, from the bank and pay USD 279 950 to the miner. As noted above, it is often difficult to take short positions in physical commodities. The role of the lease market is to create the possibility of shorting gold. Borrowing gold creates a ‘temporary long’ for the hedger, an obligation to divest himself of gold 6 months hence, which can be used to construct the synthetic short forward needed to offset the customer business.

Futures Futures are similar to forwards in all except two important and related respects. First, futures trade on organized commodity exchanges. Forwards, in contrast, trade

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over-the-counter, that is, as simple bilateral transactions, conducted as a rule by telephone, without posted prices. Second, a forward contract involves only one cash flow, at the maturity of the contract, while futures contracts generally require interim cash flows prior to maturity. The most important consequence of the restriction of futures contracts to organized exchanges is the radical reduction of credit risk by introducing a clearinghouse as the counterparty to each contract. The clearinghouse, composed of exchange members, becomes the counterparty to each contract and provides a guarantee of performance: in practice, default on exchange-traded futures and options is exceedingly rare. Over-the-counter contracts are between two individual counterparties and have as much or as little credit risk as those counterparties. Clearinghouses bring other advantages as well, such as consolidating payment and delivery obligations of participants with positions in many different contracts. In order to preserve these advantages, exchanges offer only a limited number of contract types and maturities. For example, contracts expire on fixed dates that may or may not coincide precisely with the needs of participants. While there is much standardization in over-the-counter markets, it is possible in principle to enter into obligations with any maturity date. It is always possible to unwind a futures position via an offsetting transaction, while over-the-counter contracts can be offset at a reasonable price only if there is a liquid market in the offsetting transaction. Settlement of futures contracts may be by net cash amounts or by delivery of the underlying. In order to guarantee performance while limiting risk to exchange members, the clearinghouse requires performance bond from each counterparty. At the initiation of a contract, both counterparties put up initial or original margin to cover potential default losses. Both parties put up margin because at the time a contract is initiated, it is not known whether the terminal spot price will favor the long or the short. Each day, at that day’s closing price, one counterparty will have gained and the other will have lost a precisely offsetting amount. The loser for that day is obliged to increase his margin account and the gainer is permitted to reduce his margin account by an amount, called variation margin, determined by the exchange on the basis of the change in the futures price. Both counterparties earn a short-term rate of interest on their margin accounts. Margining introduces an importance difference between the structure of futures and forwards. If the contract declines in value, the long will be putting larger and larger amounts into an account that earns essentially the overnight rate, while the short will progressively reduce his money market position. Thus the value of the futures, in contrast to that of a forward, will depend not only on the expected future price of the underlying asset, but also on expected future short-term interest rates and on their correlation with future prices of the underlying asset. The price of a futures contract may therefore be higher or lower than the price of a congruent forward contract. In practice, however, the differences are very small. Futures prices are expressed in currency units, with a minimum price movement called a tick size. In other words, futures prices cannot be any positive number, but must be rounded off to the nearest tick. For example, the underlying for the Eurodollar futures contract on the Chicago Mercantile Exchange (CME) is a threemonth USD 1 000 000 deposit at Libor. Prices are expressed as 100 minus the Libor rate at futures contract expiry, so a price of 95.00 corresponds to a terminal Libor rate of 5%. The tick size is one basis point (0.01). The value of one tick is the increment

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13

in simple interest resulting from a rise of one basis point: USD 1 000 000 · 0.0001· 90 360 ó25. Another example is the Chicago Board of Trade (CBOT) US Treasury bond futures contract. The underlying is a T-bond with a face value of USD 100 000 and a minimum remaining maturity of 15 years. Prices are in percent of par, and the tick 1 of a percentage point of par. size is 32 The difference between a futures price and the cash price of the commodity is called the basis and basis risk is the risk that the basis will change unpredictably. The qualification ‘unpredictably’ is important: futures and cash prices converge as the expiry date nears, so part of the change in basis is predictable. For market participants using futures to manage exposures in the cash markets, basis risk is the risk that their hedges will offset only a smaller part of losses in the underlying asset. At expiration, counterparties with a short position are obliged to make delivery to the exchange, while the exchange is obliged to make delivery to the longs. The deliverable commodities, that is, the assets which the short can deliver to the long to settle the futures contract, are carefully defined. Squeezes occur when a large part of the supply of a deliverable commodity is concentrated in a few hands. The shorts can then be forced to pay a high price for the deliverable in order to avoid defaulting on the futures contract. In most futures markets, a futures contract will be cash settled by having the short or long make a cash payment based on the difference between the futures price at which the contract was initiated and the cash price at expiry. In practice, margining will have seen to it that the contract is already largely cash settled by the expiration date, so only a relatively small cash payment must be made on the expiration date itself. The CBOT bond futures contract has a number of complicating features that make it difficult to understand and have provided opportunities for a generation of traders: Ω In order to make many different bonds deliverable and thus avoid squeezes, the contract permits a large class of long-term US Treasury bonds to be delivered into the futures. To make these bonds at least remotely equally attractive to deliver, the exchange establishes conversion factors for each deliverable bond and each futures contract. The futures settlement is then based on the invoice price, which is equal to the futures price times the conversion factor of the bond being delivered (plus accrued interest, if any, attached to the delivered bond). Ω Invoice prices can be calculated prior to expiry using current futures prices. On any trading day, the cash flows generated by buying a deliverable bond in the cash market, selling a futures contract and delivering the purchased bond into the contract can be calculated. This set of transactions is called a long basis position. Of course, delivery will not be made until contract maturity, but the bond that maximizes the return on a long basis position, called the implied repo rate, given today’s futures and bond prices, is called the cheapest-to-deliver. Ω Additional complications arise from the T-bond contract’s delivery schedule. A short can deliver throughout the contract’s expiry month, even though the contract does not expire until the third week of the month. Delivery is a three-day procedure: the short first declares to the exchange her intent to deliver, specifies on the next day which bond she will deliver, and actually delivers the bond on the next day.

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Forward interest rates and swaps Term structure of interest rates The term structure of interest rates is determined in part by expectations of future short-term interest rates, exchange rates, inflation, and the real economy, and therefore provides information on these expectations. Unfortunately, most of the term structure of interest rates is unobservable, in contrast to prices of most assets, such as spot exchange rates or stock-index futures, prices of which are directly observable. The term structure is difficult to describe because fixed-income investments differ widely in the structure of their cash flows. Any one issuer will have debt outstanding for only a relative handful of maturities. Also, most bonds with original maturities longer than a year or two are coupon bonds, so their yields are affected not only by the underlying term structure of interest rates, but by the accident of coupon size.

Spot and forward interest rates To compensate for these gaps and distortions, one can try to build a standard representation of the term structure using observable interest rates. This is typically done in terms of prices and interest rates of discount bonds, fixed-income investments with only one payment at maturity, and spot or zero coupon interest rates, or interest rates on notional discount bonds of different maturities. The spot interest rate is the constant annual rate at which a fixed-income investment’s value must grow starting at time to reach $1 at a future. The spot or zero coupon curve is a function relating spot interest rates to the time to maturity. Most of the zero-coupon curve cannot be observed directly, with two major exceptions: bank deposit rates and short-term government bonds, which are generally discount paper. A forward interest rate is an interest rate contracted today to be paid from one future date called the settlement date to a still later future date called the maturity date. The forward curve relates forward rates of a given time to maturity to the time to settlement. There is thus a distinct forward curve for each time to maturity. For example, the 3-month forward curve is the curve relating the rates on forward 3month deposits to the future date on which the deposits settle. Any forward rate can be derived from a set of spot rates via arbitrage arguments by identifying the set of deposits or discount bonds which will lock in a rate prevailing from one future date to another, without any current cash outlay.

Forward rate agreements Forward rate agreements (FRAs) are forwards on time deposits. In a FRA, one party agrees to pay a specific interest rate on a Eurodeposit of a specified currency, maturity, and amount, beginning at a specified date in the future. FRA prices are defined as the spot rate the buyer agrees to pay on a notional deposit of a given maturity on a given settlement date. Usually, the reference rate is Libor. For example, a 3î6 (spoken ‘3 by 6’) Japanese yen FRA on ó Y 100 000 000 can be thought of as a commitment by one counterparty to pay another the difference between the contracted FRA rate and the realized level of the reference rate on a ó Y 100 000 000 deposit. Suppose the three-month and six-month Swiss franc Libor rates are respectively

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3.55% and 3.45%. Say Bank A takes the long side and Bank B takes the short side of a DM 10 000 000 3î6 FRA on 1 January at a rate of 3.30%, and suppose threemonth DM Libor is 3.50%. If the FRA were settled by delivery, Bank A would place a three-month deposit with Bank B at a rate of 3.30%. It could then close out its 90 î position by taking a deposit at the going rate of 3.50%, gaining 0.002î 360 10 000 000ó5000 marks when the deposits mature on 1 June. FRAs are generally cash-settled by the difference between the amount the notional deposit would earn at the FRA rate and the amount it would earn at the realized Libor or other reference rate, discounted back to the settlement date. The FRA is cash-settled by Bank B paying Bank A the present value of DM 5000 on 1 March. 90 With a discount factor of 1.045î 360 ó1.01125, that comes to DM 4944.38.

Swaps and forward swaps A plain vanilla interest rate swap is an agreement between two counterparties to exchange a stream of fixed interest rate payments for a stream of floating interest rate payments. Both streams are denominated in the same currency and are based on a notional principal amount. The notional principal is not exchanged. The design of a swap has three features that determine its price: the maturity of the swap, the maturity of the floating rate, and the frequency of payments. We will assume for expository purposes that the latter two features coincide, e.g. if the swap design is fixed against six-month Libor, then payments are exchanged semiannually. At initiation, the price of a plain-vanilla swap is set so its current value – the net value of the two interest payment streams, fixed and floating – is zero. The swap can be seen as a portfolio which, from the point of view of the payer of fixed interest (called the ‘payer’ in market parlance) is long a fixed-rate bond and short a floatingrate bond, both in the amount of the notional principal. The payer of floating-rate interest (called the ‘receiver’ in market parlance) is long the floater and short the fixed-rate bond. The price of a swap is usually quoted as the swap rate, that is, as the yield to maturity on a notional par bond. What determines this rate? A floating-rate bond always trades at par at the time it is issued. The fixed-rate bond, which represents the payer’s commitment in the swap, must then also trade at par if the swap is to have an initial value of zero. In other words, the swap rate is the market-adjusted yield to maturity on a par bond. Swap rates are also often quoted as a spread over the government bond with a maturity closest to that of the swap. This spread, called the swap-Treasury spread, is almost invariably positive, but varies widely in response to factors such as liquidity and risk appetites in the fixed-income markets. A forward swap is an agreement between two counterparties to commence a swap at some future settlement date. As in the case of a cash swap, the forward swap rate is the market-adjusted par rate on a coupon bond issued at the settlement date. The rate on a forward swap can be calculated from forward rates or spot rates.

The expectations hypothesis of the term structure In fixed-income markets, the efficient markets hypothesis is called the expectations hypothesis of the term structure. As is the case for efficient markets models of other asset prices, the expectations hypothesis can be readily formulated in terms of the forward interest rate, the price at which a future interest rate exposure can be locked

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in. Forward interest rates are often interpreted as a forecast of the future spot interest rate. Equivalently, the term premium or the slope of the term structure – the spread of a long-term rate over a short-term rate – can be interpreted as a forecast of changes in future short-term rates. The interpretation of forward rates as forecasts implies that an increase in the spread between long- and short-term rates predicts a rise in both short- and long-term rates. The forecasting performance of forward rates with respect to short-term rates has generally been better than that for long-term rates. Central banks in industrialized countries generally adopt a short-term interest rate as an intermediate target, but they also attempt to reduce short-term fluctuations in interest rates. Rather than immediately raising or lowering interest rates quickly by a large amount to adjust them to changes in economic conditions, they change them in small increments over a long period of time. This practice, called interest rate smoothing, results in protracted periods in which the direction and likelihood, but not the precise timing, of the next change in the target interest rate can be guessed with some accuracy, reducing the error in market predictions of short-term rates generally. Central banks’ interest rate smoothing improves the forecasting power of shortterm interest rate futures and forwards at short forecasting horizons. This is in contrast to forwards on foreign exchange, which tend to predict better at long horizons. At longer horizons, the ability of forward interest rates to predict future short-term deteriorates. Forward rates have less ability to predict turning points in central banks’ monetary stance than to predict the direction of the next move in an already established stance.

Option basics Option terminology A call option is a contract giving the owner the right, but not the obligation, to purchase, at expiration, an amount of an asset at a specified price called the strike or exercise price. A put option is a contract giving the owner the right, but not the obligation, to sell, at expiration, an amount of an asset at the exercise price. The amount of the underlying asset is called the notional principal or underlying amount. The price of the option contract is called the option premium. The issuer of the option contract is called the writer and is said to have the short position. The owner of the option is said to be long. Figure 1.5 illustrates the payoff profile at maturity of a long position in a European call on one pound sterling against the dollar with an exercise price of USD 1.60. There are thus several ways to be long an asset: Ω Ω Ω Ω

long the spot asset long a forward on the asset long a call on the asset short a put on the asset

There are many types of options. A European option can be exercised only at expiration. An American option can be exercised at any time between initiation of the contract and expiration. A standard or plain vanilla option has no additional contractual features. An

Derivatives basics

17

Intrinsic value 0.10

0.05

Spot 1.50

1.55

1.60

1.65

1.70

Figure 1.5 Payoff on a European call option.

exotic option has additional features affecting the payoff. Some examples of exotics are Ω Barrier options, in which the option contract is initiated or cancelled if the asset’s cash price reaches a specified level. Ω Average rate options, for which the option payoff is based on the average spot price over the duration of the option contract rather than spot price at the time of exercise. Ω Binary options, which have a lump sum option payoff if the spot price is above (call) or below (put) the exercise price at maturity. Currency options have an added twist: a domestic currency put is also a foreign currency put. For example, if I give you the right to buy one pound sterling for USD 1.60 in three months, I also give you the right to sell USD 1.60 at £0.625 per dollar.

Intrinsic value, moneyness and exercise The intrinsic value of a call option is the larger of the exercise price minus the current asset price or zero. The intrinsic value of a put is the larger of the current asset price minus the exercise or zero. Denoting the exercise price by X, the intrinsic value of a call is St ñX and that of a put is XñSt . Intrinsic value can also be thought of as the value of an option if it were expiring or exercised today. By definition, intrinsic value is always greater than or equal to zero. For this reason, the owner of an option is said to enjoy limited liability, meaning that the worst-case outcome for the owner of the option is to throw it away valueless and unexercised. The intrinsic value of an option is often described by its moneyness: Ω If intrinsic value is positive, the option is said to be in-the-money. Ω If the exchange rate is below the exchange rate, a call option is said to be out-ofthe-money. Ω If the intrinsic value is zero, the option is said to be at-the-money.

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If intrinsic value is positive at maturity, the owner of the option will exercise it, that is, call the underlying away from the writer. Figure 1.6 illustrates these definitions for a European sterling call with an exercise price of USD 1.60.

Intrinsic value 0.10 atthemoney

0.05

outof themoney

inthemoney Spot

1.50

1.55

1.60

1.65

1.70

Figure 1.6 Moneyness.

Owning a call option or selling a put option on an asset is like being long the asset. Owning a deep in-the-money call option on the dollar is like being long an amount of the asset that is close to the notional underlying value of the option. Owning a deep out-of-the-money call option on the dollar is like being long an amount of the asset that is much smaller than the notional underlying value of the option.

Valuation basics Distribution- and preference-free restrictions on plain-vanilla option prices Options have an asymmetric payoff profile at maturity: a change in the exchange rate at expiration may or may not translate into an equal change in option value. The difficulty in valuing options and managing option risks arises from the asymmetry in the option payoff. Options have an asymmetric payoff profile at maturity: a change in the exchange rate at expiration may or may not translate into an equal change in option value. In contrast, the payoff on a forward increases one-for-one with the exchange rate. In this section, we study some of the many true statements about option prices that do not depend on a model. These facts, sometimes called distribution- and preference-free restrictions on option prices, meaning that they don’t depend on assumptions about the probability distribution of the exchange rate or about market participants’ positions or risk appetites. They are also called arbitrage restrictions to signal the reliance of these propositions on no-arbitrage arguments. Here is one of the simplest examples of such a proposition: Ω No plain vanilla option European or American put or call, can have a negative value: Of course not: the owner enjoys limited liability.

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Another pair of ‘obvious’ restrictions is: Ω A plain vanilla European or American call option cannot be worth more than the current cash price of the asset. The exercise price can be no lower than zero, so the benefit of exercising can be no greater than the cash price. Ω A plain vanilla European or American put option cannot be worth more than the exercise price. The cash price can be no lower than zero, so the benefit of exercising can be no greater than the exercise price. Buying a deep out-of-the-money call is often likened to buying a lottery ticket. The call has a potentially unlimited payoff if the asset appreciates significantly. On the other hand, the call is cheap, so if the asset fails to appreciate significantly, the loss is relatively small. This helps us to understand the strategy of a famous investor who in mid-1995 bought deep out-of-the-money calls on a large dollar amount against the Japanese yen (yen puts) at very low cost and with very little price risk. The dollar subsequently appreciated sharply against the yen, so the option position was then equivalent to having a long cash position in nearly the full notional underlying amount of dollars. The following restrictions pertain to sets of options which are identical in every respect – time to maturity, underlying currency pair, European or American style – except their exercise prices: Ω A plain-vanilla European or American call option must be worth more than a similar option with a lower exercise price. Ω A plain-vanilla European or American put option must be worth more than a similar option with a higher exercise price. We will state a less obvious, but very important, restriction: Ω A plain-vanilla European put or call option is a convex function of the exercise price. To understand this restriction, think about two European calls with different exercise prices. Now introduce a third call option with an exercise price midway between the exercise prices of the first two calls. The market value of this third option cannot be greater than the average value of the first two. Current value of an option Prior to expiration, an option is usually worth at least its intrinsic value. As an example, consider an at-the-money option. Assume a 50% probability the exchange rate rises USD 0.01 and a 50% probability that the rate falls USD 0.01 by the expiration date. The expected value of changes in the exchange rate is 0.5 · 0.01ò0.5 · (ñ1.01)ó0. The expected value of changes in the option’s value is 0.5 · 0.01ò0.5 · 0óñ0.005. Because of the asymmetry of option payoff, only the possibility of a rising rate affects a call option’s value. Analogous arguments hold for in- and out-of-the-money options. ‘But suppose the call is in-the-money. Wouldn’t you rather have the underlying, since the option might go back out-of-the-money? And shouldn’t the option then be worth less than its intrinsic value?’ The answer is, ‘almost never’. To be precise: Ω A European call must be worth at least as much as the present value of the forward price minus the exercise price.

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This restriction states that no matter how high or low the underlying price is, an option is always worth at least its ‘intrinsic present value’. We can express this restriction algebraically. Denote by C(X,t,T ) the current (time t) market value of a European call with an exercise price X, expiring at time T. The proposition states that C(X,t,T )P[1òrt,T (Tñt)]ñ1(Ft,T ñX). In other words, the call must be worth at least its discounted ‘forward intrinsic value’. Let us prove this using a no-arbitrage argument. A no-arbitrage argument is based on the impossibility of a set of contracts that involve no cash outlay now and give you the possibility of a positive cash flow later with no possibility of a negative cash flow later. The set of contracts is Ω Buy a European call on one dollar at a cost of C(X,t,T ). Ω Finance the call purchase by borrowing. Ω Sell one dollar forward at a rate Ft,T . The option, the loan, and the forward all have the same maturity. The net cash flow now is zero. At expiry of the loan, option and forward, you have to repay [1òrt,T (Tñt)]C(X,t,T ), the borrowed option price with interest. You deliver one dollar and receive Ft,T to settle the forward contract. There are now two cases to examine: Case (i): If the option expires in-the-money (S T [X), exercise it to get the dollar to deliver into the forward contract. The dollar then costs K and your net proceeds from settling all the contracts at maturity are Ft,T ñXñ[1òrt,T (Tñt)]C(K,t,T ). Case (ii): If the option expires out-of-the-money (S T OX), buy a dollar at the spot rate S T to deliver into the forward contract. The dollar then costs S T and your net proceeds from settling all the contracts at maturity is Ft,T ñS T ñ[1òrt,T (Tñt)]C(X,t,T ). For arbitrage to be impossible, these net proceeds must be non-positive, regardless of the value of S T . Case (i): If the option expires in-the-money, the impossibility of arbitrage implies Ft,T ñXñ[1òrt,T (Tñt)]C(X,t,T )O0. Case (ii): If the option expires out-of-the-money, the impossibility of arbitrage implies Ft,T ñS T ñ[1òrt,T (Tñt)]C(X,t,T )O0, which in turn implies Ft,T ñXñ[1òrt,T (Tñt)]C(X,t,T )O0. This proves the restriction. Time value Time value is defined as the option value minus intrinsic value and is rarely negative, since option value is usually greater than intrinsic value. Time value is greatest for at-the-money-options and declines at a declining rate as the option goes in- or outof-the-money. The following restriction pertains to sets of American options which are identical in every respect – exercise prices, underlying asset – except their times to maturity. Ω A plain-vanilla American call or put option must be worth more than a similar option with a shorter time to maturity. This restriction does not necessarily hold for European options, but usually does.

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Put-call parity Calls can be combined with forwards or with positions in the underlying asset and the money market to construct synthetic puts with the same exercise price (and vice versa). In the special case of European at-the-money forward options: Ω The value of an at-the-money forward European call is equal to the value of an at-the-money forward European put. The reason is that, at maturity, the forward payoff equals the call payoff minus the put payoff. In other words, you can create a synthetic long forward by going long one ATM forward call and short one ATM forward put. The construction is illustrated in Figure 1.7.

Intrinsic value 0.10 long call payoff 0.05 short put payoff 1.55

1.60

1.65

1.70

ST

0.05 forward payoff 0.10 Figure 1.7 Put-call parity.

Option markets Exchange-traded and over-the-counter options Options are traded both on organized exchanges and over-the-counter. The two modes of trading are quite different and lead to important differences in market conventions. The over-the-counter currency and interest rate option markets have become much more liquid in recent years. Many option market participants prefer the over-the-counter markets because of the ease with which option contracts tailored to a particular need can be acquired. The exchanges attract market participants who prefer or are required to minimize the credit risk of derivatives transactions, or who are required to transact in markets with publicly posted prices. Most money-center commercial banks and many securities firms quote over-thecounter currency, interest rate, equity and commodity option prices to customers. A smaller number participates in the interbank core of over-the-counter option trading, making two-way prices to one another. The Bank for International Settlements (BIS) compiles data on the size and liquidity of the derivatives markets from national surveys of dealers and exchanges. The most recent survey, for 1995, reveals that

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over-the-counter markets dominate trading in foreign exchange derivatives and a substantial portion of the interest rate, equity, and commodity derivatives markets. We can summarize the key differences between exchange-traded and over-thecounter option contracts as follows: Ω Exchange-traded options have standard contract sizes, while over-the-counter options may have any notional underlying amount. Ω Most exchange-traded options are written on futures contracts traded on the same exchange. Their expiration dates do not necessarily coincide with those of the futures contracts, but are generally fixed dates, say, the third Wednesday of the month, so that prices on successive days pertain to options of decreasing maturity. Over-the-counter options, in contrast, may have any maturity date. Ω Exchange-traded option contracts have fixed exercise prices. As the spot price changes, such an option contract may switch from out-of-the-money to in-themoney, or become deeper or less deep in- or out-of-the-money. It is rarely exactly at-the-money. Thus prices on succesive days pertain to options with different moneyness. Ω Mostly American options are traded on the exchanges, while primarily European options, which are simpler to evaluate, are traded over-the-counter. Prices of exchange traded options are expressed in currency units. The lumpiness of the tick size is not a major issue with futures prices, but can be quite important for option prices, particularly prices of deep out-of-the-money options with prices close to zero. The price of such an option, if rounded off to the nearest basis point 1 or 32 may be zero, close to half, or close to double its true market value. This in turn can violate no-arbitrage conditions on option prices. For example, if two options with adjacent exercise prices both have the same price, the convexity requirement is violated. It can also lead to absurdly high or low, or even undefined, implied volatilities and greeks. In spite of their flexibility, there is a good deal of standardization of over-thecounter option contracts, particularly with respect to maturity and exercise prices: Ω The typical maturities correspond to those of forwards: overnight, one week, one, two, three, six, and nine months, and one year. Interest rate options tend to have longer maturities, with five- or ten-year common. A fresh option for standard maturities can be purchased daily, so a series of prices on successive days of options of like maturity can be constructed. Ω Many over-the-counter options are initiated at-the-money forward, meaning their exercise prices are set equal to the current forward rate, or have fixed deltas, so a series of prices on successive days of options of like moneyness can be constructed.

Fixed income options The prices, payoffs, and exercise prices of interest rate options can be expressed in terms of bond prices or interest rates, and the convention differs for different instruments. The terms and conditions of all exchange-traded interest rate options and some over-the-counter interest rate options are expressed as prices rather than rates. The terms and conditions of certain types of over-the-counter interest rate options are expressed as rates. A call expressed in terms of interest rates is identical to a put expressed in terms of prices.

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Caplets and floorlets are over-the-counter calls and puts on interbank deposit rates. The exercise price, called the cap rate or floor rate, is expressed as an interest rates rather than a security price. The payoff is thus a number of basis points rather than a currency amount. Ω In the case of a caplet, the payoff is equal to the cap rate minus the prevailing rate on the maturity date of the caplet, or zero, which ever is larger. For example, a three-month caplet on six-month US dollar Libor with a cap rate of 5.00% has a payoff of 50 basis points if the six-month Libor rate six months hence ends up at 5.50%, and a payoff of zero if the six-month Libor rate ends up at 4.50% Ω In the case of a floorlet, the payoff is equal to the prevailing rate on the maturity date of the cap minus the floor rate, or zero, which ever is larger. For example, a three-month floorlet on six-month US dollar Libor with a floor rate of 5.00% has a payoff of 50 basis points if the six-month Libor rate six months hence ends up at 4.50%, and a payoff of zero if the six-month Libor rate ends up at 5.50% Figure 1.8 compares the payoffs of caplets and floorlets with that of a FRA.

payoff bp 40 20 0 20 payoff on FRA payoff on caplet payoff on floorlet

40 4.6

4.8

5

5.2

5.4

ST

Figure 1.8 FRAs, caps and ﬂoors.

A caplet or a floorlet also specifies a notional principal amount. The obligation of the writer to the option owner is equal to the notional principal amount times the payoff times the term of the underlying interest rate. For example, for a caplet or floorlet on six-month Libor with a payoff of 50 basis points and a notional principal amount of USD 1 000 000, the obligation of the option writer to the owner is USD 0.0050 · 21 · 1 000 000ó2500. To see the equivalence between a caplet and a put on a bond price, consider a caplet on six-month Libor struck at 5%. This is equivalent to a put option on a sixmonth zero coupon security with an exercise price of 97.50% of par. Similarly. A floor rate of 5% would be equivalent to a call on a six-month zero coupon security with an exercise price of 97.50. A contract containing a series of caplets or floorlets with increasing maturities is called a cap or floor. A collar is a combination of a long cap and a short floor. It protects the owner against rising short-term rates at a lower cost than a cap, since

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the premium is reduced by approximately the value of the short floor, but limits the extent to which he benefits from falling short-term rates. Swaptions are options on interest rate swaps. The exercise prices of swaptions, like those of caps and floors, are expressed as interest rates. Every swaption obliges the writer to enter into a swap at the initiative of the swaption owner. The owner will exercise the swaption by initiating the swap if the swap rate at the maturity of the swaption is in his favor. A receiver swaption gives the owner the right to initiate a swap in which he receives the fixed rate, while a payer swaption gives the owner the right to initiate a swap in which he pays the fixed rate. There are two maturities involved in any fixed-income option, the maturity of the option and the maturity of the underlying instrument. To avoid confusion, traders in the cap, floor and swaption markets will describe, say, a six-month option on a twoyear swap as a ‘six-month into two year’ swaption, since the six-month option is exercised ‘into’ a two-year swap (if exercised). There are highly liquid over-the-counter and futures options on actively traded government bond and bond futures of industrialized countries. There are also liquid markets in over-the-counter options on Brady bonds.

Currency options The exchange-traded currency option markets are concentrated on two US exchanges, the International Monetary Market (IMM) division of the Chicago Mercantile Exchange and the Philadelphia Stock Exchange (PHLX). Options on major currencies such as the German mark, Japanese yen, pound sterling and Swiss franc against the dollar, and on major cross rates such as sterling–mark and mark–yen are traded. There are liquid over-the-counter markets in a much wider variety of currency pairs and maturities than on the exchanges.

Equity and commodities There are small but significant markets for over-the-counter equity derivatives, many with option-like features. There are also old and well established, albeit small, markets in options on shares of individual companies. Similarly, while most commodity options are on futures, there exists a parallel market in over-the-counter options, which are frequently components of highly structured transactions. The over-the-counter gold options market is also quite active and is structured in many ways like the foreign exchange options markets.

Option valuation Black–Scholes model In the previous section, we got an idea of the constraints on option prices imposed by ruling out the possibility of arbitrage. For more specific results on option prices, one needs either a market or a model. Options that trade actively are valued in the market; less actively traded options can be valued using a model. The most common option valuation model is the Black–Scholes model. The language and concepts with which option traders do business are borrowed from the Black–Scholes model. Understanding how option markets work and how

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market participants’ probability beliefs are expressed through option prices therefore requires some acquaintance with the model, even though neither traders nor academics believe in its literal truth. It is easiest to understand how asset prices actually behave, or how the markets believe they behave, through a comparison with this benchmark. Like any model, the Black–Scholes model rests on assumptions. The most important is about how asset prices move over time: the model assumes that the asset price is a geometric Brownian motion or diffusion process, meaning that it behaves over time like a random walk with very tiny increments. The Black–Scholes assumptions imply that a European call option can be replicated with a continuously adjusted trading strategy involving positions in the underlying asset and the risk-free bond. This, in turn, implies that the option can be valued using risk-neutral valuation, that is, by taking the mathematical expectation of the option payoff using the risk-neutral probability distribution. The Black–Scholes model also assumes there are no taxes or transactions costs, and that markets are continuously in session. Together with the assumptions about the underlying asset price’s behavior over time, this implies that a portfolio, called the delta hedge, containing the underlying asset and the risk-free bond can be constructed and continuously adjusted over time so as to exactly mimic the changes in value of a call option. Because the option can be perfectly and costlessly hedged, it can be priced by risk-neutral pricing, that is, as though the unobservable equilibrium expected return on the asset were equal to the observable forward premium. These assumptions are collectively called the Black–Scholes model. The model results in formulas for pricing plain-vanilla European options, which we will discuss presently, and in a prescription for risk management, which we will address in more detail below. The formulas for both calls and puts have the same six inputs or arguments: Ω The value of a call rises as the spot price of the underlying asset price rises (see Figure 1.9). The opposite holds for puts.

Call value 0.1 0.08 0.06 0.04 0.02 0

Spot 1.55

1.60

1.65

1.70

Figure 1.9 Call value as a function of underlying spot price.

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Ω The value of a call falls as the exercise price rises (see Figure 1.10). The opposite holds for puts. For calls and puts, the effect of a rise in the exercise price is almost identical to that of a fall in the underlying price.

Call value 0.1 0.08 0.06 0.04 0.02 0

Strike 1.55

1.60

1.65

1.70

Figure 1.10 Call value as a function of exercise price.

Ω The value of a call rises as the call’s time to maturity or tenor rises (see Figure 1.11).

Call value 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0

Days 30

90

180

360

Figure 1.11 Call value as a function of time to maturity.

Ω Call and put values rise with volatility, the degree to which the asset price is expected to wander up or down from where it is now (see Figure 1.12). Ω The call value rises with the domestic interest rate: since the call is a way to be long the asset, its value must be higher when the money market rate – the opportunity cost of being long the cash asset – rises. The opposite is true for put options, since they are an alternative method of being short an asset (see Figure 1.13).

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Call value 0.05 0.04

inthemoney

0.03 atthemoney 0.02 outthemoney

0.01 0

Volatility 0

0.05

0.1

0.15

0.2

Figure 1.12 Call value as a function of volatility.

Call value

0.03

domestic money market

0.025 0.02 dividends

0.015

Volatility 0

0.05

0.1

0.15

0.2

Figure 1.13 Call value as a function of interest and dividend rates.

Ω The call value falls with the dividend yield of the asset, e.g. the coupon rate on a bond, the dividend rate of an equity, or the foreign interest rate in the case of a currency (see Figure 1.13). The reason is that the call owner foregoes this cash income by being long the asset in the form of an option rather than the cash asset. This penalty rises with the dividend yield. The opposite is true for put options. This summary describes the effect of variations in the inputs taken one at a time, that is, holding the other inputs constant. As the graphs indicate, it is important to keep in mind that there are important ‘cross-variation’ effects, that is, the effect of, say, a change in volatility when an option is in-the-money may be different from when it is out-of-the-money. Similarly, the effect of a declining time to maturity may be different when the interest rates are high from the effect when rates are low.

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As a rough approximation, we can take the Black–Scholes formula as a reasonable approximation to the market prices of plain vanilla options. In other words, while we have undertaken to describe how the formula values change in response to the formula inputs, we have also sketched how market prices of options vary with changes in maturity and market conditions.

Implied volatility Volatility is one of the six variables in the Black–Scholes option pricing formulas, but it is the only one which is not part of the contract or an observable market price. Implied volatility is the number obtained by solving one of the Black–Scholes formulas for the volatility, given the numerical values of the other variables. Let us look at implied volatility purely as a number for a moment, without worrying about its meaning. Denote the Black–Scholes formula for the value of a call by v(St ,t,X,T,p,r,r*). Implied volatility is found from the equation C(X,t,T )óv(St ,t,X,T,p,r,r*), which sets the observed market price of an option on the left-hand side equal to the Black– Scholes value on the right-hand side. To calculate, one must find the ‘root’ p of this equation. This is relatively straightforward in a spreadsheet program and there is a great deal of commercial software that performs this as well as other option-related calculations. Figure 1.14 shows that except for deep in- or out-of-the-money options with very low volatility, the Black–Scholes value of an option is strictly increasing in implied volatility. There are several other types of volatility: Ω Historical volatility is a measure of the standard deviation of changes in an asset price over some period in the past. Typically, it is the standard deviation of daily percent changes in the asset price over several months or years. Occasionally, historical volatility is calculated over very short intervals in the very recent past: the standard deviation of minute-to-minute or second-to-second changes over the course of a trading day is called intraday volatility. Ω Expected volatility: an estimate or guess at the standard deviation of daily percent changes in the asset price for, say, the next year. Implied volatility is often interpreted as the market’s expected volatility. The interpretation of volatility is based on the Black–Scholes model assumption that the asset price follows a random walk. If the model holds true precisely, then implied volatility is the market’s expected volatility over the life of the option from which it is calculated. If the model does not hold true precisely then implied volatility is closely related to expected volatility, but may differ from it somewhat. Volatility, whether implied or historical, has several time dimensions that can be a source of confusion: Ω Standard deviations of percent changes over what time intervals? Usually, closeover-close daily percent changes are squared and averaged to calculate the standard deviation, but minute-to-minute changes can also be used, for example, in measuring intraday volatility. Ω Percent changes averaged during what period? This varies: it can be the past day, month, year or week. Ω Volatility at what per-period rate? The units of both historical and implied volatility are generally percent per year. In risk management volatility may be scaled to the

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one-day or ten-day horizon of a value-at-risk calculation. To convert an annual volatility to a volatility per some shorter period – a month or a day – multiply by the square root of the fraction of a year involved. This is called the square-rootof-time rule.

Price volatility and yield volatility In fixed-income option markets, prices are often expressed as yield volatilities rather than the price volatilities on which we have focused. The choice between yield volatility and price volatility corresponds to the choice of considering the option as written on an interest rate or on a bond price. By assuming that the interest rate the option is written on behaves as a random walk, the Black–Scholes model assumption, the Black–Scholes formulas can be applied with interest rates substituted for bond prices. The yield volatility of a fixedincome option, like the price volatility, is thus a Black–Scholes implied volatility. As is the case for other options quoted in volatility term, this practice does not imply that dealers believe in the Black–Scholes model. It means only that they find it convenient to use the formula to express prices. There is a useful approximation that relates yield and price volatilities: Yield volatility5

Price volatility Durationîyield

To use the approximation, the yield must be expressed as a decimal. Note that when yields are low, yield volatility tends to be higher.

Option risk management Option sensitivities and risks Option sensitivities (also known as the ‘greeks’) describe how option values change when the variables and parameters change. We looked at this subject in discussing the variables that go into the Black–Scholes model. Now, we will discuss their application to option risk management. We will begin by defining the key sensitivities, and then describe how they are employed in option risk management practice: Ω Delta is the sensitivity of option value to changes in the underlying asset price. Ω Gamma is the sensitivity of the option delta to changes in the underlying asset price. Ω Vega is the sensitivity of the option delta to changes in the implied volatility of the underlying asset price. Ω Theta is the sensitivity of the option delta to the declining maturity of the option as time passes. Formally, all the option sensitivities can be described as mathematical partial derivatives with respect to the factors that determine option values. Thus, delta is the first derivative of the option’s market price or fair value with respect to the price of the underlying, gamma is the second derivative with respect to the underlying price, vega is the derivative with respect to implied volatility, etc. The sensitivities

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are defined as partial derivatives, so each one assumes that the other factors are held constant. Delta Delta is important for two main reasons. First, delta is a widely used measure of the exposure of an option position to the underlying. Option dealers are guided by delta in determining option hedges. Second, delta is a widely used measure of the degree to which an option is in- or out-of-the-money. The option delta is expressed in percent or as a decimal. Market parlance drops the word ‘percent’, and drops the minus sign on the put delta: a ‘25-delta put’ is a put with a delta of ñ0.25 or minus 25%. Delta is the part of a move in the underlying price that shows up in the price or value of the option. When a call option is deep in-the-money, its value increases almost one-for-one with the underlying asset price, so delta is close to unity. When a call option is deep out-of-the-money, its value is virtually unchanged when the underlying asset price changes, so delta is close to zero. Figure 1.14 illustrates the relationship between the call delta and the rate of change of the option value with respect to the underlying asset price.

Call value 0.020 0.015 0.010 delta at 1.60 0.005 delta at 1.61 Spot 1.58

1.59

1.60

1.61

1.62

Figure 1.14 Delta as the slope of the call function.

The reader may find a summary of the technical properties of delta useful for reference. (Figure 1.15 displays the delta for a typical call option): Ω The call option delta must be greater than 0 and less than or equal to the present value of one currency unit (slightly less than 1). For example, if the discount or risk-free rate is 5%, then the delta of a three-month call cannot exceed 1.0125ñ1 (e ñ0.0125, to be exact). Ω Similarly, the put option delta must be less than 0 and greater or equal to the negative of the present value of one currency unit. For example, if the discount or risk-free rate is 5%, then the delta of a three-month call cannot be less than ñ1.0125ñ1. Ω The put delta is equal to the call delta minus the present value of one currency unit.

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Ω Put-call parity implies that puts and calls with the same exercise price must have identical implied volatilities. For example, the volatility of a 25-delta put equals the volatility of a 75-delta call.

Call delta 1.00

PV of underlying

0.75

0.50

0.25

Spot 1.45

1.50

1.55

1.60

1.65

1.70

Figure 1.15 Call delta.

Gamma Formally, gamma is the second partial derivative of the option price with respect to the underlying price. The units in which gamma is expressed depend on the units of the underlying. If the underlying is expressed in small units (Nikkei average), gamma will be a larger number. If the underlying is expressed in larger units (dollar–mark), gamma will be a smaller number. Gamma is typically greatest for at-the-money options and for options that are close to expiry. Figure 1.16 displays the gamma for a typical call option. Gamma is important because it is a guide to how readily delta will change if there is a small change in the underlying price. This tells dealers how susceptible their positions are to becoming unhedged if there is even a small change in the underlying price. Vega Vega is the exposure of an option position to changes in the implied volatility of the option. Formally, it is defined as the partial derivative of the option value with respect to the implied volatility of the option. Vega is measured in dollars or other base currency units. The change in implied volatility is measured in vols (one voló0.01). Figure 1.17 displays the vega of a typical European call option. Implied volatility is a measure of the general level of option prices. As the name suggests, an implied volatility is linked to a particular option valuation model. In the context of the valuation model on which it is based, an implied volatility has an interpretation as the market-adjusted or risk neutral estimate of the standard deviation of returns on the underlying asset over the life of the option. The most common option valuation model is the Black–Scholes model. This model is now

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Figure 1.16 Gamma.

Κ 14 12

outof themoney

inthemoney

10 8 6 4 2

atthemoney forward

0 110

115

120

125

spot

130

Figure 1.17 Vega.

familiar enough that in some over-the-counter markets, dealers quote prices in terms of the Black–Scholes implied volatility. Vega risk can be thought of as the ‘own’ price risk of an option position. Since implied volatility can be used as a measure of option prices and has the interpretation as the market’s perceived future volatility of returns, option markets can be viewed as markets for the ‘commodity’ asset price volatility: Exposures to volatility are traded and volatility price discovery occur in option markets. Vega risk is unique to portfolios containing options. In her capacity as a pure market maker in options, an option dealer maintains a book, a portfolio of purchased and written options, and delta hedges the book. This leaves the dealer with risks that are unique to options: gamma and vega. The non-linearity of the option payoff with respect to the underlying price generates gamma risk. The sensitivity of the option book to the general price of options generates vega risk.

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Because implied volatility is defined only in the context of a particular model, an option pricing model is required to measure vega. Vega is then defined as the partial derivative of the call or put pricing formula with respect to the implied volatility. Theta Theta is the exposure of an option position to changes in short-term interest rates, in particular, to the rate at which the option position is financed. Formally, it is defined as the partial derivative of the option value with respect to the interest rate. Like vega, theta is measured in dollars or other base currency units. Theta, in a sense, is not a risk, since it not random. Rather, it is a cost of holding options and is similar to cost-of-carry in forward and futures markets. However, unlike cost-of-carry, theta is not a constant rate per unit time, but depends on other factors influencing option prices, particularly moneyness and implied volatility.

Delta hedging and gamma risk Individuals and firms buy or write options in order to hedge or manage a risk to which they are already exposed. The option offsets the risk. The dealers who provide these long and short option positions to end-users take the opposite side of the option contracts and must manage the risks thus generated. The standard procedure for hedging option risks is called delta or dynamic hedging. It ordains Ω Buying or selling forward an amount of the underlying equal to the option delta when the option is entered into, and Ω Adjusting that amount incrementally as the underlying price and other market prices change and the option nears maturity. A dealer hedging, say, a short call, would run a long forward foreign exchange position consisting of delta units of the underlying currency. As the exchange rate and implied volatility changes and the option nears expiration, the delta changes, so the dealer would adjust the delta hedge incrementally by buying or selling currency. The motivation for this hedging procedure is the fact that delta, as the first derivative of the option value, is the basis for a linear approximation to the option value in the vicinity of a specific point. For small moves in the exchange rate, the value of the hedge changes in an equal, but opposite, way to changes in the value of the option position. The delta of the option or of a portfolio of options, multiplied by the underlying amounts of the options, is called the delta exposure of the options. A dealer may immediately delta hedge each option bought or sold, or hedge the net exposure of her entire portfolio at the end of the trading session. In trades between currency dealers, the counterparties may exchange forward foreign exchange in the amount of the delta along with the option and option premium. The option and forward transactions then leave both dealers with no additional delta exposure. This practice is known as crossing the delta. Delta hedging is a linear approximation of changes in the option’s value. However, the option’s value changes non-linearly with changes in the value of the underlying asset: the option’s value is convex. This mismatch between the non-linearity of the option’s value and the linearity of the hedge is called gamma risk. If you are long a call or put, and you delta hedge the option, then a perturbation

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of the exchange rate in either direction will result in a gain on the hedged position. This is referred to as good gamma. Good gamma is illustrated in Figure 1.18. The graph displays a long call position and its hedge. The option owner initially delta hedges the sterling call against the dollar at USD 1.60 by buying an amount of US dollars equal to the delta. The payoff on the hedge is the heavy negatively sloped line. If the pound falls one cent, the value of the call falls by an amount equal to line segment bc, but the value of the hedge rises by ab[bc. If the pound rises one cent, the value of the hedge falls ef, but the call rises by de[ef. Thus the hedged position gains regardless of whether sterling rises or falls.

0.020

long call d

0.015 0.010

a

0.005

b c

e

0

f hedge

0.005 1.58

1.59

1.60

1.61

1.62

Figure 1.18 Good gamma.

If you are short a call or put, and you delta hedge the option, then a perturbation of the exchange rate in either direction will result in a loss on the hedged position. This is referred to as bad gamma. Bad gamma is illustrated in Figure 1.19. The graph displays a short call position and its hedge. The option owner initially delta hedges the sterling call against the dollar at USD 1.60 by selling an amount of US dollars equal to the delta. The payoff on the hedge is the heavy positively sloped line. If the pound falls one cent, the value of the short call position rises by an amount equal to line segment ab, but the value of the hedge falls by bc[ad. If the pound rises one cent, the value of the hedge rises de, but the short call rises by ef[de. Thus the hedged position loses regardless of whether sterling rises or falls. Why not hedge with both delta and gamma? The problem is, if you hedge only with the underlying asset, you cannot get a non-linear payoff on the hedge. To get a ‘curvature’ payoff, you must use a derivative. In effect, the only way to hedge gamma risk is to lay the option position off.

The volatility smile The Black–Scholes model implies that all options on the same asset have identical implied volatilities, regardless of time to maturity and moneyness. However, there

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Value 0.005 hedge 0

d

a

0.005

b

0.010

c

e

f

0.015

short call

0.020 1.58

1.59

1.60

1.61

Spot 1.62

Figure 1.19 Bad gamma.

are systematic ‘biases’ in the implied volatilities of options on most assets. This is a convenient if somewhat misleading label, since the phenomena in question are biased only from the point of view of the Black–Scholes model, which neither dealers nor academics consider an exact description of reality: Ω Implied volatility is not constant but changes constantly. Ω Options with the same exercise price but different tenors often have different implied volatilities, giving rise to a term structure of implied volatility and indicating that market participants expect the implied volatility of short-dated options to change over time. Ω Out-of-the money options often have higher implied volatilities than at-the-money options, indicating that the market perceives asset prices to be kurtotic, that is, the likelihood of large moves is greater than is consistent with the lognormal distribution. Ω Out-of-the money call options often have implied volatilities which differ from those of equally out-of the money puts, indicating that the market perceives the distribution of asset prices to be skewed. The latter two phenomena are known as the volatility smile because of the characteristic shape of the plot of implied volatilities of options of a given tenor against the delta or against the exercise price.

The term structure of implied volatility A rising term structural volatility indicates that market participants expect shortterm implied volatility to rise or that they are willing to pay more for protection against near-term asset price volatility. Figure 1.20 illustrates a plot of the implied volatilities of options on a 10-year US dollar swap (swaptions) with option maturities between one month and 5 years. Typically, longer-term implied volatilities vary less over time than shorter-term volatilities on the same asset. Also typically, there are only small differences among the historical averages of implied volatilities of different maturities. Longer-term

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Σ

16 15 14 13

0

1

2

3

4

5

Τ

Figure 1.20 Term structure of volatility.

volatilities are therefore usually closer to the historical average of implied volatility than shorter-term implied volatilities. Shorter-term implied volatilities may be below the longer-term volatilities, giving rise to an upward-sloping term structure, or above the longer-term volatilities, giving rise to a downward-sloping term structure. Downward-sloping term structures typically occur when shocks to the market have abruptly raised volatilities across the term structure. Short-term volatility responds most readily, since shocks are usually expected to abate over the course of a year.

The volatility smile Option markets contain much information about market perceptions that asset returns are not normal. Earlier we discussed the differences between the actual behavior of asset returns and the random walk hypothesis which underlies the Black–Scholes option pricing model. The relationship between in- or out-of-the money option prices and those of at-the-money options contains a great deal of information about the market perception of the likelihood of large changes, or changes in a particular direction, in the cash price. The two phenomena of curvature and skewness generally are both present in the volatility smile, as in the case depicted in Figure 1.21. The chart tells us that options which pay off if asset prices fall by a given amount are more highly valued than options which pay off if asset prices rise. That could be due to a strong market view that asset prices are more likely to fall than to rise; it could also be due in part to market participants seeking to protect themselves against losses from falling rates or losses in other markets associated with falling rates. The market is seeking to protect itself particularly against falling rather than rising rates. Also, the market is willing to pay a premium for option protection against large price changes in either direction. Different asset classes have different characteristic volatility smiles. In some markets, the typical pattern is highly persistent. For example, the negatively sloping smile for S&P 500 futures options illustrated in Figure 1.22 has been a virtually

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Figure 1.21 Volatility smile in the foreign exchange market.

permanent feature of US equity index options since October 1987, reflecting market eagerness to protect against a sharp decline in US equity prices. In contrast, the volatility smiles of many currency pairs such as dollar–mark have been skewed, depending on market conditions, against either the mark or the dollar.

Σ 33

futures price

32

31

30 Strike 1025

1030

1035

1040

1045

1050

1055

1060

Figure 1.22 Volatility smile in the S&P futures market.

Over-the-counter option market conventions Implied volatility as a price metric One of the most important market conventions in the over-the-counter option markets is to express option prices in terms of the Black–Scholes implied volatility. This convention is employed in the over-the-counter markets for options on currencies, gold, caps, floors, and swaptions. The prices of over-the-counter options on

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bonds are generally expressed in bond price units, that is, percent of par. It is generally used only in price quotations and trades among interbank dealers, rather than trades between dealers and option end-users. The unit of measure of option prices under this convention, is implied volatility at an annual percent rate. Dealers refer to the units as vols. If, for example, a customer inquires about dollar–mark calls, the dealer might reply that ‘one-month at-themoney forward dollar calls are 12 at 12.5’, meaning that the dealer buys the calls at an implied volatility of 12 vols and sells them at 12.5 vols. It is completely straightforward to express options in terms of vols, since as Figure 1.12 makes clear, a price in currency units corresponds unambiguously to any price in vols. When a deal is struck between two traders in terms of vols, the appropriate Black–Scholes formula is used to translate into a price in currency units. This requires the counterparties to agree on the remaining market data inputs to the formula, such as the current forward price of the underlying and the money market rate. Although the Black–Scholes pricing formulas are used to move back and forth between vols and currency units, this does not imply that dealers believe in the Black–Scholes model. The formulas, in this context, are divorced from the model and used only as a metric for price. Option dealers find this convenient because they are in the business of trading volatility, not the underlying. Imagine the dealer maintaining a chalkboard displaying his current price quotations for options with different underlying assets, maturities and exercise prices. If the option prices are expressed in currency units, than as the prices of the underlying assets fluctuate in the course of the trading day, the dealer will be obliged to constantly revise the option prices. The price fluctuations in the underlying may, however, be transitory and random, related perhaps to the idiosyncrasies of order flow, and have no significance for future volatility. By expressing prices in vols, the dealer avoids the need to respond to these fluctuations.

Delta as a metric for moneyness The moneyness of an option was defined earlier in terms of the difference between the underlying asset price and the exercise price. Dealers in the currency option markets often rely on a different metric for moneyness, the option delta, which we encountered earlier as an option sensitivity and risk management tool. As shown in Figure 1.23, the call delta declines monotonically as exercise price rises – and the option goes further out-of-the-money – so dealers can readily find the unique exercise price corresponding to a given delta, and vice versa. The same holds for puts. Recall that the delta of a put is equal to the delta of a call with the same exercise price, minus the present value of one currency unit (slightly less than one). Often, exercise prices are set to an exchange rate such that delta is equal to a round number like 25% or 75%. A useful consequence of put–call parity (discussed above) is that puts and calls with the same exercise price must have identical implied volatilities. The volatility of a 25-delta put is thus equal to the volatility of a 75-delta call. Because delta varies as market conditions, including implied volatility, the exercise price corresponding to a given delta and the difference between that exercise price and the current forward rate vary over time. For example, at an implied volatility of 15%, the exercise prices of one-month 25-delta calls and puts are about 3% above and below the current forward price, while at an implied volatility of 5%, they are about 1% above and below.

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Exercise price

1.70 1.60 1.50 1.40 Call delta 0

0.2

0.4

0.6

0.8

1

Figure 1.23 Exercise price as a function of delta.

The motivation for this convention is similar to that for using implied volatility: it obviates the need to revise price quotes in response to transitory fluctuations in the underlying asset price. In addition, delta can be taken as an approximation to the market’s assessment of the probability that the option will be exercised. In some trading or hedging techniques involving options, the probability of exercise is more relevant than the percent difference between the cash and exercise prices. For example, a trader taking the view that large moves in the asset price are more likely than the market is assessing might go long a 10-delta strangle. He is likely to care only about the market view that there is a 20% chance one of the component options will expire in-the-money, and not about how large a move that is.

Risk reversals and strangles A combination is an option portfolio containing both calls and puts. A spread is a portfolio containing only calls or only puts. Most over-the-counter currency option trading is in combinations. The most common in the interbank currency option markets is the straddle, a combination of an at-the-money forward call and an atthe-money forward put with the same maturity. Straddles are also quite common in other over-the-counter option markets. Figure 1.24 illustrates the payoff at maturity of a sterling–dollar straddle struck at USD 1.60. Also common in the interbank currency market are combinations of out-of-themoney options, particularly the strangle and the risk reversal. These combinations both consist of an out-of-the-money call and out-of-the-money put. The exercise price of the call component is higher than the current forward exchange rate and the exercise price of the put is lower. In a strangle, the dealer sells or buys both out-of-the-money options from the counterparty. Dealers usually quote strangle prices by stating the implied volatility at which they buy or sell both options. For example, the dealer might quote his selling price as 14.6 vols, meaning that he sells a 25-delta call and a 25-delta put at an implied volatility of 14.6 vols each. If market participants were convinced that exchange rates move as random walks, the out-of-the-money options would have the

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Payoff 0.1 0.08 0.06 0.04 0.02 0 1.55

1.60

1.65

1.70

Terminal price

Figure 1.24 Straddle payoff.

same implied volatility as at-the-money options and strangle spreads would be zero. Strangles, then, indicate the degree of curvature of the volatility smile. Figure 1.25 illustrates the payoff at maturity of a 25-delta dollar–mark strangle.

Payoff 0.08 0.06 0.04 0.02 0 1.50

1.55

1.60

1.65

1.70

Terminal price

Figure 1.25 Strangle payoff.

In a risk reversal, the dealer exchanges one of the options for the other with the counterparty. Because the put and the call are generally not of equal value, the dealer pays or receives a premium for exchanging the options. This premium is expressed as the difference between the implied volatilities of the put and the call. The dealer quotes the implied volatility differential at which he is prepared to exchange a 25-delta call for a 25-delta put. For example, if dollar–mark is strongly expected to fall (dollar depreciation), an options dealer might quote dollar–mark risk reversals as follows: ‘one-month 25-delta risk reversals are 0.8 at 1.2 mark calls over’. This means he stands ready to pay a net premium of 0.8 vols to buy a 25-delta

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41

mark call and sell a 25-delta mark put against the dollar, and charges a net premium of 1.2 vols to sell a 25-delta mark call and buy a 25-delta mark put. Figure 1.26 illustrates the payoff at maturity of a 25-delta dollar–mark risk reversal.

Payoff

0.05 0 0.05

1.50

1.55

1.60

1.65

1.70

Terminal price

Figure 1.26 Risk reversal payoff.

Risk reversals are commonly used to hedge foreign exchange exposures at low cost. For example, a Japanese exporter might buy a dollar-bearish risk reversal consisting of a long 25-delta dollar put against the yen and short 25-delta dollar call. This would provide protection against a sharp depreciation of the dollar, and provide a limited zone – that between the two exercise prices – within which the position gains from a stronger dollar. However, losses can be incurred if the dollar strengthens sharply. On average during the 1990s, a market participant desiring to put on such a position has paid the counterparty a net premium, typically amounting to a few tenths of a vol. This might be due to the general tendency for the dollar to weaken against the yen during the floating exchange rate period, or to the persistent US trade deficit with Japan.

2

Measuring volatility KOSTAS GIANNOPOULOS

Introduction The objective of this chapter is to examine the ARCH family of volatility models and its use in risk analysis and measurement. An overview of unconditional and conditional volatility models is provided. The former is based on constant volatilities while the latter uses all information available to produce current (or up-do-date) volatility estimates. Unconditional models are based on rigorous assumptions about the distributional properties of security returns while the conditional models are less rigorous and treat unconditional models as a special case. In order to simplify the VaR calculations unconditional models make strong assumptions about the distributional properties of financial time series. However, the convenience of these assumptions is offset by the overwhelming evidence found in the empirical distribution of security returns, e.g. fat tails and volatility clusters. VaR calculations based on assumptions that do not hold, underpredict uncommonly large (but possible) losses. In this chapter we will argue that one particular type of conditional model (ARCH/ GARCH family) provides more accurate measures of risk because it captures the volatility clusters present in the majority of security returns. A comprehensive review of the conditional heteroskedastic models is provided. This is followed by an application of the models for use in risk management. This shows how the use of historical returns of portfolio components and current portfolio weights can generate accurate estimates of current risk for a portfolio of traded securities. Finally, the properties of the GARCH family of models are treated rigorously in the Appendix.

Overview of historical volatility models Historical volatility is a static measure of variability of security returns around their mean; they do not utilize current information to update their estimate. This implies that the mean, variance and covariance of the series are not allowed to vary over time in response to current information. This is based on the assumption that the returns series is stationary. That is, the series of returns (and, in general, any time series) has constant statistical moments over different periods. If the series is stationary then the historical mean and variance are well defined and there is no conceptual problem in computing them. However, unlike stationary time series, the mean of the sample may become a function of its length when the series is nonstationary. Non-stationarity, as this is referred to, implies that the historical means,

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43

variances and covariances estimates of security return are subject to error estimation. While sample variances and covariances can be computed, it is unlikely that it provides any information regarding the true unconditional second moments since the latter are not well defined. The stationarity of the first two moments in security return has been challenged for a long time and this has called into question the historical volatility models. This is highlighted in Figure 2.1 which shows the historical volatility of FTSE-100 Index returns over a 26- and 52-week interval.

Figure 2.1 Historical volatility of FTSE-100 index.

Figure 2.1 shows that when historical volatilities are computed on overlapping samples and non-equal length time periods, they change over time. This is attributable to the time-varying nature of historical means, variances and covariances caused by sampling error.

Assumptions VaR is estimated using the expression VaRóPpp 2.33t

(2.1)

where Ppp 2.33 is Daily-Earnings-at-Risk (DEaR), which describes the magnitude of the daily losses on the portfolio at a probability of 99%; pp is the daily volatility (standard deviation) of portfolio returns; t is the number of days, usually ten, over which the VaR is estimated; p is usually estimated using the historical variance– covariance. In the historical variance–covariance approach, the variances are defined in 1 T 2 p t2 ó ; e tñi (2.2) T ió1

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where e is the residual returns (defined as actual returns minus the mean). On the other hand, the ES approach is expressed as ê 2 2 p t2 óje tñ1 ò(1ñj) ; jie tñi

(2.3)

ió1

where 0OjO1 and is defined as the decay factor that attaches different weights over the sample period of past squared residual returns. The ES approach attaches greater weight to more recent observations than observations well in the past. The implication of this is that recent shocks will have a greater impact on current volatility than earlier ones. However, both the variance–covariance and the ES approaches require strong assumptions regarding the distributional properties of security returns. The above volatility estimates relies on strong assumptions on the distributional properties of security returns, i.e. they are independently and identically distributed (i.i.d. thereafter). The identically distributed assumption ensures that the mean and the variance of returns do not vary across time and conforms to a fixed probability assumption. The independence assumption ensures that speculative price changes are unrelated to each other at any point of time. These two conditions form the basis of the random walk model. Where security returns are i.i.d. and the mean and the variance of the distribution are known, inferences made regarding the potential portfolio losses will be accurate and remain unchanged over a period of time. In these circumstances, calculating portfolio VaR only requires one estimate, the standard deviation of the change in the value of the portfolio. Stationarity in the mean and variance implies that the likelihood of a specified loss will be the same for each day. Hence, focusing on the distributional properties of security returns is of paramount importance to the measurement of risk. In the next section, we examine whether these assumptions are valid.

The independence assumption Investigating the validity of the independence assumption has focused on testing for serial correlation in changes in price. The general conclusion reached by past investigations is that successive price changes are autocorrelated, but are too weak to be of economic importance. This observation has led most investigations to accept the random walk hypothesis. However, evidence has shown that a lack of autocorrelation does not imply the acceptance of the independence assumption. Some investigations has found that security returns are governed by non-linear processes that allow successive price changes to be linked through the variance. This phenomenon was observed in the pioneering investigations of the 1960s, where large price movements were followed by large price movements and vice versa. More convincing evidence is provided in later investigations in their more rigorous challenge to the identical and independence assumptions. In the late 1980s much attention was focused on using different time series data ranging from foreign exchange currencies to commodity prices to test the validity of the i.i.d. assumptions. Another development in those investigations is the employment of more sophisticated models such as the conditional heteroskedastic models (i.e. ARCH/GARCH) used to establish the extent to which the i.i.d. assumption is violated. These type of models will be examined below.

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The form of the distribution The assumption of normality aims to simplify the measurement of risk. If security returns are normally distributed with stable moments, then the two parameters, the mean and the variance, are sufficient to describe it. Stability implies that the probability of a specified portfolio loss is the same for each day. These assumptions, however, are offset by the overwhelming evidence suggesting the contrary which is relevant to measuring risk, namely the existence of fat tails or leptokurtosis in the distribution that exceeds those of the normal. Leptokurtosis in the distribution of security returns was reported as early as the 1950s. This arises where the empirical distribution of daily changes in stock returns have more observations around the means and in the extreme tails than that of a normal distribution. Consequently, the non-normality in the distribution has led some studies to suggest that attaching alternative probability distributions may be more representative of the data and observed leptokurtosis. One such is the Paretian distribution, which has the characteristic exponent. This is a peakness parameter that measures the tail of the distribution. However, the problem with this distribution is that unlike normal distribution, the variance and higher moments are not defined except as a special case of the normal. Another distribution suggested is the Student t-distribution which has fatter tails than that of the normal, assuming that the degrees of freedom are less than unity. While investigations have found that t-distributions adequately describe weekly and monthly data, as the interval length in which the security returns are measured increases, the t-distribution tends to converge to a normal. Other investigations have suggested that security returns follow a mixture of distributions where the distribution is described as a combination of normal distributions that possess different variances and possible different means.

Non-stationarity in the distribution The empirical evidence does not support the hypothesis of serial dependence (autocorrelation) in security returns. This has caused investigators to focus more directly on non-stationary nature of the two statistical moments, the mean and the variance, which arises when both moments vary over time. Changes in the means and variance of security returns is an alternative explanation to the existence of leptokurtosis in the distribution. Investigations that focus on the non-stationarity in the means have been found to be inconclusive in their findings. However, more concrete evidence is provided when focusing on non-stationarity with respect to the variance. It has been found that it is the conditional dependency in the variance that causes fatter tails in the unconditional distribution that is greater than that of the conditional one. Fatter tails and the non-stationarity in the distribution in the second moments are caused by volatility clustering in the data set. This occurs where rates of return are characterized by very volatile and tranquil periods. If the variances are not stationary then the formula DEaRt does not hold.1

Conditional volatility models The time-varying nature of the variance may be captured using conditional time series models. Unlike historical volatility models, this class of statistical models make more effective use of the information set available at time t to estimate the means

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and variances as varying with time. One type of model that has been successful in capturing time-varying variances and covariances is a state space technique such as the Kalman filter. This is a form of time-varying parameter model which bases regression estimates on historical data up to and including the current time period. A useful attribute of this model lies in its ability to describe historical data that is generated from state variables. Hence the usefulness of Kalman filter in constructing volatility forecasts on the basis of historical data. Another type of model is the conditional heteroskedastic models such as the Autoregressive Conditional Heteroskedastic (ARCH) and the generalized ARCH (GARCH). These are designed to remove the systematically changing variance from the data which accounts for much of the leptokurtosis in the distribution of speculative price changes. Essentially, these models allow the distribution of the data to exhibit leptokurtosis and hence are better able to describe the empirical distribution of financial data.

ARCH models: a review The ARCH(1) The ARCH model is based on the principal that speculative price changes contain volatility clusters. Suppose that a security’s returns Yt can be modeled as: Yt ómt dòet

(2.4)

where mt is a vector of variables with impact on the conditional mean of Yt , and et is the residual return with zero mean, Etñ1 (et )ó0, and variance Etñ1 (e t2 )óh t . The conditional mean are expected returns that changes in response to current information. The square of the residual return, often referred to as the squared error term, e t2 , can be modeled as an autoregressive process. It is this that forms the basis of the Autoregressive Conditional Heteroskedastic (ARCH) model. Hence the first-order ARCH can be written: 2 h t óuòae tñ1

(2.5)

where u[0 and aP0, and h t denotes the time-varying conditional variance of Yt . 2 This is described as a first-order ARCH process because the squared error term e tñ1 is lagged one period back. Thus, the conditional distribution of et is normal but its conditional variance is a linear function of past squared errors. ARCH models can validate scientifically a key characteristic of time series data that ‘large changes tend to be followed by large changes – of either sign – and small changes tend to be followed by small changes’. This is often referred to as the clustering effect and, as discussed earlier, is one of the major explanations behind the violation of the i.i.d. assumptions. The usefulness of ARCH models relates to its 2 ability to deal with this effect by using squared past forecast errors e tñ1 to predict future variances. Hence, in the ARCH methodology the variance of Yt is expressed as a (non-linear) function of past information, it validates earlier concerns about heteroskedastic stock returns and meets a necessary condition for modeling volatility as conditional on past information and as time varying.

Higher-order ARCH The way in which equations (2.4) and (2.5) can be formulated is very flexible. For

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example, Yt can be written as an autoregressive process, e.g. Yt óYtñ1 òet , and/or can include exogenous variables. More important is the way the conditional variance h t can be expressed. The ARCH order of equation (2.5) can be increased to express today’s conditional variance as a linear function of a greater amount of past information. Thus the ARCH(q) can be written as: 2 2 h t óuòa1 e tñ1 ò. . . òaq e tñq

(2.6)

Such a model specification is generally preferred to a first-order ARCH since now the conditional variance depends on a greater amount of information that goes as far as q periods in the past. With an ARCH(1) model the estimated h t is highly unstable since single large (small) surprises are allowed to drive h t to inadmissible extreme values. With the higher-order ARCH of equation (2.6), the memory of the process is spread over a larger number of past observations. As a result, the conditional variance changes more slowly, which seems more plausible.

Problems As with the ARCH(1), for the variance to be computable, the sum of a1 , . . . , aq in equation (2.6) must be less than one. Generally, this is not a problem with financial return series. However, a problem that often arises with the higher-order ARCH is that not every one of the a1 , . . . , aq coefficients is positive, even if the conditional variance computed is positive at all times. This fact cannot be easily explained in economic terms, since it implies that a single large residual return could drive the conditional variance negative. The model in equation (2.6) has an additional disadvantage. The number of parameters increases with the ARCH order and makes the estimation process formidable. One way of attempting to overcome this problem is to express past errors in an ad hoc linear declining way. Equation (2.6) can then be written as: 2 h t óuòa ; wk e tñk

(2.7)

where wk , kó1, . . . , q and &wk ó1 are the constant linearly declining weights. While the conditional variance is expressed as a linear function of past information, this model attaches greater importance to more recent shocks in accounting for the most of the h t changes. This model was adopted by Engle which has been found to give a good description of the conditional variance in his 1982 study and a later version published in 1983. The restrictions for the variance equation parameters remain as in ARCH(1).

GARCH An alternative and more flexible lag structure of the ARCH(q) model is provided by the GARCH(p, q), or Generalized ARCH model: p

q

2 ò ; bj h tñj h t óuò ; ai e tñi ió1

(2.8)

jó1

with ió1, . . . , p and jó1, . . . , q. In equation (2.8) the conditional variance h t is a function of both past innovations and lagged values conditional variance, i.e.

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h tñ1 , . . . , htñp . The lagged conditional variance is often referred to as old news because it is defined as p

q

2 ò ; bj h tñjñ1 h tñj óuò ; ae tñiñ1 ió1

(2.9)

jó1

In other words, if i, jó1, then htñj in equation (2.8) and formulated in equation (2.9) is explainable by past information and the conditional variance at time tñ2 or lagged two periods back. In order for a GARCH(p, q) model to make sense the next condition must be satisfied: 1PG; ai ò; bj H[0 In this situation the GARCH(p, q) corresponds to an infinite-order ARCH process with exponentially decaying weights for longer lags. Researchers have suggested that loworder GARCH(p, q) processes may have properties similar to high-order ARCH but with the advantage that they have significantly fewer parameters to estimate. Empirical evidence also exists that a low-order GARCH model fits as well or even better than a higher-order ARCH model with linearly declining weights. A large number of empirical studies has found that a GARCH(1, 1) is adequate for most financial time series.

GARCH versus exponential smoothing (ES) In many respects the GARCH(1, 1) representation shares many features of the popular exponential smoothing to which can be added the interpretation that the level of current volatility is a function of the previous period’s volatility and the square of the previous period’s returns. These two models have many similarities, i.e. today’s volatility is estimated conditionally upon the information set available at each period. Both the GARCH(1, 1) model in equation (2.8) and the (ES) model in equation (2.7) use the last period’s returns to determine current levels of volatility. Subsequently, it follows that today’s volatility is forecastable immediately after yesterday’s market closure.2 Since the latest available information set is used, it can be shown that both models will provide more accurate estimators of volatility than the use of historical volatility. However, there are several differences in the operational characteristics of the two models. The GARCH model, for example, uses two independent coefficients to estimate the impact the variables have in determining current volatility, while the ES 2 model uses only one coefficient and forces the variables e tñ1 and h tñ1 to have a unit effect on current period volatility. Thus, a large shock will have longer lasting impact on volatility using the GARCH model of equation (2.8) than the ES model of (2.3) The terms a and b in GARCH do not need to sum to unity and one parameter is not the complement of the other. Hence, it avoids the potential for simultaneity bias in the conditional variance. Their estimation is achieved by maximizing the likelihood function.3 This is a very important point since the values of a and b are critical in determining the current levels of volatility. Incorrect selection of the parameter values will adversely affect the estimation of volatility. The assumption that a and b sum to unity is, however, very strong and presents an hypothesis that can be tested rather than a condition to be imposed. Acceptance of the hypothesis that a and b sum to unity indicates the existence of an Integrated GARCH process or I-GARCH. This is a specification that characterizes the conditional variance h t as exhibiting a

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nonstationary component. The implication of this is that shocks in the lagged 2 squared error term e tñi will have a permanent effect on the conditional variance. Furthermore, the GARCH model has an additional parameter, u, that acts as a floor and prevents the volatility dropping to below that level. In the extreme case where a and bó0, volatility is constant and equal to u. The value of u is estimated together with a and b using maximum likelihood estimation and the hypothesis uó0 can be tested easily. The absence of the u parameter in the ES model allows volatility, after a few quiet trading days, to drop to very low levels.

Forecasting with ARCH models In view of the fact that ARCH models make better use of the available information set than standard time series methodology by allowing excess kurtosis in the distribution of the data, the resulting model fits the observed data set better. However, perhaps the strongest argument in favor of ARCH models lies in their ability to predict future variances. The way the ARCH model is constructed it can ‘predict’ the next period’s variance without uncertainty. Since the error term at time t, et , is known we can rewrite equation (2.5) as h tò1 óuòat2

(2.10)

Thus, the next period’s volatility is found recursively by updating the last observed error in the variance equation (2.5). For the GARCH type of models it is possible to deliver a multi-step-ahead forecast. For example, for the GARCH(1, 1) it is only necessary to update forecasts using: E(h tòs D't )óuò(aòb)E(h tòsñ1 D't )

(2.11)

where u, a and b are GARCH(1, 1) parameters of equation (2.8) and are estimated using the data set available, until period t. Of course, the long-run forecasting procedure must be formed on the basis that the variance equation has been parameterized. For example, the implied volatility at time t can enter as an exogenous variable in the variance equation: E(h tò1 D't )óuòae t2 òbh t òdp t2

if só1

(2.12a)

E(h tòs D't )óuòdp t2 ò(aòb)E(h tòsñ1 D't )

if sP2

(2.12b)

where the term p t2 is the implied volatility of a traded option on the same underlying asset, Y.

ARCH-M (in mean) The ARCH model can be extended to allow the mean of the series to be a function of its own variance. This parameterization is referred to as the ARCH-in-Mean (or ARCH-M) model and is formed by adding a risk-related component to the return equation, in other words, the conditional variance h t . Hence, equation (2.4) can be rewritten as: Yt ómt dòjh t òet

(2.13)

Therefore the ARCH-M model allows the conditional variance to explain directly the dependent variable in the mean equation of (2.13). The estimation process consists

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of solving equations (2.13) and (2.5) recursively. The term jh t has a time-varying impact on the conditional mean of the series. A positive j implies that the conditional mean of Y increases as the conditional variance increases. The (G)ARCH-M model specification is ideal for equity returns since it provides a unified framework to estimate jointly the volatility and the time-varying expected return (mean) of the series by the inclusion of the conditional variance in the mean equation. Unbiased estimates of assets risk and return are crucial to the mean variance utility approach and other related asset pricing theories. Finance theory states that rational investors should expect a higher return for riskier assets. The parameter j in equation (2.13) can be interpreted as the coefficient of relative risk aversion of a representative investor and when recursively estimated, the same coefficient jh t can be seen as the time-varying risk premium. Since, in the presence of ARCH, the variance of returns might increase over time, the agents will ask for greater compensation in order to hold the asset. A positive j implies that the agent is compensated for any additional risk. Thus, the introduction of h t into the mean is another non-linear function of past information. Since the next period’s variance, h tò1 , is known with certainty the next period’s return forecast, E(Ytò1 ), can be obtained recursively. Assuming that mt is known we can rearrange equation (2.13) as E[Ytò1 DIt ]•mtò1 ó'tò1 dò#h tò1

(2.14)

Thus, the series’ expectation at tò1 (i.e. one period ahead into the future) is equal to the series’ conditional mean, mtò1 , at the same period. Unlike the unconditional mean, kóE(Y ), which is not a random variable, the conditional mean is a function of past volatility, and because it uses information for the period up to t, can generally be forecasted more accurately. In contrast to the linear GARCH model, consistent estimation of the parameter estimates of an ARCH-M model are sensitive to the model specification. A model is said to be misspecified in the presence of simultaneity bias in the conditional mean equation as defined in equation (2.13). This arises because the estimates for the parameters in the conditional mean equation are not independent of the estimates of the parameters in the conditional variance. Therefore, it has been argued that a misspecification in the variance equation will lead to biased and inconsistent estimates for the conditional mean equation.

Using GARCH to measure correlation Historical variance–covariance matrix: problems In risk management, the monitoring of changes in the variance and covariance of the assets that comprises a portfolio is an extensive process of distinguishing shifts over a period of time. Overseeing changes in the variance (or risk) of each asset and the relationship between the assets in a portfolio through the variance is achieved using the variance–covariance matrix. For the variance and covariance estimates in the matrix to be reliable it is necessary that the joint distributions of security returns are multivariate normal and stationary. However, as discussed earlier, investigations have found that the distribution of speculative price changes are not normally distributed and exhibit fat tails. Consequently, if the distribution of return for

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51

individual series fails to satisfy the i.i.d. assumptions, it is inconceivable to expect the joint distribution to do so. Therefore, forecasts based on past extrapolations of historical estimates must be viewed with skepticism. Hence, the usefulness of conditional heteroskedastic models in multivariate setting to be discussed next.

The multivariate (G)ARCH model All the models described earlier are univariate. However, risk analysis of speculative prices examines both an asset’s return volatility and its co-movement with other securities in the market. Modeling the co-movement among assets in a portfolio is best archived using a multivariate conditional heteroskedastic model which accounts for the non-normality in the multivariate distribution of speculative price changes. Hence, the ARCH models can find more prominent use in empirical finance if they could describe risk in a multivariate context. There are several reasons for examining the variance parameter of a multivariate distribution of financial time series after modeling within the ARCH framework. For example, covariances and the beta coefficient which in finance theory is used as a measure of the risk could be represented and forecasted in the same way as variances. The ARCH model has been extended to a multivariate case using different parameterizations. The most popular is the diagonal one where each element of the conditional variance–covariance matrix Ht is restricted to depend only on its own lagged squared errors.4 Thus, a diagonal bivariate GARCH(1, 1) is written as: Y1,t ó'T1,t d1 òe1,t

(2.15a)

Y2,t ó'T2,t d2 òe2,t

(2.15b)

with

e1,t

e2,t

~N(0, Ht )

where Y1,t , Y2,t is the return on the two assets over the period (tñ1, t). Conditional on the information available up to time (tñ1), the vector with the surprise errors et is assumed to follow a bivariate normal distribution with zero mean and conditional variance–covariance matrix Ht . Considering a two-asset portfolio, the variance– covariance matrix Ht can be decomposed as 2 h 1,t óu1 òa1 e 1,tñ1 òb1 h 1,tñ1

h 12,t óu12 òa12 e1,tñ1 e2,tñ1 òb12 h 12,tñ1 2 h 2,t óu2 òa2 e 2,tñ1 òb2 h 2,tñ1

(2.16a) (2.16b) (2.16c)

Here h1,t and h2,t can be seen as the conditional variances of assets 1 and 2 respectively. These are expressed as past realizations of their own squared distur2 bances denoted as e 1,tñ1 . The covariance of the two return series, h12,t , is a function of the cross-product between past disturbances in the two assets. The ratio h 1,t h 2,t /h 12,t forms the correlation between assets 1 and 2. However, using the ARCH in a multivariate context is subject to limitations in modeling the variances and covariances in a matrix, most notably, the number of

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variances and covariances that are required to be estimated. For example, in a widely diversified portfolio containing 100 assets, there are 4950 conditional covariances and 100 variances to be estimated. Any model used to update the covariances must keep to the multivariate normal distribution otherwise the risk measure will be biased. Given the computationally intensive nature of the exercise, there is no guarantee that the multivariate distribution will hold.

Asymmetric ARCH models The feature of capturing the volatility clustering in asset returns has made (G)ARCH models very popular in empirical studies. Nevertheless, these models are subject to limitations. Empirical studies have observed that stock returns are negatively related with changes in return volatility. Volatility tends to rise when prices are falling, and to fall when prices are rising. Hence, the existence of asymmetry in volatility, which is often referred to as the leverage effect. All the models described in the previous section assumed that only the magnitude and not the sign of past returns determines the characteristics of the conditional variance, h t . In other words, the ARCH and GARCH models described earlier do not discriminate negative from positive shocks which has been shown to have differing impacts on the conditional variance.

Exponential ARCH (EGARCH) To address some of the limitations an exponential ARCH parameterization or EGARCH has been proposed. The variance of the residual error term for the EGARCH(1, 1) is given by ln(h t )óuòb ln(h tñ1 )òcttñ1 ò{(Dttñ1 Dñ(2/n)1/2 )

(2.17)

where tt óet /h t (standardized residual). Hence, the logarithm of the conditional variance ln(h t ) at period t is a function of the logarithm of the conditional period variance, lagged one period back ln(h tñ1 ), the standarized value of the last residual error, ttñ1 , and the deviation of the absolute value of ttñ1 from the expected absolute value of the standardized normal variate, (2/n)1/2. The parameter c measures the impact ‘asymmetries’ on the last period’s shocks have on current volatility. Thus, if c\0 then negative past errors have a greater impact on the conditional variance ln(h t ) than positive errors. The conditional variance h t is expressed as a function of both the size and sign of lagged errors.

Asymmetric ARCH (AARCH) An ARCH model with properties similar to those of EGARCH is the asymmetric ARCH (AARCH). In its simplest form the conditional variance h t can be written as h t óuòa(etñ1 òc)2 òbh tñ1

(2.18)

The conditional variance parameterization in equation (2.18) is a quadratic function of one-period-past error (etñ1 òc)2 . Since the model of equation (2.18) and higherorder versions of this model formulation still lie within the parametric ARCH, it can therefore, be interpreted as the quadratic projection of the squared series on the information set. The (G)AARCH has similar properties to the GARCH but unlike the latter, which

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53

explores only the magnitude of past errors, the (G)AARCH allows past errors to have an asymmetric effect on h t . That is, because c can take any value, a dynamic asymmetric effect of positive and negative lagged values of et on h t is permitted. If c is negative the conditional variance will be higher when etñ1 is negative than when it is positive. If có0 the (G)AARCH reduces to a (G)ARCH model. Therefore, the (G)AARCH, like the EGARCH, can capture the leverage effect present in the stock market data. When the sum of ai and bj (where ió1, . . . , p and jó1, . . . , q are the orders of the (G)AARCH(p, q) process) is unity then analogously to the GARCH, the model is referred to as Integrated (G)AARCH. As with the GARCH process, the autocorrelogram and partial autocorrelogram of the squares of e, as obtained by an AR(1), can be used to identify the {p, q} orders. In the case of the (G)AARCH(1, 1), the unconditional variance of the process is given by p 2 ó(uòc2a)/(1ñañb)

(2.19)

Other asymmetric speciﬁcations GJR or threshold: 2 2 ¯ tñ1 e tñ1 h t óuòbh tñ1 òae tñ1 òcS

(2.20)

¯ t ó1 if et \0, S ¯ t ó0 otherwise. where S Non-linear asymmetric GARCH: h t óuòbh tñ1 òa(etñ1 òch tñ1 )2

(2.21)

h t óuòbh tñ1 òa(etñ1 /h tñ1 òc)2

(2.22)

VGARCH:

If the coefficient c is positive in the threshold model then negative values of etñ1 have an additive impact on the conditional variance. This allows asymmetry on the conditional variance in the same way as EGARCH and AARCH. If có0 the model reduces to a GARCH(1, 1). Similarly, as in the last two models, the coefficient c measures the asymmetric effect where the negative errors have a greater impact on the variance when c\0.

Identiﬁcation and diagnostic tests for ARCH ARCH models are almost always estimated using the maximum likelihood method and with the use of computationally expensive techniques. Although linear GARCH fits well with a variety of data series and is less sensitive to misspecification, others like the ARCH-M requires that the full model be correctly specified. Thus identification tests for ARCH effects need to be carried out before, and misspecification tests after the estimation process are necessary for proper ARCH modeling.

Identiﬁcation tests One test proposed by Engle is based on the Lagrange Multiplier (LM) principle. To perform the test only estimates of the homoskedastic model are required. Assume that the AR(p) is a stationary process which generates the set of returns, Yt , such

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that {e}~i.i.d. and N(0, p 2 ) and that Yñpñ1 , . . . , Y0 is an initial fixed part of this series. Subsequent inference will be based on a conditional likelihood function. The least squares estimators of the parameters in the above process are denoted by kˆ , a ˆ2, . . . , a ˆ p , where pˆ 2 is an estimator of p 2 , and setting zˆ 2 ó(ñ1, Ytñ1 , . . . , ˆ1, a Ytñ2 , . . . , Ytñp )@. When H0 for homoskedasticity is tested against an ARCH(p) process then the LM statistic is asymptotically equivalent to nR2 from the auxiliary regression 2 òet ˆe t2 óaò&aeˆ tñi

for ió1, . . . , p

H0 : a1 óa2 ó. . . óap ó0

(2.23)

H1 : ai Ö0 Under H0 the LMónR 2 has a chi-squared distribution denoted as s2(p), where p represents the number of lags. However, when the squared residuals are expressed as linearly declining weights of past squared errors the LM test for ARCH, which will follow a s2(1) distribution, will be 2 òet ˆe t2 óaòa1 &w1 ˆe tñ1

(2.24)

where wi are the weights which decline at a constant rate. The above test has been extended to deal with the bivariate specification of the ARCH models. When the model is restricted to the diagonal representation, then 2 3N(R 12 òR 22 òR 12 ) is distributed as s2(3p), where R2 is the coefficient of determination. 2 The terms R 1 , R 22 stand for the autoregression of squared residuals for each of the 2 denotes the autoregression of the covariance for the two series two assets, and R 12 residuals. Researchers also suggest that the autocorrelogram and partial autocorrelogram for ˆe t2 can be used to specify the GARCH order {p, q} in a similar way to that used to identify the order of a Box–Jenkins ARMA process. The Box–Pierce Q-statistic for the normalized squared residuals (i.e. (eˆ t2 /h t )) can be used as a diagnostic test against higher-order specifications for the variance equation. If estimations are performed under both the null and alternative hypotheses, likelihood ratio (LR) tests can be obtained by LRóñ2(ML(h0 )ñML(ha ))~s2(k) where ML(h0 ) and ML(ha ) are the ML function evaluations and k is the number of restrictions in the parameters.

Diagnostic tests Correct specification of h t is very important. For example, because h t relates future variances to current information, the accuracy of forecast depends on the selection of h t . Therefore, diagnostic tests should be employed to test for model misspecification. Most of the tests dealing with misspecification examine the properties of the standardized residuals defined as ˆe t* óeˆt htñ1 which is designed to make the residual returns conform to a normal distribution. If the model is correctly specified the standardized residuals should behave as a white noise series. That is because under the ARCH model ˆe t* óeˆt htñ1 D'tñ1~N(0,1)

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55

For example, the autocorrelation of squared residual returns, ˆe t2 , or normalized squared residuals (eˆ *2 )2 may reveal a model failure. More advanced tests can also be used to detect non-linearities in (eˆ 2* )2 . An intuitively appealing test has been suggested by Pagan and Schwert. They propose regressing ˆe t2 against a constant and h t , the estimates of the conditional variance, to test the null hypothesis that the coefficient b in equation (2.25) is equal to unity: ˆe t2 óaòbh t òlt

(2.25)

If the forecasts are unbiased, aó0 and bó1. A high R 2 in the above regression indicates that the model has high forecasting power for the variance.

An alternative test A second test, based on the Ljung–Box Q-statistic, tests the standardized squared residuals (Y 12/p 2 ) for normality and hence the acceptance of the i.i.d. assumptions. Large values of the Q-statistic could be regarded as evidence that the standardized residuals violate the i.i.d. assumption and hence normality, while low values of the Q-statistic would provide evidence that the standardized residuals are independent. In other words, when using this test failure to accept the maintained hypothesis of independence would indicate that the estimated variance has not removed all the clusters of volatility. This in turn would imply that the data still holds information that can be usefully translated into volatility.

An application of ARCH models in risk management In this section, we provide an application of how to use GARCH techniques in a simplified approach to estimate a portfolio’s VaR. We will show that the use of historical returns of portfolio components and current weights can produce accurate estimates of current risk for a portfolio of traded securities. Information on the time series properties of returns of the portfolio components is transformed into a conditional estimate of current portfolio volatility without needing to use complex time series procedures. Stress testing and correlation stability are discussed in this framework.

A simpliﬁed way to compute a portfolio’s VaR Traditional VaR models require risk estimates for the portfolio holdings, i.e. variance and correlations. Historical volatilities are ill-behaved measures of risk because they presume that the statistical moments of the security returns remain constant over different time periods. Conditional multivariate time series techniques are more appropriate since they use past information in a more efficient way to compute current variances and covariances. One such model which fits well with financial data is the multivariate GARCH. Its use, however, is restricted to few assets at a time. A simple procedure to overcome the difficulties of inferring current portfolio volatility from past data, is to utilize the knowledge of current portfolio weights and historical returns of the portfolio components in order to construct a hypothetical series of the returns that the portfolio would have earned if its current weights had

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been kept constant in the past. Let Rt be the Nî1 vector (R1,t , R2,t , . . . , Rn ,t ) where Ri ,t is the return on the ith asset over the period (tñ1, t) and let W be the Nî1 vector of the portfolio weights over the same period. The historical returns of our current portfolio holdings are given by: Yt óW TRt

(2.26)

In investment management, if W represents actual investment holdings, the series Y can be seen as the historical path of the portfolio returns.5 The portfolio’s risk and return trade-off can be expressed in terms of the statistical moments of the multivariate distribution of the weighted investments as: E(Yt )óE(W TR)óm

(2.27a)

var(Yt )óW T )Wóp 2

(2.27b)

where ) is the unconditional variance–covariance matrix of the returns of the N assets. A simplified way to find the portfolio’s risk and return characteristics is by estimating the first two moments of Y: E(Y)óm

(2.28a)

var(Y)óE[YñE(Y)]2 óp 2

(2.28b)

Hence, if historical returns are known the portfolio’s mean and variance can be found as in equation (2.28). This is easier than equation (2.27) and still yields identical results. The method in (2.28b) can easily be deployed in risk management to compute the value at risk at any given time t. However, p 2 will only characterize current conditional volatility if W has not changed. If positions are being modified, the series of past returns, Y, needs to be reconstructed and p 2 , the volatility of the new position, needs to be re-estimated as in equation (2.28b). This approach has many advantages. It is simple, easy to compute and overcomes the dimensionality and bias problems that arise when the NîN covariance matrix is being estimated. On the other hand, the portfolio’s past returns contain all the necessary information about the dynamics that govern aggregate current investment holdings. In this chapter we will use this approach to make the best use of this information.6 For example, it might be possible to capture the time path of portfolio (conditional) volatility using conditional models such as GARCH.

An empirical investigation In this example we selected closing daily price indices from thirteen national stock markets7 over a period of 10 years, from the first trading day of 1986 (2 January) until the last trading day of 1995 (29 December). The thirteen markets have been selected in a way that matches the regional and individual market capitalization of the world index. Our data sample represents 93.3% of the Morgan Stanley International world index capitalization.8 The Morgan Stanley Capital International (MSCI) World Index has been chosen as a proxy to the world portfolio. To illustrate how our methodology can be used to monitor portfolio risk we constructed a hypothetical portfolio, diversified across all thirteen national markets of our data sample. To form this hypothetical portfolio we weighted each national index in proportion to its

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57 Table 2.1 Portfolio weights at December 1995 Country

Our portfolio

World index

0.004854 0.038444 0.041905 0.018918 0.013626 0.250371 0.024552 0.007147 0.010993 0.012406 0.036343 0.103207 0.437233

0.004528 0.035857 0.039086 0.017645 0.012709 0.233527 0.022900 0.006667 0.010254 0.011571 0.033898 0.096264 0.407818

Denmark France Germany Hong Kong Italy Japan Netherlands Singapore Spain Sweden Switzerland UK USA

capitalization in the world index as on December 1995. The portfolio weights are reported in Table 2.1. The 10-year historical returns of the thirteen national indexes have been weighted according to the numbers in the table to form the returns of our hypothetical portfolio. Since portfolio losses need to be measured in one currency, we expressed all local returns in US dollars and then formed the portfolio’s historical returns. Table 2.2 reports the portfolio’s descriptive statistics together with the Jarque–Bera normality test. The last column is the probability that our portfolio returns are generated from a normal distribution. Table 2.2 Descriptive statistics of the portfolio historical returns Mean (p.a.) 10.92%

Std dev. (p.a.)

Skewness

Kurtosis

JB test

p-value

12.34%

ñ2.828

62.362

3474.39

0.000

Notes: The test for normality is the Jarque–Bera test, N((p3 )2 /6ò(p4 ñ3)2 /24). The last column is the signiﬁcance level.

Modeling portfolio volatility The excess kurtosis in this portfolio is likely to be caused by changes in its variance. We can capture these shifts in the variance by employing GARCH modeling. For a portfolio diversified across a wide range of assets, the non-constant volatility hypothesis is an open issue.9 The LM test and the Ljung–Box statistic are employed to test this hypothesis. The test statistics with significance levels are reported in Table 2.3. Both tests are highly significant, indicating that the portfolio’s volatility is not constant over different days and the squares of the portfolio returns are serially correlated.10 Table 2.3 Testing for ARCH

Test statistic p-value

LM test (6)

Ljung–Box (6)

352.84 (0.00)

640.64 (0.00)

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One of the advantages that the model in (2.28) is that is simplifies econometric modeling on the portfolio variance. Because we have to model only a single series of returns we can select a conditional volatility model that bests fits the data. There are two families of models, the GARCH and SV (Stochastic Volatility), which are particularly suited to capturing changes in volatility of financial time series. To model the hypothetical portfolio volatility, we use GARCH modeling because it offers wide flexibility in the mean and variance specifications and its success in modeling conditional volatility has been well documented in the financial literature. We tested for a number of different GARCH parameterizations and found that an asymmetric GARCH(1, 1)-ARMA(0, 1) specification best fits11 our hypothetical portfolio. This is defined as: Yt ó'etñ1 òet

et ~NI(0, h t )

(2.29a)

h t óuòa(etñ1 òc)2 òbh tñ1

(2.29b)

The parameter estimates reported in Table 2.4 are all highly significant, confirming that portfolio volatility can be better modeled as conditionally heteroskedastic. The coefficient a that measures the impact of last period’s squared innovation, e, on today’s variance is found to be positive and significant; in addition, (uòac2 )/ (1ñañb)[0 indicating that the unconditional variance is constant. Table 2.4 Parameter estimates of equation (2.30) Series Estimate t-statistic

'

u

a

b

c

Likelihood

0.013 (2.25)

1.949 (3.15)

0.086 (6.44)

0.842 (29.20)

ñ3.393 (5.31)

ñ8339.79

Moreover, the constant volatility model, which is the special case of aóbó0, can be rejected. The coefficient c that captures any asymmetries in volatility that might exist is significant and negative, indicating that volatility tends to be higher when the portfolio’s values are falling. Figure 2.2 shows our hypothetical portfolio’s conditional volatility over the 10-year period. It is clear that the increase in portfolio volatility occurred during the 1987 crash and the 1990 Gulf War.

Diagnostics and stress analysis Correct model specification requires that diagnostic tests be carried out on the fitted residual, ˆe. Table 2.5 contains estimates of the regression: ˆe t2 óaòbhˆ t

(2.30)

with t-statistics given in parentheses. Table 2.5 Diagnostics on the GARCH residuals

Statistic Signiﬁcance

a

b

R2

Q(6) on eˆt

Q(6) on eˆ 2t

JB

ñ7.054 (1.73)

1.224 (1.87)

0.373

3.72 (0.71)

10.13 (0.12)

468.89 (0.00)

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59

Figure 2.2 Portfolio volatility based on bivariate GARCH.

The hypotheses that aó0 and bó1 cannot be rejected at the 95% confidence level, indicating that our GARCH model produces a consistent estimator for the portfolio’s time-varying variance. The uncentered coefficient of determination, R 2 in equation (2.30), measures the fraction of the total variation of everyday returns explained by the estimated conditional variance, and has a value 37.3%. Since the portfolio conditional variance uses the information set available from the previous day, the above result indicates that our model, on average, can predict more than one third of the next day’s squared price movement. The next two columns in Table 2.5 contain the Ljung–Box statistic of order 6 for the residuals and squared residuals. Both null hypotheses, for serial correlation and further GARCH effect, cannot be rejected, indicating that our model has removed the volatility clusters from the portfolio returns and left white noise residuals. The last column contains the Jarque–Bera normality test on the standardized residuals. Although these residuals still deviate from the normal distribution, most of the excess kurtosis has been removed, indicating that our model describes the portfolio returns well. Figure 2.3 illustrates the standardized empirical distribution of these portfolio returns which shows evidence of excess kurtosis in the distribution. The area under the continuous line represents the standardized empirical distribution of our hypothetical portfolio.12 The dashed line shows the shape of the distribution if returns were normally distributed. The values on the horizontal axis are far above and below the (3.0, ñ3.0) range, which is due to very large daily portfolio gains and losses. In Figure 2.4 the standardized innovations of portfolio returns are shown. The upper and lower horizontal lines represent the 2.33 standard deviations (0.01 probability) threshold. We can see that returns are moving randomly net of any volatility clusters. Figure 2.5 shows the Kernel distribution of these standardized innovations against

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Figure 2.3 Empirical distribution of standardized portfolio returns.

Figure 2.4 Portfolio stress analysis (standardized conditional residuals).

the normal distribution. It is apparent that the distribution of these scaled innovations is rather non-normal with values reaching up to fourteen standard deviations. However, when the outliers to the left (which reflect the large losses during the 1987 crash), are omitted, the empirical distribution of the portfolio residual returns matches that of a Gaussian.

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Figure 2.5 Empirical distribution of portfolio conditional distribution.

These results substantiate the credibility of the volatility model in equation (2.29) in monitoring portfolio risk. Our model captures all volatility clusters present in the portfolio returns, removes a large part of the excess kurtosis and leaves residuals approximately normal. Furthermore, our method for estimating portfolio volatility using only one series of past returns is much faster to compute than the variance–covariance method and provides unbiased volatility estimates with higher explanatory power.

Correlation stability and diversiﬁcation beneﬁts In a widely diversified portfolio, e.g. containing 100 assets, there are 4950 conditional covariances and 100 variances to be estimated. Furthermore, any model used to update the covariances must keep the multivariate features of the joint distribution. With a large matrix like that, it is unlikely to get unbiased estimates13 for all 4950 covariances and at the same time guarantee that the joint multivariate distribution still holds. Obviously, errors in covariances as well as in variances will affect the accuracy of our portfolio’s VaR estimate and will lead to wrong risk management decisions. Our approach estimates conditionally the volatility of only one univariate time series, the portfolio’s historical returns, and so overcomes all the above problems. Furthermore, since it does not require the estimation of the variance–covariance matrix, it can be easily computed and can handle an unlimited number of assets. On the other hand it takes into account all changes in assets’ variances and covariances. Another appealing property of our approach is to disclose the impact that the overall changes in correlations have on portfolio volatility. It can tell us what proportion an increase/decrease in the portfolio’s VaR is due to changes in asset variances or correlations. We will refer to this as correlation stability. It is known that each correlation coefficient is subject to changes at any time.

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Nevertheless, changes across the correlation matrix may not be correlated and their impact on the overall portfolio risk may be diminished. Our conditional VaR approach allows us to attribute any changes in the portfolio’s conditional volatility to two main components: changes in asset volatilities and changes in asset correlations. If h t is the portfolio’s conditional variance, as estimated in equation (2.25), its time-varying volatility is pt ó h t . This is the volatility estimate of a diversified portfolio at period t. By setting all pair-wise correlation coefficients in each period equal to 1.0, the portfolio’s volatility becomes the weighted volatility of its asset components. Conditional volatilities of the individual asset components can be obtained by fitting a GARCH-type model for each return series. We denote the volatility of this undiversified portfolio as st . The quantity 1ñ(pt /st ) tells us what proportion of portfolio volatility has been diversified away because of imperfect correlations. If that quantity does not change significantly over time, then the weighted overall effect of time-varying correlations is invariant and we have correlation stability. The correlation stability shown in Figure 2.6 can be used to measure the risk manager’s ability to diversify portfolio’s risk. On a well-diversified (constantly weighted) portfolio, the quantity 1ñ(pt /st ) should be invariant over different periods. It has been shown that a portfolio invested only in bonds is subject to greater correlation risk than a portfolio containing commodities and equities because of the tendency of bonds to fall into step in the presence of large market moves.

Figure 2.6 Portfolio correlation stability: volatility ratio (diversiﬁed versus non-diversiﬁed).

The ‘weighted’ effect of changes in correlations can also be shown by observing the diversified against the undiversified portfolio risk. Figure 2.7 illustrates how the daily annualized standard deviation of our hypothetical portfolio behaves over the tested period. The upper line shows the volatility of an undiversified portfolio; this is the volatility the same portfolio would have if all pair-wise correlation coefficients of the

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63

Figure 2.7 Portfolio conditional volatility (diversiﬁed versus non-diversiﬁed).

assets invested were 1.0 at all times. The undiversified portfolio’s volatility is simply the weighted average of the conditional volatilities of each asset included in the portfolio. Risk managers who rely on average standard historical risk measures will be surprised by the extreme values of volatility a portfolio may produce in a crash. Our conditional volatility estimates provide early warnings about the risk increase and therefore are a useful supplement to existing risk management systems. Descriptive statistics for diversified and undiversified portfolio risk are reported in Table 2.6. These range of volatility are those that would have been observed had the portfolio weights been effective over the whole sample period. Due to the diversification of risk, the portfolio’s volatility is reduced by an average of 40%.14 During the highly volatile period of the 1987 crash, the risk is reduced by a quarter. Table 2.6 Portfolio risk statistic Portfolio risk

Minimum

Maximum

Mean

Diversiﬁed Undiversiﬁed

0.0644 0.01192

0.2134 0.2978

0.0962 0.1632

Portfolio VaR and ‘worst case’ scenario Portfolio VaR A major advantage that our methodology has is that it forecasts portfolio volatility recursively upon the previous day’s volatility. Then it uses these volatility forecasts to calculate the VaR over the next few days. We discuss below how this method is implemented. By substituting the last day’s residual return and variance in equation (2.29b) we can estimate the portfolio’s volatility for day tò1 and by taking the expectation, we

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can estimate recursively the forecast for longer periods. Hence, the portfolio volatility forecast over the next 10 days is h tòi óuòa(et òc)2 òbh t

if ió1

(2.31a)

h tòi/t óuòac2(aòb)h tòiñ1/t

if i[1

(2.31b)

Therefore, when portfolio volatility is below average levels, the forecast values will be rising.15 The portfolio VaR that will be calculated on these forecasts will be more realistic about possible future losses.

Figure 2.8 Portfolio VaR.

Figure 2.8 shows our hypothetical portfolio’s VaR for 10 periods of length between one and 10 days. The portfolio VaR is estimated at the close of business on 29 December 1995. To estimate the VaR we obtain volatility forecasts for each of the next business days, as in equation (2.31). The DEaR is 1.104% while the 10-day VaR is 3.62%. Worst-case scenario VaR measures the market risk of a portfolio in terms of the frequency that a specific loss will be exceeded. In risk management, however, it is important to know the size of the loss rather than the number of times the losses will exceed a predefined threshold. The type of analysis which tells us the worst than can happen to a portfolio’s value over a given period is known as the ‘worst-case scenario’ (WCS). Hence, the WCS is concerned with the prediction of uncommon events which, by definition, are bound to happen. The WCS will answer the question, how badly will it hit?

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For a VaR model, the probability of exceeding a loss at the end of a short period is a function of the last day’s volatility and the square root of time (assuming no serial correlation). Here, however, the issue of fat tails arises. It is unlikely that there exists a volatility model that predicts the likelihood and size of extreme price moves. For example, in this study we observed that the GARCH model removes most of the kurtosis but still leaves residuals equal to several standard deviations. Given that extreme events, such as the 1987 crash, have a realistic probability of occurring again at any time, any reliable risk management system must account for them. The WCS is commonly calculated by using structured Monte Carlo simulation (SMC). This method aims to simulate the volatilities and correlations of all assets in the portfolio by using a series of random draws of the factor shocks (etò1 ). At each simulation run, the value of the portfolio is projected over the VaR period. By repeating the process several thousand times, the portfolio returns density function is found and the WCS is calculated as the loss that corresponds to a very small probability under that area. There are three major weaknesses with this analysis. First, there is a dimensionality problem which also translates to computation time. To overcome this, RiskMetrics proposes to simplify the calculation of the correlation matrix by using a kind of factorization. Second, the SMC method relies on a (timeinvariant) correlation structure of the data. But as we have seen, security covariances are changing over different periods and the betas tend to be higher during volatile periods like that of the 1987 crash. Hence, correlations in the extremes are higher and the WCS will underestimate the risk. Finally, the use of a correlation matrix requires returns in the Monte Carlo method to follow an arbitrary distribution. In practice the empirical histogram of returns is ‘smoothed’ to fit a known distribution. However, the WCS is highly dependent on a good prediction of uncommon events or catastrophic risk and the smoothing of the data leads to a cover-up of extreme events, thereby neutralizing the catastrophic risk. Univariate Monte Carlo methods can be employed to simulate directly various sample paths of the value of the current portfolio holdings. Hence, once a stochastic process for the portfolio returns is specified, a set of random numbers, which conform to a known distribution that matches the empirical distribution of portfolio returns, is added to form various sample paths of portfolio return. The portfolio VaR is then estimated from the corresponding density function. Nevertheless, this method is still exposed to a major weakness. The probability density of portfolio residual returns is assumed to be known.16 In this application, to further the acceptance of the VaR methodology, we will assess its reliability under conditions likely to be uncorrelated in financial markets. The logical method to investigate this issue is through the use of historical simulation which relies on a uniform distribution to select innovations from the past.17 These innovations are applied to current asset prices to simulate their future evolution. Once a sufficient number of different paths has been explored, it is possible to determine a portfolio VaR without making arbitrary distributional assumptions. This is especially useful in the presence of abnormally large portfolio returns. To make historical simulation consistent with the clustering of large returns, we will employ the GARCH volatility estimates of equation (2.29) to scale randomly selected past portfolio residual returns. First, the past daily portfolio residual returns are divided by the corresponding GARCH volatility estimates to obtain standardized residuals. Hence, the residual returns used in the historical simulation are i.i.d.,

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which ensures that the portfolio simulated returns will not be biased. A simulated portfolio return for tomorrow is obtained by multiplying randomly selected standardized residuals by the GARCH volatility to forecast the next day’s volatility. This simulated return is then used to update the GARCH forecast for the following days, that is, it is multiplied by a newly selected standardized residual to simulate the return for the second day. This recursive procedure is repeated until the VaR horizon (i.e. 10 days) is reached, generating a sample path of portfolio volatilities and returns. A batch of 10 000 sample paths of portfolio returns is computed and a confidence band for the portfolio return is built by taking the first and the ninety-ninth percentile of the frequency distribution of returns at each time. The lower percentile identifies the VaR over the next 10 days. To illustrate our methodology we use the standardized conditional residuals for our portfolio over the entire 1986–1995 period as shown in Figure 2.4. We then construct interactively the daily portfolio volatility that these returns imply according to equation (2.29). We use this volatility to rescale our returns. The resulting returns reflect current market conditions rather than historical conditions associated with the returns in Figure 2.3. To obtain the distribution of our portfolio returns we replicated the above procedure 10 000 times. The resulting–normalized–distribution is shown in Figure 2.9. The normal distribution is shown in the same figure for comparison. Not surprisingly, simulated returns on our well-diversified portfolio are almost

Figure 2.9 Normalized estimated distribution of returns in 10 days versus the normal density (10 000 simulations).

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normal, except for their steeper peaking around zero and some clustering in the tails. The general shape of the distribution supports the validity of the usual measure of VaR for our portfolio. However, a closer examination of our simulation results shows how even our well-diversified portfolio may depart from normality under worst-case scenarios. There are in fact several occurrences of very large negative returns, reaching a maximum loss of 7.22%. Our empirical distribution implies (under the WCS) losses of at least 3.28% and 2.24% at confidence levels of 1% and 5% respectively.18 The reason for this departure is the changing portfolio volatility and thus portfolio VaR, shown in Figure 2.10. Portfolio VaR over the next 10 days depends on the random returns selected in each simulation run. Its pattern is skewed to the right, showing how large returns tend to cluster in time. These clusters provide realistic WCS consistent with historical experience. Of course, our methodology may produce more extreme departures from normality for less diversified portfolios.

Figure 2.10 Estimated distribution of VaR.

Conclusions While portfolio holdings aim at diversifying risk, this risk is subject to continuous changes. The GARCH methodology allows us to estimate past and current and predicted future risk levels of our current position. However, the correlation-based VaR, which employed GARCH variance and covariance estimates, failed the diagnostic tests badly. The VaR model used in this application is a combination of historical

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simulation and GARCH volatility. It relies only on historical data for securities prices but applies the most current portfolio positions to historical returns. The use of historical returns of portfolio components and current weights can produce accurate estimates of current risk for a portfolio of traded securities. Information on the time series properties of returns of the portfolio components is transformed into a conditional estimate of the current portfolio volatility with no need to use complex multivariate time series procedures. Our approach leads to a simple formulation of stress analysis and correlation risk. There are three useful products of our methodology. The first is a simple and accurate measure of the volatility of the current portfolio from which an accurate assessment of current risk can be made. This is achieved without using computationally intensive multivariate methodologies. The second is the possibility of comparing a series of volatility patterns similar to Figure 2.7 with the historical volatility pattern of the actual portfolio with its changing weights. This comparison allows for an evaluation of the managers’ ability to ‘time’ volatility. Timing volatility is an important component of performance, especially if expected security returns are not positively related to current volatility levels. Finally, the possibility of using the GARCH residuals on the current portfolio weights allows for the implementation of meaningful stress testing procedures. Stress testing and the evaluation of correlation risk are important criteria in risk management models. To test our simplified approach to VaR we employed a hypothetical portfolio. We fitted an asymmetric GARCH on the portfolio returns and we forecasted portfolio volatility and VaR. The results indicate that this approach to estimating VaR is reliable. This is implied by the GARCH model yielding unbiased estimators for the portfolio conditional variance. Furthermore, this conditional variance estimate can now predict, on average, one third of the next day’s square price movement. We then applied the concept of correlation stability which we argue is a very useful tool in risk management in that it measures the proportion of an increase or decrease in the portfolio VaR caused by changes in asset correlations. In comparing the conditional volatility of our diversified and undiversified hypothetical portfolio, the effects of changes in correlations can be highlighted. While we found that the volatility of the diversified portfolio is lower than the undiversified portfolio, the use of correlation stability has the useful property of acting as an early warning to risk managers in relation to the effects of a negative shock, such as that of a stock market crash, on the riskiness of our portfolio. This is appealing to practitioners because it can be used to determine the ability of risk managers to diversify portfolio risk. Correlation stability is appealing to practitioners because it can be used, both in working with the portfolio selection and assessing the ability of risk managers to diversify portfolio risk. Thereafter, we show how ‘worst-case’ scenarios (WCS) for stress analysis may be constructed by applying the largest outliers in the innovation series to the current GARCH parameters. While the VaR estimated previously considers the market risk of a portfolio in relation to the frequency that a specific loss will be exceeded, it does not determine the size of the loss. Our exercise simulates the effect of the largest historical shock on current market conditions and evaluates the likelihood of a given loss occurring over the VaR horizon. In conclusion, our simulation methodology allows for a fast evaluation of VaR and WCS for large portfolios. It takes into account current market conditions and does not rely on the knowledge of the correlation matrix of security returns.

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Appendix: ARCH(1) properties The unconditional mean The ARCH model specifies that E(eD'tñ1 )ó0 for all realizations of 'tñ1 , the information set. Applying the law of iterated expectations we have E(et )ó0óE[E(et D'tñ1 )]

(A1)

Because the ARCH model specifies that E(et D'tñ1 )ó0 for all realizations of 'tñ1 this implies that E(et )ó0 and so the ARCH process has unconditional mean zero. The set 't contains all available information at time tñ1 but usually includes past returns and variances only.

The unconditional variance Similarly, the unconditional variance of the ARCH(1) process can be written as 2 E(e t2 )óE(e t2 D'tñ1 )óE(h t )óuòaE(e tñ1 )

(A2)

Assuming that the process began infinitely far in the past with finite initial variance, and by using the law of iterated expectations, it can be proved that the sequence of variances converge to a constant: E(e t2 )óh t óp 2 ó

u (1ña)

(A3)

The necessary and sufficient condition for the existence of the variance (the variance to be stationary) is u[0 and 1ña[0. Equation (A3) implies that although the variance of et conditional on 'tñ1 is allowed to change with the elements of the information set ', unconditionally the ARCH process is homoskedastic, hence E(e t2 )óh t óp 2 , the historical variance. After rearranging equation (A3) h t can be written as 2 h t ñp 2 óa(e tñ1 ñp 2 )

(A4)

It follows then that the conditional variance will be greater than the unconditional variance p 2 , whenever the squared past error exceeds p 2 .

The skewness and kurtosis of the ARCH process As et is conditionally normal, it follows that for all odd integers m we have E(e m t D'tñ1 )ó0 Hence, the third moment of an ARCH process is always 0, and is equal to the unconditional moment. However, an expression for the fourth moment, kurtosis, is available only for ARCH(1) and GARCH(1, 1) models. Using simple algebra we can write the kurtosis of an ARCH(1) as19 E(e t4 ) 3(1ña2 ) ó p e4 (1ñ3a2 )

(A5)

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which is greater than 3, the kurtosis coefficient of the normal distribution. This allows the distribution of Yt to exhibit fat tails without violating the normality assumption, and therefore to be symmetric. Researchers have established that the distribution, of price changes or their logarithm, in a variety of financial series symmetric but with fatter tails than those of normal distribution, even if the assumption of normality for the distribution of Yt is violated20 the estimates of an ARCH (family) model will still be consistent, in a statistical sense. Other researchers have advanced the idea that any serial correlation present in conditional second moments of speculative prices could be attributed to the arrival of news within a serial correlated process. We have seen so far that if a\1 and et is conditionally normal, the ARCH model has one very appealing property. It allows the errors, et , to be serially uncorrelated but not necessarily independent, since they can be related through their second moments (when a[0). Of course, if aó0 the process reduces to homoskedastic case.

Estimating the ARCH model The efficient and popular method for computing an ARCH model is maximum likelihood. The likelihood function usually assumes that the conditional density is Gaussian, so the logarithmic likelihood of the sample is given by the sum of the individual normal conditional densities. For example, given a process {yt } with constant mean and variance and drawn from a normal distribution the log likelihood function is given by ln(#)óñ

T T 1 2 ln(2n)ñ ln p 2 ñ p 2 2 2

T

; (Yt ñk)2

(A6a)

tó1

where ln(#) is the natural logarithm of the likelihood function for tó1, . . . , T. The procedure in maximizing the above likelihood function stands to maximize ln(#) for tó1, . . . , T. This involves finding the optimal values of the two parameters, p 2 and k. This is achieved by setting the first-order partial derivatives equal to zero and solving for the values of p 2 and k that yield the maximum value of ln(#). When the term k is replaced with bXt the likelihood function of the classical regression is derived. By replacing p 2 with h t and Yt ñk with e t2 , the variance residual error at time t, the likelihood for the ARCH process is derived. Thus the likelihood function of an ARCM process is given as 1 1 T 1 T e2 ln(#)ó ñ ln(2n)ñ ; ln(h t )ñ ; t 2 tó1 h t 2 2 tó1

(A6b)

Unfortunately ARCH models are highly non-linear and so analytical derivatives cannot be used to calculate the appropriate sums in equation (A6b). However, numerical methods can be used to maximize the likelihood function and obtain the parameter vector #. The preferred approach for maximizing the likelihood function and obtaining the required results is an algorithm proposed by Berndt, Hall and Hausman in a paper published in 1974. Other standard algorithms are available to undertake this task, for example, Newton. These are not recommended since they require the evaluation of the Hessian matrix, and often fail to converge. The strength of the maximum likelihood method to estimate an ARCH model lies in the fact that the conditional variance and mean can be estimated jointly, while

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exogenous variables can still have an impact in the return equation (A1), in the same way as in conventionally specified economic models. However, because ML estimation is expensive to compute, a number of alternative econometric techniques have been proposed to estimate ARCH models. Among those is the generalized method of moments (GMM) and a two-stage least squares method (2SLS). The second is very simple and consists of regressing the squared residuals of an AR(1) against past squared residuals. Although this method provides a consistent estimator of the parameters, it is not efficient.

GARCH(1, 1) properties Following the law of iterated expectations the unconditional variance for a GARCH(1, 1) can be written as E(e t2 )óp 2 óu/(1ñañb)

(A7)

which implies (aòb)\1 in order for h to be finite. An important property emerges from equation (A7). Shocks to volatility decay at a constant rate where the speed of the decay is measured by aòb. The closer that aòb is to one, the higher will be the persistence of shocks to current volatility. Obviously if aòbó1 then shocks to volatility persist for ever, and the unconditional variance is not determined by the model. Such a process is known as ‘Integrated GARCH’, or IGARCH. It can be shown that e t2 can be written as an ARMA(m, p) process with serially uncorrelated innovations Vt , where Vt •e t2 ñh t . The conditional variance of a GARCH(p, q) can be written as 2 e t2 óuò&(a1 òb1 )e tñ1 ò&bjVtñ1 òVt

(A8)

with ió1, . . . , m, mómax{p, q}, a•0 for i[q and bi •0 for i[p. The autoregressive parameters are a(L)òb(L), the moving average ones are ñb(L), and V are the serially non-correlated innovations. The autocorrelogram and partial autocorrelogram for e t2 can be used to identify the order {p, q} of the GARCH model. Thus if a(L)òb(L) is close to one, the autocorrelation function will decline quite slowly, indicating a relatively slow-changing conditional variance. An immediately recognized weakness of ARCH models is that a misspecification in the variance equation will lead to biased estimates for the parameters. Thus, estimates for the conditional variance and mean will no longer be valid in small samples but will be asymptotically consistent. However, GARCH models are not as sensitive to misspecification.

Exponential GARCH properties Unlike the linear (G)ARCH models, the exponential ARCH always guarantees a positive h t without imposing any restriction on the parameters in the variance equation (this is because logarithms are used). In addition, the parameters in the variance equation are always positive solving the problem of negative coefficients often faced in higher-order ARCH and GARCH models. It has been possible to overcome this problem by restricting the parameters to be positive or imposing a declining structure. Furthermore, unlike the GARCH models which frequently reveal that there is a persistence of shocks to the conditional variance, in exponential ARCH the ln(h t ) is strictly stationary and ergodic.

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Notes 1

ES techniques recognize that security returns are non-stationary (variances changes over time). However, they contradict themselves when used to calculate the VaR. 2 The variance in Riskmetrics is using current period surprise (et ). Hence, they have superior information to the GARCH model. 3 Maximization of the likelihood function is the computational price that is involved in using GARCH. The ES model involves only one parameter whose optimum value can be obtained by selecting that estimate which generates the minimum sum of squared residuals. 4 Hence each of the two series of the conditional (GARCH) variance is restricted to depend on its own past values and the last period’s residual errors to be denoted as surprises. Similarly, the conditional (GARCH) estimates for the covariance (which, among others, determine the sine of the cross-correlation coefficient) is modeled on its own last period’s value and the cross-product of the errors of the two assets. 5 When W represents an investment holding under consideration, Y describes the behavior of this hypothetical portfolio over the past. 6 Markowitz incorporates equation (2.27b) in the objective function of his portfolio selection problem because his aim was to find the optimal vector of weights W. However, if W is known a priori then the portfolio’s (unconditional) volatility can be computed more easily as in equation (2.28b). 7 The terms ‘local market’, ‘national market’, ‘domestic market’, ‘local portfolio’, ‘national portfolio’ refer to the national indices and will be used interchangeably through this study. 8 Due to investment restrictions for foreign investors in the emerging markets and other market misconceptions along with data non-availability, our study is restricted to the developed markets only. 9 In a widely diversified portfolio, which may contain different types of assets, the null hypothesis of non-ARCH may not be rejected even if each asset follows a GARCH process itself. 10 If the null hypothesis had not been rejected, then portfolio volatility could be estimated as a constant. 11 A number of different GARCH parameterizations and lag orders have been tested. Among these conditional variance parameterizations are the GARCH, exponential GARCH, threshold GARCH and GARCH with t-distribution in the likelihood function. We used a number of diagnostic tests, i.e. serial correlation, no further GARCH effect, significant t-statistics. The final choice for the model in equation (2.29) is the unbiasedness in conditional variance estimates as tested by the Pagan–Ullah test which is expressed in equation (2.30) and absence of serial correlation in the residual returns. Non-parametric estimates of conditional mean functions, employed later, support this assumption. 12 Throughout this chapter the term ‘empirical distribution’ refers to the Kernel estimators. 13 The Pagan–Ullah test can also be applied to measure the goodness of fit of a conditional covariance model. This stands on regressing the cross product of the two residual series against a constant and the covariance estimates. The unbiasedness hypothesis requires the constant to be zero and the slope to be one. The uncentered coefficient of determination of the regression tells us the forecasting power of the model. Unfortunately, even with daily observations, for most financial time series the coefficient of determination tends to be very low, pointing to the great difficulty in obtaining good covariance estimates. 14 That is, the average volatility of a diversified over the average of an undiversified portfolio. 15 The forecast of the portfolio variance converges to a constant, (uòac2 )/(1ñañb), which is also the mean of the portfolio’s conditional volatility. 16 A second limitation arises if the (stochastic) model that describes portfolio returns restricts portfolio variance to be constant over time. 17 Historical simulation is better known as ‘bootstrapping’ simulation. 18 Note that the empirical distribution has asymmetric tails and is kurtotic. Our methodology ensures that the degree of asymmetry is consistent with the statistical properties of portfolio returns over time.

Measuring volatility 19 20

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The ARCH(1) case requires that 3a2 \1 for the fourth moment to exist. Thus, the assumption that the conditional density is normally distributed usually does not affect the parameter estimates of an ARCH model, even if it is false, see, for example, Engle and Gonzalez-Rivera (1991).

Further reading Barone-Adesi, G., Bourgoin, F. and Giannopoulos, K. (1998) ‘Don’t look back’, Risk, August. Barone-Adesi, G., Giannopoulos, K. and Vosper, L. (1990) ‘VaR without correlations for non-linear portfolios’, Journal of Futures Markets, 19, 583–602. Berndt, E., Hall, B., Hall, R. and Hausman, J. (1974) ‘Estimation and interference in non-linear structured models’, Annals of Economic and Social Measurement, 3, 653–65. Black, M. (1968) ‘Studies in stock price volatility changes’, Proceedings of the 1976 Business Meeting of the Business and Economic Statistics Section, American Statistical Association, 177–81. Bollerslev, T. (1986) ‘Generalised autoregressive conditional heteroskedasticity’, Journal of Econometrics, 31, 307–28. Bollerslev, T. (1988) ‘On the correlation structure of the generalised autoregressive conditional heteroskedastic process’, Journal of Time Series Analysis, 9, 121–31. Christie, A. (1982) ‘The stochastic behaviour of common stock variance: value, leverage and interest rate effects’, Journal of Financial Economics, 10, 407–32. Diebold, F. and Nerlove, M. (1989) ‘The dynamics of exchange rate volatility: a multivariate latent factor ARCH model’, Journal of Applied Econometrics, 4, 1–21. Engle, R. (1982) ‘Autoregressive conditional heteroskedasticity with estimates of the variance in the UK inflation’, Econometrica, 50, 987–1008. Engle, R. and Bollerslev, T. (1986) ‘Modelling the persistence of conditional variances’, Econometric Reviews, 5, 1–50. Engle, R. and Gonzalez-Rivera, G. (1991) ‘Semiparametric ARCH models’, Journal of Business and Economic Statistics, 9, 345–59. Engle, R., Granger, C. and Kraft, D. (1984) ‘Combining competing forecasts of inflation using a bivariate ARCH model’, Journal of Economic Dynamics and Control, 8, 151–65. Engle, R., Lilien, D. and Robins, R. (1987) ‘Estimating the time varying risk premia in the term structure: the ARCH-M model’, Econometrica, 55, 391–407. Engle, R. and Ng, V. (1991) ‘Measuring and testing the impact of news on volatility’, mimeo, University of California, San Diego. Fama, E. (1965) ‘The behaviour of stock market prices’, Journal of Business, 38, 34–105. Glosten, L., Jagannathan, R. and Runkle, D. (1991) ‘Relationship between the expected value and the volatility of the nominal excess return on stocks’, mimeo, Northwestern University. Joyce, J. and Vogel, R. (1970) ‘The uncertainty in risk: is variance unambiguous?’, Journal of Finance, 25, 127–34. Kroner, K., Kneafsey, K. and Classens, S. (1995) ‘Forecasting volatility in commodity markets’, Journal of Forecasting, 14, 77–95. LeBaron, B. (1988) ‘Stock return nonlinearities: comparing tests and finding structure’, mimeo, University of Wisconsin.

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Mandelbrot, B. (1963) ‘The variation of certain speculative prices’, Journal of Business, 36, 394–419. Nelson, D. (1988) The Time Series Behaviour of Stock Market Volatility Returns, PhD thesis, MIT, Economics Department. Nelson, D. (1990) ‘Conditional heteroskedasticity in asset returns: a new approach’, Econometrica, 59, 347–70. Nelson, D. (1992) ‘Filtering and forecasting with misspecified ARCH models: getting the right variance with the wrong model’, Journal of Econometrics, 52. Pagan, A. and Schwert, R. (1990) ‘Alternative models for conditional stock volatility’, Journal of Econometrics, 45, 267–90. Pagan, A. and Ullah, A. (1988) ‘The econometric analysis of models with risk terms’, Journal of Applied Econometrics, 3, 87–105. Rosenberg, B. (1985) ‘Prediction of common stock betas’, Journal of Portfolio Management, 11, Winter, 5–14. Scheinkman, J. and LeBaron, B. (1989) ‘Non-linear dynamics and stock returns’, Journal of Business, 62, 311–37. Zakoian, J.-M. (1990) ‘Threshold heteroskedastic model’, mimeo, INSEE, Paris.

3

The yield curve P. K. SATISH

Introduction Fundamental to any trading and risk management activity is the ability to value future cash flows of an asset. In modern finance the accepted approach to valuation is the discounted cash flows (DCF) methodology. If C(t) is a cash flow occurring t years from today, according to the DCF model, the value of this cash flow today is V0 óC(t)Z(t) where Z(t) is the present value (PV) factor or discount factor. Therefore, to value any asset the necessary information is the cash flows, their payment dates, and the corresponding discount factors to PV these cash flows. The cash flows and their payment dates can be directly obtained from the contract specification but the discount factor requires the knowledge of the yield curve. In this chapter we will discuss the methodology for building the yield curve for the bond market and swap market from prices and rates quoted in the market. The yield curve plays a central role in the pricing, trading and risk management activities of all financial products ranging from cash instruments to exotic structured derivative products. It is a result of the consensus economic views of the market participants. Since the yield curve reflects information about the microeconomic and macroeconomic variables such as liquidity, anticipated inflation rates, market risk premia and expectations on the overall state of the economy, it provides valuable information to all market participants. The rationale for many trades in the financial market are motivated by a trader’s attempt to monetize their views about the future evolution of the yield curve when they differ from that of the market. In the interest rate derivative market, yield curve is important for calibrating interest rate models such as Black, Derman and Toy (1990), Hull and White (1990) and Heath, Jarrow and Morton (1992) and Brace, Gatarek and Musiela (1997) to market prices. The yield curve is built using liquid market instrument with reliable prices. Therefore, we can identify hedges by shocking the yield curve and evaluating the sensitivity of the position to changes in the yield curve. The time series data of yield curve can be fed into volatility estimation models such as GARCH to compute Value-at-Risk (VaR). Strictly speaking, the yield curve describes the term structure of interest rates in any market, i.e. the relationship between the market yield and maturity of instruments with similar credit risk. The market yield curve can be described by a number of alternative but equivalent ways: discount curve, par-coupon curve, zero-coupon

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or spot curve and forward rate curve. Therefore, given the information on any one, any of the other curves can be derived with no additional information. The discount curve reflects the discount factor applicable at different dates in the future and represents the information about the market in the most primitive fashion. This is the most primitive way to represent the yield curve and is primarily used for valuation of cash flows. An example of discount curve for the German bond market based on the closing prices on 29 October 1998 is shown in Figure 3.1. The par, spot, and forward curves that can be derived from the discount curve is useful for developing yield curve trading ideas.

Figure 3.1 DEM government discount curve.

The par-coupon curve reflects the relationship between the yield on a bond issued at par and maturity of the bond. The zero curve or the spot curve, on the other hand, indicates the yield of a zero coupon bond for different maturity. Finally, we can also construct the forward par curve or the forward rate curve. Both these curves show the future evolution of the interest rates as seen from today’s market yield curve. The forward rate curve shows the anticipated market interest rate for a specific tenor at different points in the future while the forward curve presents the evolution of the entire par curve at a future date. Figure 3.2 shows the par, spot, forward curves German government market on 29 October 1998. For example the data point (20y, 5.04) in the 6m forward par curve tell us that the 20-year par yield 6m from the spot is 5.04%. The data point (20y, 7.13) on the 6m forward rate curve indicates that the 6-month yield 20 years from the spot is 7.13%. For comparison, the 6-month yield and the 20-year par yield on spot date is 3.25% and 4.95% respectively. Since discount factor curve forms the fundamental building block for pricing and trading in both the cash and derivative markets we will begin by focusing on the methodology for constructing the discount curve from market data. Armed with the

The yield curve

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Figure 3.2

DEM par-, zero-, and forward yield curves.

knowledge of discount curve we will then devote our attention to developing other representation of market yield curve. The process for building the yield curve can be summarized in Figure 3.3.

Bootstrapping swap curve Market participant also refers to the swap curve as the LIBOR curve. The swap market yield curve is built by splicing together the rates from market instruments that represent the most liquid instruments or dominant instruments in their tenors. At the very short end, the yield curve uses the cash deposit rates, where available the International Money Market (IMM) futures contracts are used for intermediate tenors and finally par swap rates are used for longer tenors. A methodology for building the yield curve from these market rates, referred to as bootstrapping or zero coupon stripping, that is widely used in the industry is discussed in this section. The LIBOR curve can be built using the following combinations of market rates: Ω Cash depositòfuturesòswaps Ω Cash depositòswaps The reason for the popularity of the bootstrapping approach is its ability to produce a no-arbitrage yield curve, meaning that the discount factor obtained from bootstrapping can recover market rates that has been used in their construction. The downside to this approach, as will be seen later, is the fact that the forward rate curve obtained from this process is not a smooth curve. While there exists methodologies to obtain smooth forward curves with the help of various fitting

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Figure 3.3 Yield curve modeling process.

algorithms they are not always preferred as they may violate the no-arbitrage constraint or have unacceptable behavior in risk calculations.

Notations In describing the bootstrapping methodology we will adopt the following notations for convenience: t0 S (T ): Z (T ): a(t1 , t 2 ): F (T1 , T2 ): P (T1, T2 ): f (T1, T2 ): d (T ):

Spot date Par swap rate quote for tenor T at spot date Zero coupon bond price or discount factor maturing on date T at spot date Accrual factor between date t1 and t 2 in accordance to day count convention of the market (ACT/360, 30/360, 30E/360, ACT/ACT) Forward rate between date T1 and T2 as seen from the yield curve at spot date IMM futures contract price deliverable on date T1 at spot date Futures rate, calculated as 100-P (T1 , T2 ) at spot date Money market cash deposit rates for maturity T at spot date

Extracting discount factors from deposit rates The first part of the yield curve is built using the cash deposit rates quoted in the market. The interest on the deposit rate accrue on a simple interest rate basis and

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as such is the simplest instrument to use in generating discount curve. It is calculated using the following fundamental relationship in finance: Present valueófuture valueîdiscount factor The present value is the value of the deposit today and the future value is the amount that will be paid out at the maturity of the deposit. Using our notations we can rewrite this equation as 1ó(1òd(T )a(t 0 , T )îZ(T ) or equivalently, Z(T )ó

1 (1òd(T )a(t 0 , T )

(3.1)

The accrual factor, a(t 0 , T ), is calculated according to money market day count basis for the currency. In most currencies it is Actual/360 or Actual/365. For example, consider the deposit rate data for Germany in Table 3.1. Table 3.1 DEM cash deposit rates data Tenor

Bid

Accrual basis

O/N T/N T/N 1M 2M 3M 6M 9M 12M

3.35 3.38 3.38 3.45 3.56 3.55 3.53 3.44 3.47

Actual/360

The discount factor for 1W is 1

7 1ò3.38% 360

ó0.9993

Similarly, using expression (3.1) we can obtain the discount factor for all other dates as well. The results are shown in Table 3.2. These calculations should be performed after adjusting the maturity of cash rates for weekends and holidays where necessary. In the above calculation the spot date is trade date plus two business days as per the convention for the DEM market and the discount factor for the spot date is defined to be 1. If instead of the spot date we define the discount factor for the trade date to be 1.0 then the above discount factor needs to be rebased using the overnight rate and tomorrow next rate. First, we can calculate the overnight discount factor as: 1

7 1ò3.35% 360

ó0.9999

80

The Professional’s Handbook of Financial Risk Management Table 3.2 DEM cash deposit discount factor curve at spot date Tenor Trade date Spot 1W 1M 2M 3M 6M 9M 12M

Maturity

Accrued days

Discount factor

22-Oct-98 26-Oct-98 02-Nov-98 26-Nov-98 28-Dec-98 26-Jan-99 26-Apr-99 26-Jul-99 26-Oct-99

0 7 31 63 92 182 273 365

1.0000 0.9993 0.9970 0.9938 0.9910 0.9824 0.9745 0.9660

Next, we use the tomorrow next rate to calculate the discount factor for the spot date. The tomorrow next rate is a forward rate between trade day plus one business day to trade date plus two business day. Therefore, the discount factor for the spot date is: 1 ó0.9996 0.9999î 3 1ò3.38% 360

The discount factors to trade date can be obtained by multiplying all the discount factors that has been previously calculated to spot date by 0.9996. This is shown in Table 3.3. Table 3.3 DEM cash deposit discount curve at trade date Tenor Trade Date O/N Spot 1W 1M 2M 3M 6M 9M 12M

Maturity

Accrued days

Discount factor

22-Oct-98 23-Oct-98 26-Oct-98 02-Nov-98 26-Nov-98 28-Dec-98 26-Jan-99 26-Apr-99 26-Jul-99 26-Oct-99

0 1 4 11 35 67 96 186 277 369

1.00000000 0.99990695 0.99962539 0.99896885 0.99666447 0.99343628 0.99063810 0.98209875 0.97421146 0.96565188

Extracting discount factors from futures contracts Next we consider the method for extracting the discount factor from the futures contract. The prices for IMM futures contract reflect the effective interest rate for lending or borrowing 3-month LIBOR for a specific time period in the future. The contracts are quoted on a price basis and are available for the months March, June, September and December. The settlement dates for the contracts vary from exchange to exchange. Typically these contracts settle on the third Wednesday of the month

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81

and their prices reflect the effective future interest rate for a 3-month period from the settlement date. The relationship between the discount factor and the futures rate is given by the expression below. t0

T1

T2 1 [1òf (T1, T2 )a(T1, T2 )

Z(T2 )óZ(T1)

(3.2)

The futures rate is derived from the price of the futures contract as follows: f (T1, T2 )ó

100ñP(T1,T2 ) 100

Thus, with the knowledge of discount factor for date T1 and the interest rate futures contract that spans time period (T1 ,T2 ) we can obtain the discount factor for date T2 . If the next futures contract span (T2 ,T3 ) then we can reapply expression (3.2) and use for Z(T2 ) the discount factor calculated from the previous contract. In general, Z(Ti )óZ(Tiñ1)

1 (1òf (Tiñ1,Ti )a(Tiñ1,Ti )

An issue that arises during implementation is that any two adjacent futures contract may not always adjoin perfectly. This results in gaps along the settlement dates of the futures contract making the direct application of expression (3.2) difficult. Fortunately, this problem can be overcome. A methodology for dealing with gaps in the futures contract is discussed later. Building on the example earlier, consider the data in Table 3.4 for 3-month Euromark futures contract in LIFFE. The settlement date is the third Wednesday of the contract expiration month. We assume that the end date for the 3-month forward period is the settlement date of the next contract, i.e. ignore existence of any gaps. Table 3.4 DEM futures price data Contract

Price

Implied rate (A/360 basis)

Settle date

End date

Accrued days

DEC98 MAR99 JUN99 SEP99 DEC99 MAR00 JUN00 SEP00 DEC00 MAR01 JUN01 SEP01 DEC01 MAR02 JUN02 SEP02

96.5100 96.7150 96.7500 96.7450 96.6200 96.6600 96.5600 96.4400 96.2350 96.1700 96.0800 95.9750 95.8350 95.7750 95.6950 95.6100

3.4900% 3.2850% 3.2500% 3.2550% 3.3800% 3.3400% 3.4400% 3.5600% 3.7650% 3.8300% 3.9200% 4.0250% 4.1650% 4.2250% 4.3050% 4.3900%

16-Dec-98 17-Mar-99 16-Jun-99 15-Sep-99 15-Dec-99 15-Mar-00 21-Jun-00 20-Sep-00 20-Dec-00 21-Mar-01 20-Jun-01 19-Sep-01 19-Dec-01 20-Mar-02 19-Jun-02 18-Sep-02

17-Mar-99 16-Jun-99 15-Sep-99 15-Dec-99 15-Mar-00 21-Jun-00 20-Sep-00 20-Dec-00 21-Mar-01 20-Jun-01 19-Sep-01 19-Dec-01 20-Mar-02 19-Jun-02 18-Sep-02 18-Dec-02

91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91

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The price DEC98 futures contract reflects the interest rate for the 91-day period from 16 December 1998 to 17 March 1999. This can be used to determine the discount factor for 17 March 1999 using expression (3.2). However, to apply expression (3.2) we need the discount factor for 16 December 1998. While there are several approaches to identify the missing discount factor we demonstrate this example by using linear interpolation of the 1-month (26 November 1998) and 2-month (28 December 1998) cash rate. This approach gives us a cash rate of 3.5188% and discount factor for 0.99504 with respect to the spot date. The discount factor for 17 March 1998 is 0.9950

1 91 1ò3.49% 360

ó0.9863

In the absence of any gaps in the futures contract the above discount factor together with the MAR99 contract can be used determine the discount factor for 16 June 1999 and so on until the last contract. The results from these computations are shown in Table 3.5. Table 3.5 DEM discount curve from futures prices Date

Discount factor

26-Oct-98 16-Dec-98

1.00000 0.99504

17-Mar-99 16-Jun-99 15-Sep-99 15-Dec-99 15-Mar-00 21-Jun-00 20-Sep-00 20-Dec-00 21-Mar-01 20-Jun-01 19-Sep-01 19-Dec-01 20-Mar-02 19-Jun-02 18-Sep-02 18-Dec-02

0.98634 0.97822 0.97024 0.96233 0.95417 0.94558 0.93743 0.92907 0.92031 0.91148 0.90254 0.89345 0.88414 0.87480 0.86538 0.85588

Method Spot Interpolated cash rate DEC98 MAR99 JUN99 SEP99 DEC99 MAR00 JUN00 SEP00 DEC00 MAR01 JUN01 SEP01 DEC01 MAR02 JUN02 SEP02

Extracting discount factor from swap rates As we go further away from the spot date we either run out of the futures contract or, as is more often the case, the futures contract become unsuitable due to lack of liquidity. Therefore to generate the yield curve we need to use the next most liquid instrument, i.e. the swap rate. Consider a par swap rate S(t N ) maturing on t N with cash flow dates {t1 , t 2 , . . . t N }.

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83

The cash flow dates may have an annual, semi-annual or quarterly frequency. The relationship between the par swap rate and the discount factor is summarized in the following expression: N

1ñZ(t N )óS(t N ) ; a(t iñ1 , t i )Z(t i )

(3.3)

ió1

The left side of the expression represents the PV of the floating payments and the right side the PV of the fixed rate swap payments. Since a par swap rate by definition has zero net present value, the PV of the fixed and floating cash flows must to be equal. This expression can be rearranged to calculate the discount factor associated with the last swap coupon payment: Nñ1

1ñS(t N ) ; a(tiñ1, t i )Z(t i ) Z(t N )ó

ió1

(3.4)

1òa(t Nñ1 , t N )S(t N )

To apply the above expression we need to know the swap rate and discount factor associated with all but the last payment date. If a swap rate is not available then it has to be interpolated. Similarly, if the discount factors on the swap payment dates are not available then they also have to be interpolated. Let us continue with our example of the DEM LIBOR curve. The par swap rates are given in Table 3.6. In this example all swap rates are quoted in the same frequency and day count basis. However, note that this need not be the case; for example, the frequency of the 1–3y swap rate in Australia dollar is quarterly while the rest are semi-annual. First we combine our cash curve and futures curve as shown in Table 3.7. Notice that all cash discount factors beyond 16 December 1998 have been dropped. This is because we opted to build our yield curve using the first futures contract. Even though the cash discount factors are available beyond 16 December 1998 the futures takes precedence over the cash rates. Since we have already generated discount factor until 18 December 2002 the first relevant swap rate is the 5y rate. Before applying expression (3.3) to bootstrap the

Table 3.6 DEM swap rate data Tenor 2Y 3Y 4Y 5Y 6Y 7Y 8Y 9Y 10Y 12Y 15Y 20Y 30Y

Swap Rate

Maturity

3.4600% 3.6000% 3.7600% 3.9100% 4.0500% 4.1800% 4.2900% 4.4100% 4.4900% 4.6750% 4.8600% 5.0750% 5.2900%

26-Oct-00 26-Oct-01 28-Oct-02 27-Oct-03 26-Oct-04 26-Oct-05 26-Oct-06 26-Oct-07 27-Oct-08 26-Oct-10 28-Oct-13 26-Oct-18 26-Oct-28

Frequency/basis Annual, Annual, Annual, Annual, Annual, Annual, Annual, Annual, Annual, Annual, Annual, Annual, Annual,

30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360 30E/360

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The Professional’s Handbook of Financial Risk Management Table 3.7 DEM cash plus futures discount factor curve Date

Discount factor

26-Oct-98 27-Oct-98 02-Nov-98 26-Nov-98 16-Dec-98

1.00000 0.99991 0.99934 0.99704 0.99504

17-Mar-99 16-Jun-99 15-Sep-99 15-Dec-99 15-Mar-00 21-Jun-00 20-Sep-00 20-Dec-00 21-Mar-01 20-Jun-01 19-Sep-01 19-Dec-01 20-Mar-02 19-Jun-02 18-Sep-02 18-Dec-02

0.98634 0.97822 0.97024 0.96233 0.95417 0.94558 0.93743 0.92907 0.92031 0.91148 0.90254 0.89345 0.88414 0.87480 0.86538 0.85588

Source Spot Cash Cash Cash Interpolated cash Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures Futures

discount factor for 27 October 2003 we have to interpolate the discount factor for all the prior payment dates from the ‘cash plus futures’ curve we have so far. This is shown in Table 3.8 using exponential interpolation for discount factors.

Table 3.8 DEM 5y swap payment date discount factor from exponential interpolation Cash ﬂow dates

Accrual factor

Discount factor

Method

26-Oct-99 26-Oct-00 26-Oct-01 28-Oct-02 27-Oct-03

1.0000 1.0000 1.0000 1.0056 0.9972

0.96665 0.93412 0.89885 0.86122 ?

Exponential interpolation from cashòfutures curve

Therefore, the discount factor for 27 October 2003 is 1ñ3.91%(1.00î0.9665ò1.00î0.93412ò1.00î0.89885ò1.0056î0.86122) 1ò3.91%î0.9972 ó0.82452

The yield curve

85

This procedure can be continued to derive all the discount factors. Each successive swap rate helps us identify the discount factor associated with the swap’s terminal date using all discount factors we know up to that point. When a swap rate is not available, for example the 11y rate, it has to be interpolated from the other available swap rates. The results are shown in Table 3.9 below, where we apply linear interpolation method for unknown swap rates. In many markets it may be that the most actively quoted swap rates are the annual tenor swaps. However, if these are semi-annual quotes then we may have more discount factors to bootstrap than available swap rates. For example, suppose that we have the six-month discount factor, the 1y semi-annual swap rate and 2y semiannual swap rate. To bootstrap the 2y discount factor we need the 18 month discount factor which is unknown: Spot1 Z0

6m Z 6m

1y Z 1y

18y Z 18M

2y Z 2Y

A possible approach to proceed in building the yield curve is to first interpolate (possibly linear interpolation) the 18-month swap rate from the 1y and 2y swap rate. Next, use the interpolated 18-month swap rate to bootstrap the corresponding discount factor and continue onwards to bootstrap the 2y discount factor. Another alternative is to solve numerically for both the discount factors simultaneously. Let G be an interpolation function for discount factors that takes as inputs the dates and adjacent discount factors to return the discount factor for the interpolation date, that is, Z 18m óG(T18m , Z 1y ,Z 2y ) The 2y equilibrium swap is calculated as S2y ó

1ñZ 2y [aspot,6m Z 6m òa6m,1y Z 1y òa1y,18y Z 18m òa18m,2y Z 2y ]

where a’s are the accrual factor according to the day count basis. Substituting the relationship for the 18m discount factor we get S2y ó

1ñZ 2y [aspot,6m Z 6m òa6m,1y Z 1y òa1y,18y G(T18m , Z 1y , Z 2y )òa18m,2y Z 2y ]

The above expression for the 2y swap rate does not require the 18m discount factor as input. We can then use a numerical algorithm such as Newton–Raphson to determine the discount factor for 2y, Z 2y , that will ensure that the equilibrium 2y swap rate equals the market quote for the 2y swap rate. Finally, putting it all together we have the LIBOR discount factor curve for DEM in Table 3.10. These discount factors can be used to generate the par swap curve, forward rate curve, forward swap rate curve or discount factors for pricing various structured products. The forward rate between any two dates T1 and T2 as seen from the yield curve on spot date t 0 is F (T1 , T2 )ó

Z(T1 ) 1 ñ1 Z(T2 ) a(T1 , T2 )

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The Professional’s Handbook of Financial Risk Management Table 3.9 DEM discount factors from swap rates Tenor 5y 6y 7y 8y 9y 10y 11y 12y 13y 14y 15y 16y 17y 18y 19y 20y 21y 22y 23y 24y 25y 26y 27y 28y 29y 30y

Maturity

Swap rate

Accrual factor

Discount factor

27-Oct-03 26-Oct-04 26-Oct-05 26-Oct-06 26-Oct-07 27-Oct-08 26-Oct-09 26-Oct-10 26-Oct-11 26-Oct-12 28-Oct-13 27-Oct-14 26-Oct-15 26-Oct-16 26-Oct-17 26-Oct-18 28-Oct-19 26-Oct-20 26-Oct-21 26-Oct-22 26-Oct-23 28-Oct-24 27-Oct-25 26-Oct-26 26-Oct-27 26-Oct-28

3.9100% 4.0500% 4.1800% 4.2900% 4.4100% 4.4900% 4.5824% 4.6750% 4.7366% 4.7981% 4.8600% 4.9029% 4.9459% 4.9889% 5.0320% 5.0750% 5.0966% 5.1180% 5.1395% 5.1610% 5.1825% 5.2041% 5.2256% 5.2470% 5.2685% 5.2900%

0.9972 0.9972 1.0000 1.0000 1.0000 1.0028 0.9972 1.0000 1.0000 1.0000 1.0056 0.9972 0.9972 1.0000 1.0000 1.0000 1.0056 0.9944 1.0000 1.0000 1.0000 1.0056 0.9972 0.9972 1.0000 1.0000

0.82452 0.78648 0.74834 0.71121 0.67343 0.63875 0.60373 0.56911 0.53796 0.50760 0.47789 0.45122 0.42543 0.40045 0.37634 0.35309 0.33325 0.31445 0.29634 0.27900 0.26240 0.24642 0.23127 0.21677 0.20287 0.18959

This can be calculated from the discount factor curve after applying suitable interpolation method to identify discount factors not already available. For example, using exponential interpolation we find that the discount factor for 26 April 1999 is 0.98271 and that for 26 October 1999 is 0.96665. The forward rate between 26 April 1999 and 26 October 1999 is

0.98271 ñ1 0.96665

1

ó3.27%

183 360

The 6m forward rate curve for DEM is shown in Table 3.11. Forward rate curves are important for pricing and trading a range of products such as swaps, FRAs, Caps and Floors and a variety of structured notes. Similarly, the equilibrium par swap rate and forward swap rates can be calculated from the discount from S(t s , t sòN )ó

Z(t s )ñZ(t sòN ) N

; a(t sòiñ1 , t sòi )Z(t sòi ) ió1

The yield curve

87 Table 3.10 DEM swap or LIBOR bootstrapped discount factor curve

Cash Date 26-Oct-98 27-Oct-98 02-Nov-98 26-Nov-98 16-Dec-98

Futures

Swaps

Discount factor

Date

Discount factor

Date

Discount factor

1.00000 0.99991 0.99934 0.99704 0.99504

17-Mar-99 16-Jun-99 15-Sep-99 15-Dec-99 15-Mar-00 21-Jun-00 20-Sep-00 20-Dec-00 21-Mar-01 20-Jun-01 19-Sep-01 19-Dec-01 20-Mar-02 19-Jun-02 18-Sep-02 18-Dec-02

0.98634 0.97822 0.97024 0.96233 0.95417 0.94558 0.93743 0.92907 0.92031 0.91148 0.90254 0.89345 0.88414 0.87480 0.86538 0.85588

27-Oct-03 26-Oct-04 26-Oct-05 26-Oct-06 26-Oct-07 27-Oct-08 26-Oct-09 26-Oct-10 26-Oct-11 26-Oct-12 28-Oct-13 27-Oct-14 26-Oct-15 26-Oct-16 26-Oct-17 26-Oct-18 28-Oct-19 26-Oct-20 26-Oct-21 26-Oct-22 26-Oct-23 28-Oct-24 27-Oct-25 26-Oct-26 26-Oct-27 26-Oct-28

0.82452 0.78648 0.74834 0.71121 0.67343 0.63875 0.60373 0.56911 0.53796 0.50760 0.47789 0.45122 0.42543 0.40045 0.37634 0.35309 0.33325 0.31445 0.29634 0.27900 0.26240 0.24642 0.23127 0.21677 0.20287 0.18959

S(t s , t sòN ) is the equilibrium swap rate starting at time t s and ending at time t sòN . If we substitute zero for s in the above expression we get the equilibrium par swap rate. Table 3.12 shows the par swap rates and forward swap rates from our discount curve. For comparison we also provide the market swap rates. Notice that the market swap rate and the equilibrium swap rate computed from the bootstrapped does not match until the 5y swaps. This is due to the fact that we have used the futures contract to build our curve until 18 December 2002. The fact that the equilibrium swap rates are consistently higher than the market swap rates during the first 4 years is not surprising since we have used the futures contract without convexity adjustments (see below).

Curve stitching Cash rates and futures contracts In building the yield curve we need to switch from the use of cash deposit rates at the near end of the curve to the use of futures rates further along the curve. The choice of the splice date when cash deposit rate is dropped and futures rate is picked up is driven by the trader’s preference, which in turn depends on the instruments

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The Professional’s Handbook of Financial Risk Management Table 3.11 DEM 6 month forward rates from discount factor curve Date

Discount factor

Accrual factor

6m forward rate

26-Oct-98 26-Apr-99 26-Oct-99 26-Apr-00 26-Oct-00 26-Apr-01 26-Oct-01 26-Apr-02 26-Oct-02 26-Apr-03 26-Oct-03 26-Apr-04 26-Oct-04 26-Apr-05 26-Oct-05 26-Apr-06 26-Oct-06 26-Apr-07 26-Oct-07 26-Apr-08 26-Oct-08 26-Apr-09

1.00000 0.98271 0.96665 0.95048 0.93412 0.91683 0.89885 0.88035 0.86142 0.84300 0.82463 0.80562 0.78648 0.76749 0.74834 0.72981 0.71121 0.69235 0.67343 0.65605 0.63884 0.62125

0.5083 0.5083 0.5083 0.5056 0.5083 0.5056 0.5083 0.5056 0.5083 0.5083 0.5083 0.5056 0.5083 0.5056 0.5083 0.5056 0.5083 0.5083 0.5083 0.5056

3.27% 3.35% 3.44% 3.73% 3.93% 4.16% 4.32% 4.32% 4.38% 4.64% 4.79% 4.89% 5.03% 5.02% 5.14% 5.39% 5.52% 5.21% 5.30% 5.60%

Table 3.12 DEM equilibrium swap rates from discount factor curve Tenor

Market swap rate

2y 3y 4y 5y 6y 7y 8y 9y 10y

3.4600% 3.6000% 3.7600% 3.9100% 4.0500% 4.1800% 4.2900% 4.4100% 4.4900%

Equilibrium swap 6m forward start rate swap rate 3.4658% 3.6128% 3.7861% 3.9100% 4.0500% 4.1800% 4.2900% 4.4100% 4.4900%

3.5282% 3.7252% 3.8917% 4.0281% 4.1678% 4.2911% 4.4088% 4.5110% 4.5970%

that will be used to hedge the position. However, for the methodology to work it is necessary that the settlement date of the first futures contract (referred to as a ‘stub’) lie before the maturity date of the last cash deposit rate. If both cash deposit rate and futures rates are available during any time period then the futures price takes precedence over cash deposit rates. Once the futures contracts have been identified all discount factors until the last futures contract is calculated using the bootstrapping procedure. To bootstrap the curve using the futures contract, the discount factor corresponding to the first

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89

futures delivery date (or the ‘stub’) is necessary information. Clearly, the delivery date of the first futures contract selected may not exactly match the maturity date of one of the cash deposit rates used in the construction. Therefore, an important issue in the curve building is the method for identifying discount factor corresponding to the first futures contract or the stub rate. The discount factor for all subsequent dates on the yield curve will be affected by the stub rate. Hence the choice of the method for interpolating the stub rate can have a significant impact on the final yield curve. There are many alternative approaches to tackle this problem, all of which involves either interpolation method or fitting algorithm. We will consider a few based on the example depicted in Figure 3.4.

Figure 3.4 Cash-futures stitching.

The first futures contract settles on date T1 and spans from T1 to T2 . One alternative is to interpolate the discount factor for date T1 with the 3m and 6m discount factor calculated from expression (3.1). The second alternative is to directly interpolate the 3m and 6m cash deposit rates to obtain the stub rate and use this rate to compute the discount factor using expression (3.1). The impact of applying different interpolation method on the stub is presented in Table 3.13. Table 3.13 DEM stub rate from different interpolation methods Data Spot date: 26-Oct-98 1m (26-Nov-99): 3.45% 2m (28-Dec-98): 3.56% Basis: Act/360 Stub: 16-Dec-98

Interpolation method

Discount factor

Cash rate (stub rate)

Linear cash rate Exponential DF Geometric DF Linear DF

0.99504 0.99504 0.99502 0.99502

3.5188% 3.5187% 3.5341% 3.5332%

The exponential interpolation of discount factor and linear interpolation of the cash rate provide similar results. This is not surprising since exponential interpolation of discount factor differs from the linear interpolation of rate in that it performs a linear interpolation on the equivalent continuously compounded yield.

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To see the impact of the stitching method on the forward around the stub date we have shown the 1m forward rate surrounding the stub date in Figure 3.5 using different interpolation methods. Also, notice that the forward rate obtained from the bootstrapping approach is not smooth.

Figure 3.5 Six-month forward rate from different interpolation.

Going back to Figure 3.4, once the 3m and 6m rate has been used for interpolating the discount factor for T1 all cash rates beyond the 3m is dropped. The yield curve point after the 3m is T2 and all subsequent yield curve point follow the futures contract (i.e. 3 months apart). Since the 6m has been dropped, the 6m cash rate interpolated from the constructed yield curve may not match the 6m cash rate that was initially used. This immediately raises two issues. First, what must be the procedure for discounting any cash flow that occurs between T1 and 6m? Second, what is the implication of applying different methods of interpolation of the discount curve on the interpolated value of the 6m cash rate versus the market cash rate. Further, is it possible to ensure that the interpolated 6m cash rate match the market quoted 6m cash rate? If recovering the correct cash rate from the yield curve points is an important criterion then the methodologies discussed earlier would not be appropriate. A possible way to handle this issue is to change the method for obtaining the stub rate by directly solving for it. We can solve for a stub rate such that 6m rate interpolated from the stub discount factor for T1 and discount factor for T2 match the market. In some markets such as the USD the short-term swap dealers and active cash dealers openly quote the stub rate. If so then it is always preferable to use the market-quoted stub rate and avoid any interpolation.

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91

Futures strip and swap rates To extend the curve beyond the last futures contract we need the swap rates. The required swap rate may be available as input data or may need to be interpolated. Consider the following illustration where the last futures contract ends on TLF . If S(T2 ), the swap rate that follows the futures contract, is available as input then we can apply expression (3.3) to derive the discount factor for date T2 . This is a straightforward case that is likely to occur in currencies such as DEM with annual payment frequency:

t0

S(T1 )

TLF

S(T2 )

The discount factor corresponding to all payment dates except the last will need to be interpolated. In the next scenario depicted below suppose that swap rate S(T2 ) is not available from the market:

t0

S(T1 )

TLF

S(T2 )

S(T3 )

We have two choices. Since we have market swap rate, S(T1 ) and S(T3 ), we could use these rates to interpolate S(T2 ). Alternatively, since we have discount factor until date TLF we can use them to calculate a equilibrium swap rate, [email protected](T1 ), for tenor T1 . The equilibrium swap rate [email protected](T1 ) and market swap rate S(T3 ) can be used to interpolate the missing swap rate S(T2 ). In some circumstances we may have to interpolate swap rates with a different basis and frequency from the market-quoted rates. In these cases we recommend that the market swap rates be adjusted to the same basis and frequency as the rate we are attempting to interpolate. For example, to get a 2.5y semi-annual, 30E/360 swap rate from the 2y and 3y annual, 30E/360 swap rate, the annual rates can be converted to an equivalent semi-annual rate as follows: Ssemi-annual ó2î[(1òSannual )1/2 ñ1]

Handling futures gaps and overlaps In the construction of the discount factor using expression (3.2), we are implicitly assuming that all futures contract are contiguous with no gaps or overlaps. However, from time to time due to holidays it is possible that the contracts do not line up exactly. Consider the illustration below:

T1 , Z 1

T2 , Z 2

[email protected] , Z [email protected]

T3 , Z 3

The first futures contract span from T1 to T2 while the next futures contract span from [email protected] to T3 resulting in a gap. An approach to resolve this issue is as follows. Define G(T, Z a , Z b ) to be a function representing the interpolation method (e.g. exponential)

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for discount factor. We can apply this to interpolate Z 2 as follows: Z [email protected]óG ([email protected] , Z 2 , Z 3 )

(3.4)

From expression (3.2) we also know that Z 3 óZ [email protected]

1 •(( f, Z [email protected] ) [1òf ([email protected] , T3 )a([email protected] , T3 )]

(3.5)

Combining expressions (3.4) and (3.5) we have Z 3 ó(( f, G([email protected] , Z 2 , Z 3 ))

(3.6)

All variables in expression (3.6) are known except Z 3 . This procedure can be applied to find the discount factor Z 3 without the knowledge of Z [email protected] caused by gaps in the futures contract. For example, if we adopt exponential interpolation ([email protected] ñt 0 )

j

([email protected] ñt 0 )

Z [email protected] óG([email protected] , Z 2 , Z 3 )óZ 2 (T2 ñt 0 ) Z (T3 ñt 0 )

(1ñj)

and jó

(T3 ñT2 ) (T3 ñT2 )

Therefore, ([email protected] ñt 0 )

j

([email protected] ñt 0 )

Z 3 óZ 2 (T2 ñt 0 ) Z (T3 ñt 0 )

(1ñj)

1 [1òf ([email protected] , T3 )a([email protected] , T3 )]

This can be solved analytically for Z 3 . Specifically,

ln Z 2

Z 3 óExp

([email protected] ñt 0 ) j (T2 ñt 0 )

1 [1òf ([email protected] , T3 )a([email protected] , T3 )]

(T ñt 0 ) (1ñj) 1ñ [email protected] (T3 ñt 0 )

(3.7)

As an example consider an extreme case where one of the futures price is entirely missing. Suppose that we know the discount factor for 16 June 1999 and the price of SEP99 futures contract. The JUN99 contract is missing. Normally we would have used the JUN99 contract to derive the discount factor for 15 September 1999 and then use the SEP99 contract to obtain the discount factor for 15 December 1999 (Table 3.14). Table 3.14 Contract

Price

Implied rate (A/360 basis)

Settle date

End date

Discount factor

16-Jun-99 15-Sep-99

26-Oct-98 (t0 ) 16-Jun-99 (T2 ) 15-Sep-99 ([email protected] ) 15-Dec-99 (T3 )

1.0000 0.9782 N/A ?

Spot JUN99 SEP99

Missing 96.7450

Missing 3.2550%

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In this example we can apply expression (3.7) to obtain the discount factor for 15 December 1999:

0.8877

ln Z 20.6384

Z 15-Dec-99 óexp

î0.5

1ñ

1 [1ò3.255%î91/360]

0.8877 (1ñ0.5) 1.1370

ó0.96218

Earlier when we had the price for the JUN99 contract the discount factor for 15 December1999 was found to be 0.96233. Solution to overlaps are easier. If the futures contracts overlap (i.e. [email protected] \T2 ) then the interpolation method can be applied to identify the discount factor Z [email protected] corresponding to the start of the next futures contract.

Futures convexity adjustment Both futures and FRA are contracts written on the same underlying rate. At the expiration of the futures contract, the futures rate and the forward rate will both be equal to the then prevailing spot LIBOR rate. However, these two instruments differ fundamentally in the way they are settled. The futures contracts are settled daily whereas the FRAs are settled at maturity. As a result of this difference in the settlement procedure the daily changes in the value of a position in futures contract and that of a position in FRA to anticipated changes in the future LIBOR rate are not similar. The futures contract react linearly to changes in the future LIBOR rate while the FRA reacts non-linearly. This convexity effect creates an asymmetry in the gains/ losses between being long or short in FRA and hedging them with futures contracts. To be more precise, there is an advantage to being consistently short FRA and hedging them with short futures contracts. This is recognized by the market and reflected in the market price of the futures contract. The convexity effect implies that the forward rate obtained from the futures price will be high. Since the futures rate and forward rate converge as we approach the maturity date, the futures rate must drift downwards. Hence while building the LIBOR yield curve it is important that the forward rates implied from the futures price be adjusted (downwards) by the drift. In most markets the drift adjustments tend to be fairly small for futures contracts that expire within one year from the spot date, but can get progressively larger beyond a year. Consider an N futures contract for periods (t i , t iò1 ), ió1, 2, . . . N and (t iò1 ñt i )ó*t. A simple method to calculate the drift adjustments for the kth futures contract is given below:1 k

kk ó ; f (t i , t iò1 )of Z pf (ti ,tiò1) pZ(tiò1) *t ió1

where f (t i , t iò1 ) is the futures rate for the period t i to t iò1 , pZ(tiò1) is the volatility of the zero coupon bond maturing on t iò1 , pf (ti ,tiò1) is the volatility of the forward rate for the corresponding period and of Z is the correlation between the relevant forward rate and zero coupon bond price. The kth forward rate can be calculated from the futures contract as follows. F(t k , t kò1 )óf (t k , t kò1 )òkk

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Since the correlation between the forward rate and the zero coupon price is expected to be negative the convexity adjustment would result in the futures rate being adjusted downwards. We demonstrate the calculations for the convexity adjustment in the table below. Table 3.15

Contract

Date

Futures rate (1)

SPOT DEC98 MAR99 JUN99 SEP99 DEC99 MAR99 JUN00

26-Oct-98 16-Dec-98 17-Mar-99 16-Jun-99 15-Sep-99 15-Dec-99 15-Mar-00 21-Jun-00

3.49% 3.29% 3.25% 3.26% 3.38% 3.34%

Futures Zero rate coupon volatility volatility (2) (3) 5% 12% 14% 18% 20% 15%

0.25% 0.75% 1.25% 1.50% 2.00% 2.25%

Correlation n (4)

*t (5)

ñ0.99458 ñ0.98782 ñ0.97605 ñ0.96468 ñ0.95370 ñ0.94228

0.25 0.25 0.25 0.25 0.25 0.27

Drift Cumula(bp)ó(1) tive drift or Convexity î(2)î(3) convexity adjusted î(4)î(5) bias (bp) futures rate (6) (7) (8)ó(1)ò(7) ñ0.0108 ñ0.0728 ñ0.1383 ñ0.2112 ñ0.3212 ñ0.2850

ñ0.01 ñ0.08 ñ0.22 ñ0.43 ñ0.75 ñ1.04

3.49% 3.28% 3.25% 3.25% 3.37% 3.33%

Typically the convexity bias is less that 1 basis point for contracts settling within a year from the spot. Between 1 year and 2 years the bias may range from 1 basis point to 4 basis point. For contracts settling beyond 2 years the bias may be as high as 20 basis point – an adjustment that can no longer be ignored.

Interpolation The choice of interpolation algorithm plays a significant role in the process of building the yield curve for a number of reasons: First, since the rates for some of the tenors is not available due to lack of liquidity (for example, the 13-year swap rate) these missing rates need to be determined using some form of interpolation algorithm. Second, for the purposes of pricing and trading various instruments one needs the discount factor for any cash flow dates in the future. However, the bootstrapping methodology, by construction, produces the discount factor for specific maturity dates based on the tenor of the interest rates used in the construction process. Therefore, the discount factor for other dates in the future may need to be identified by adopting some interpolation algorithm. Finally, as discussed earlier, the discount factor corresponding to the stub will most of the time require application of interpolation algorithm. Similarly, the futures and swap rates may also need to be joined together with the help of interpolation algorithm. The choice of the interpolation algorithm is driven by the requirement to balance the need to control artificial risk spillage (an important issue for hedging purposes) against the smoothness of the forward curve (an important issue in the pricing of exotic interest rate derivatives). Discount factor interpolation Consider the example described below:

t0

Z 1 , T1

Z s , Ts

Z 2 , T2

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(a) Linear interpolation The linear interpolation of discount factor for date Ts is obtained by fitting a straight line between the two adjacent dates T1 and T2 . According to linear interpolation, the discount factor for date Ts is: Z s óZ 1 ò

Z 2 ñZ 1 (Ts ñT1) T2 ñT1

or Zsó

T2 ñTs T ñT1 Z1ò s Z2 T2 ñT1 T2 ñT1

Linear interpolation of the discount factor is almost never due to the non-linear shape of the discount curve, but the error from applying it is likely to be low in the short end where there are more points in the curve. (b) Geometric (log-linear) Interpolation The geometric or log linear interpolation of discount factor for date Ts is obtained by applying a natural logarithm transformation to the discount factor function and then performing a linear interpolation on the transformed function. To recover the interpolated discount factor we take the exponent of the interpolated value as shown below: ln(Z s )ó

T2 ñTs T ñT1 ln(Z 1)ò s ln(Z 2 ) T2 ñT1 T2 ñT1

or Z sóexp or

T2 ñTs T ñT1 ln(Z 1)ò s ln(Z 2 ) T2 ñT1 T2 ñT1

Z óZ s

1

T2 ñTs T2 ñT1

Z 2

Ts ñT1 T2 ñT1

(c) Exponential interpolation The continuously compounded yield can be calculated from the discount factor as follows: y1 óñ

1 ln(Z 1 ) (T1 ñt 0 )

and y2 óñ

1 ln(Z 2 ) (T2 ñt 0 )

To calculate the exponential interpolated discount factor we first perform a linear interpolation of the continuously compounded yields as follows: ys óy1

(T2 ñTs ) (T ñT1) òy2 s (T2 ñT1) (T2 ñT1)

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Next we can substitute for yield y1 and y2 to obtain: ys óñ

1 1 ln(Z 1 )jñ ln(Z 2 )(1ñj) (T1 ñt 0 ) (T2 ñt 0 )

where jó

(T2 ñTs ) (T2 ñT1)

The exponentially interpolated value for date Ts is Z s óexp(ñys (Ts ñt 0 ) or (Ts ñt 0 )

j

(Ts ñt 0 )

Z s óZ 1(T1 ñt 0 ) Z 2(T2 ñt 0 )

(1ñj)

Interpolation example Consider the problem of finding the discount factor for 26 February 1999 using the data in Table 3.16. Table 3.16 Discount factor interpolation data Days to spot

Discount factor

0 92 123 182

1.00000 0.99101 ? 0.98247

Date 26-Oct-98 26-Jan-99 26-Feb-99 26-Apr-99

Linear interpolation: Z 26-Feb-99 ó

182ñ123 123ñ92 0.99101ò 0.98247 182ñ92 182ñ92

ó0.98806 Geometric interpolation: Z 26-Feb-99 ó0.99101

0.98247

182ñ123 182ñ92

ó0.98805 Exponential interpolation: jó

(182ñ123) (182ñ92)

ó0.6555

123ñ92 182ñ92

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97 (123ñ0)

Z s ó0.99101 (92ñ0)

0.6555

(123ñ0)

0.98247 (182ñ0)

(1ñ0.6555)

ó0.988039 (d) Cubic interpolation Let {tót 0 , t1 , t 2 , . . . t n óT} be a vector of yield curve point dates and Zó{Z 0 , Z 1 , Z 2 , . . . Z n } be the corresponding discount factors obtained from the bootstrapping process. Define Z i óa i òbi t i òci t i2 òd i t i3 to be a cubic function defined over the interval [t i , t iò1 ]. A cubic spline function is a number of cubic functions joined together smoothly at a number of knot points. If the yield curve points {t, t1 , t 2 , . . .T } are defined to be knot points, then coefficients of the cubic spline function defined over the interval [t,T ] can be obtained by imposing the following constraints:

Z i óa i òbi t i òci t i2 òd i t i3 2 3 òd i t iò1 Z iò1 óa i òbi t iò1 òci t iò1 bi ò2ci t i ò3d i t i2 óbiò1 ò2ciò1 t i ò3diò1 t i2 2ci ò6d i t i ó2ciò1 ò6diò1 t i

ió0 to nñ1; n constraints ió0 to nñ1; n constraints ió0 to nñ2; nñ2 constraints ió0 to nñ2; nñ2 constraints

The first sets of n constraints imply that the spline function fit the knot points exactly. The second sets of n constraints require that the spline function join perfectly at the knot point. The third and the fourth sets of constraints ensure that the first and second derivatives match at the knot point. We have a 4n coefficient to estimate and 4n-2 equation so far. The two additional constraints are specified in the form of end point constraints. In the case of natural cubic spline these are that the second derivative equals zero at the two end points, i.e. 2ci ò6d i t i ó0

ió0 and n

The spline function has the advantage of providing a very smooth curve. In Figure 3.6 we present the discount factor and 3-month forward rate derived from exponential and cubic interpolation. Although the discount curves in both interpolation seem similar; comparison of the 3-month forward rate provides a clearer picture of the impact of interpolation technique. The cubic spline produces a smoother forward curve. Swap rate interpolation As in the discount curve interpolation, the swap rate for missing tenor can be interpolated using the methods discussed earlier for the discount factor. The exponential or geometric interpolation is not an appropriate choice for swap rate. Of the remaining methods linear interpolation is the most popular. In Figure 3.7 we compare the swap rate interpolated from linear and cubic splines for GBP. The difference between the rate interpolated by linear and cubic spline ranges from ò0.15 bp to ñ0.25 basis points. Compared to the swap rate from linear interpolation, the rate from cubic spline more often higher, particularly between 20y and 30y tenors. Unfortunately, the advantage of the smooth swap rate curve from the cubic spline is overshadowed by the high level of sensitivity exhibited by the

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Figure 3.6 Forward rate and discount factor from cubic spline and exponential interpolation.

Figure 3.7 Linear and cubic spline swap rate interpolation.

method to knot point data. This can give rise to artificial volatility with significant implications for risk calculations. Figure 3.8 shows the changes in the interpolated swap rate (DEM) for all tenors corresponding to a 1 basis point change in the one of the swap tenors. A 1 basis point change in one swap rate can change the interpolated swap rates for all tenors

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99

irrespective of their maturities! That is, all else being equal, the effects of a small change in one swap rate is not localized. For example, a 1 bp shift in the 2y swap rate results in a ñ0.07 bp shift in the 3.5y swap rate and a 1 bp shift in the 5y swap rate results in a ñ0.13 bp change in the 3.5y swap rate. This is an undesirable property of cubic spline interpolation, and therefore not preferred in the market.

Figure 3.8 Sensitivity of cubic spline interpolation.

Figure 3.9 displays the implications of applying linear interpolation. The interpolation method, while not smooth like the cubic spline, does keep the impact of a small change in any swap rate localized. The effect on swap rates outside the relevant segment is always zero. This property is preferred for hedge calculations.

Figure 3.9 Sensitivity of linear interpolation.

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Government Bond Curve The bond market differs from the swap market in that the instruments vary widely in their coupon levels, payment dates and maturity. While in principle it is possible to follow the swap curve logic and bootstrap the discount factor, this approach is not recommended. Often the motivation for yield curve construction is to identify bonds that are trading rich or cheap by comparing them against the yield curve. Alternatively, one may be attempting to develop a time series data of yield curve for use in econometric modeling of interest rates. Market factors such as liquidity effect and coupon effect introduce noise that makes direct application of market yields unsuitable for empirical modeling. In either application the bootstrapping approach that is oriented towards guaranteeing the recovery of market prices will not satisfy our objective. Therefore the yield curve is built by applying statistical techniques to market data on bond price that obtains a smooth curve. Yield curve models can be distinguished based on those that fit market yield and those that fit prices. Models that fit yields specify a functional form for the yield curve and estimate the coefficient of the functions using market data. The estimation procedure fits the functional form to market data so as to minimize the sum of squared errors between the observed yield and the fitted yield. Such an approach while easy to implement is not theoretically sound. The fundamental deficiency in this approach is that it does not constrain the cash flows occurring on the same date to be discounted at the same rate. Models that fit prices approach the problem by specifying a functional form for the discount factor and estimate the coefficient using statistical methods. Among the models that fit prices there is also a class of models that treat forward rate as the fundamental variable and derive the implied discount function. This discount function is estimated using the market price data. In this section we limit our discussion to the later approach that was pioneered by McCulloch (1971). This approach is well accepted, although there is no agreement among the practitioners on the choice of the functional form for discount factor. There is a large volume of financial literature that describes the many ways in which this can be implemented. The discussions have been limited to a few approaches to provide the reader with an intuition into the methodology. A more comprehensive discussion on this topic can be found in papers listed in the References.

Parametric approaches The dirty price of a bond is simply the present value of its future cash flows. The dirty price of a bond with N coupon payments and no embedded options can be expressed as: N

P(TN )òA(TN )ó ; cN Z(Ti )òFN Z(TN )

(3.8)

ió1

where P(TN ) is the clean price of a bond on spot date t 0 and maturing on TN , A(TN ) is its accrued interest, cN is the coupon payment on date t, Z(Ti ) is the discount factor at date t and FN is the face value or redemption payment of the bond. The process of building the yield curve hinges on identifying the discount factors corresponding to the payment dates. In the swap market we obtained this from bootstrapping the cash, futures and swap rates. In contrast, in the bond market we assume it to be

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101

one of the many functions available in our library of mathematical function and then estimate the function to fit the data. While implementing this approach two factors must be kept in mind. First, the discount factor function selected to represent the present value factor at different dates in the future must be robust enough to fit any shape for the yield curve. Second, it must satisfy certain reasonable boundary conditions and characteristics. The discount curve must be positive monotonically non-increasing to avoid negative forward rates. Mathematically, we can state these conditions as (a) Z(0)ó1 (b) Z(ê)ó0 (c) Z(Ti )[Z(Tió1 ) Conditions (a) and (b) are boundary conditions on present value factors based upon fundamental finance principles. Condition (c) ensures that the discount curve is strictly downward sloping and thus that the forward rates are positive. A mathematically convenient choice is to represent the discount factor for any date in the future as a linear combination of k basis functions: k

Z(Ti )ó1ò ; a i fi (Ti ) jó1

where fj (t) is the jth basis function and a j is the corresponding coefficient. The basis function can take a number of forms provided they produce sensible discount function. Substituting equation (3.9) into (3.8) we get N

P(TN )òA(TN )ócN ; ió1

k

k

1ò ; a j f (Ti ) òFN 1ò ; a j f (TN ) jó1

jó1

(3.10)

This can be further simplified as k

N

P(TN )òA(TN )ñNcN ñFN ó ; a j cN ; f (Ti )óFN f (TN ) jó1

ió1

(3.11)

Equivalently, k

yN ó ; a j xN

(3.12)

jó1

where yN óP(TN )òA(TN )ñNcN ñFN

(3.13)

N

xN ócN ; f (Ti )òFN f (TN )

(3.14)

ió1

If we have a sample of N bonds the coefficient of the basis function can be estimated using ordinary least squares regression. The estimated discount function can be used to generate the discount factors for various tenors and the yield curves. McCulloch (1971) modeled the basis function as f (T )óT j for jó1, 2, . . . k. This results in the discount function being approximated as a kth degree polynomial. One of the problems with this approach is that it has uniform resolution power. This is

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not a problem if the observations are uniformly distributed across the maturities. Otherwise, it fits well wherever there is greatest concentration of observations and poorly elsewhere. Increasing the order of the polynomial while solving one problem can give rise to another problem of unstable parameters. Another alternative suggested by McCulloch (1971) is to use splines. A polynomial spline is a number of polynomial functions joined together smoothly at a number of knot points. McCulloch (1971,1975) shows the results from applying quadratic spline and cubic spline functions. The basis functions are represented as a family of quadratic or cubic functions that are constrained to be smooth around the knot points. Schaefer (1981) suggested the use of Bernstein polynomial with the constraint that discount factor at time zero is 1. A major limitation of these methods is that the forwards rates derived from the estimated discount factors have undesirable properties at long maturity. Vasicek and Fong (1982) model the discount function as an exponential function and describe an approach that produces asymptotically flat forward curve. This approach is in line with equilibrium interest rate models such as Vasicek (1977) and Hull and White (1990) that show that zero coupon bond price or the discount factor to have exponential form. Rather than modeling the discount curve Nelson and Siegel (1987) directly model the forward rate. They suggest the following functional form for the forward rate:

F(t)ób0 òb1 exp ñ

t òb2 a1

t t exp ñ a1 a1

(3.15)

This implies the following discount curve:

Z(t)óexp ñt b0 ò(b1 òb2 ) 1ñexp ñ

t a1

t a1 ñb2 exp ñ t a1

(3.16)

Coleman, Fisher and Ibbotson (1992) also model the forward rates instead of the discount curve. They propose instantaneous forward rate to be a piecewise constant function. Partitioning the future dates into N segments, {t 0 , t1 , t 2 , . . . t N }, their model define the forward rate in any segment to be t iñ1 \tOt i

F(t)óji

(3.17)

This model implies that the discount factor at date t between t kñ1 and t k is

kñ1

Z(t)óexp ñ j1t 1 ò ; ji (t i ñt iñ1 )ójk (tñt kñ1 ) ió2

(3.18)

The discount curve produced by this model will be continuous but the forward rate curve will not be smooth. Chambers, Carleton and Waldman (1984) propose an exponential polynomial for the discount curve. The exponential polynomial function for discount factor can be written as

k

Z(t)óexp ñ ; a j m j jó1

(3.19)

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103

They recommend that a polynomial of degree 3 or 4 is sufficient to model the yield curve. Finally, Wiseman (1994) model the forward curve as an exponential function k

F(t)ó ; a j e ñkj t

(3.20)

jó0

Exponential model To see how the statistical approaches can be implemented consider a simplified example where the discount curve is modeled as a linear combination of m basis functions. Each basis function is assumed to be an exponential function. More specifically, define the discount function to be: m

Z(t)ó ; a k (e ñbt )k

(3.21)

kó1

where a and b are unknown coefficients of the function that need to be estimated. Once these parameters are known we can obtain a theoretical discount factor at any future date. These discount factors can be used to determine the par, spot and forward curves. Substituting the condition that discount factor must be 1 at time zero we obtain the following constraint on the a coefficients: m

; ak ó1

(3.22)

kó1

We can rearrange this as mñ1

am ó1ñ ; ak

(3.23)

kó1

Suppose that we have a sample of N bonds. Let P(Ti ), ió1, 2, . . . N, be the market price of the ith bond maturing Ti years from today. If qi is the time when the next coupon will be paid, according to this model the dirty price of this bond can be expressed as: Ti

P(Ti )òA(Ti )ó ; cj Z(t)ò100Z(Ti )òei tóqi Ti

ó ; ci tóqi

m

; a k e ñkbt ò100

kó1

m

(3.24)

; ak e ñkbTi òei

kó1

One reason for specifying the price in terms of discount factors is that price of a bond in linear in discount factor, while it is non-linear in either forward or spot rates. We can simplify equation (3.24) further and write it as: mñ1

Ti

P(Ti )òA(Ti )ó ; a k ci ; e ñkbt ò100e ñkbTi kó1

tóqi

mñ1

ò 1ñ ; ak kó1

Ti

(3.25)

ci ; e ñmbt ò100e ñmbTi òei tóqi

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Rearranging, we get

Ti

P(Ti )òA(Ti )ñ ci ; e ñmbt ò100e ñmbTi Ti

ó ; ak tóqi

tóqi

Ti

(3.26)

Ti

ci ; e ñkbt ò100e ñkbTi ñ ci ; e ñmbt ò100e ñmbTi tóqi

tóqi

òei

or mñ1

yi ó ; ak xi , k òei

(3.27)

kó1

where

Ti

zi , k ó ci ; e ñkbt ò100e ñkbTi tóqi

yi óP(Ti )òA(Ti )ñzi , m

(3.28)

xi , k ózi , k ñzi , m To empirically estimate the discount function we first calculate yi and xi,k for each of the N bonds in our sample. The coefficient of the discount function must be selected such that they price the N bonds correctly or at least with minimum error. If we can set the b to be some sensible value then the a’s can be estimated using the ordinary least squares regression.

y1 x1,1 y2 x2,1 ó · · yN xN,1

x1,2 · · ·

· x1,m · · · · · xN,m

aˆ1 e1 aˆ2 e2 ò · · aˆm eN

(3.29)

The aˆ ’s estimated from the ordinary least squares provide the best fit for the data by minimizing the sum of the square of the errors, &Nió1 ei2 . The estimated values of aˆ and b can be substituted into (3.21) to determine the bond market yield curve. The model is sensitive to the number of basis functions therefore it should be carefully selected so as not to over-fit the data. Also, most of the models discussed are very sensitive to the data. Therefore, it is important to implement screening procedures to identify bonds and exclude any bonds that are outliers. Typically one tends to exclude bonds with unreliable prices or bonds that due to liquidity, coupon or tax reasons is expected to have be unusually rich or cheap. A better fit can also be achieved by iterative least squares. The model can be extend in several ways to obtain a better fit to the market data such as imposing constraint to fit certain data point exactly or assuming that the model is homoscedastic in yields and applying generalized least squares.

Exponential model implementation We now present the results from implementing the exponential model. The price data for a sample of German bond issues maturing less than 10 years and settling on 28 October 1998 is reported in Table 3.17.

The yield curve

105

Table 3.17 DEM government bond price data

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Issue

Price

Yield

Accrued

Coupon

Maturity (years)

TOBL5 12/98 BKO3.75 3/99 DBR7 4/99 DBR7 10/99 TOBL7 11/99 BKO4.25 12/99 BKO4 3/0 DBR8.75 5/0 BKO4 6/0 DBR8.75 7/0 DBR9 10/0 OBL 118 OBL 121 OBL 122 OBL 123 OBL 124 THA7.75 10/2 OBL 125 THA7.375 12/2 OBL 126 DBR6.75 7/4 DBR6.5 10/5 DBR6 1/6 DBR6 2/6 DBR6 1/7 DBR6 7/7 DBR5.25 1/8 DBR4.75 7/8

100.21 100.13 101.68 103.41 103.74 100.94 100.81 108.02 100.95 108.81 110.56 104.05 103.41 102.80 102.94 103.06 114.81 104.99 113.78 103.35 114.74 114.85 111.75 111.91 112.05 112.43 107.96 104.81

3.3073 3.3376 3.3073 3.3916 3.3907 3.3851 3.3802 3.3840 3.3825 3.3963 3.3881 3.3920 3.5530 3.5840 3.5977 3.6218 3.6309 3.6480 3.6846 3.6409 3.8243 4.0119 4.0778 4.0768 4.2250 4.2542 4.1860 4.1345

4.3194 2.2813 3.6556 0.1556 6.4750 3.6715 2.4556 3.7917 1.4667 2.3819 0.2000 3.6021 4.4597 3.0750 2.0125 0.8625 0.5813 4.8056 6.6785 3.1250 1.9313 0.2528 4.8833 4.2000 4.9000 1.9000 4.2875 1.5042

5 3.75 7 7 7 4.25 4 8.75 4 8.75 9 5.25 4.75 4.5 4.5 4.5 7.75 5 7.375 4.5 6.75 6.5 6 6 6 6 5.25 4.75

0.1361 0.3917 0.4778 0.9778 1.0750 1.1361 1.3861 1.5667 1.6333 1.7278 1.9778 2.3139 3.0611 3.3167 3.5528 3.8083 3.9250 4.0389 4.0944 4.3056 5.7139 6.9611 7.1861 7.3000 8.1833 8.6833 9.1833 9.6833

Suppose that we choose to model the discount factor with 5 basis functions and let b equal to the yield of DBR4.75 7/2008. The first step is to calculate the zi,j , ió1 to 28, jó1 to 5. An example of the calculation for DBR6.75 7/4 is described below. This bond pays a coupon of 6.75%, matures in 5.139 years and the next coupon is payable in 0.7139 years from the settle date.

5

z21,2 ó 6.75 ; e ñ2î0.0413î(tò0.7139) ò100e ñ2î0.0413î5.7139 tó0

ó93.70 Similarly, we can calculate the zi,j for all the bonds in our sample and the results are shown in Table 3.18. Next we apply equation (3.28) to obtain the data for the ordinary least square regression estimation. This is reported in Table 3.19. Finally we estimate the a’s using ordinary least square regression and use it in equation (3.20) to generate the discount factors and yield curves. The coefficient

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The Professional’s Handbook of Financial Risk Management Table 3.18 DEM government bond zi,j calculation results

Issue TOBL5 12/98 BKO3.75 3/99 DBR7 4/99 DBR7 10/99 TOBL7 11/99 BKO4.25 12/99 BKO4 3/0 DBR8.75 5/0 BKO4 6/0 DBR8.75 7/0 DBR9 10/0 OBL 118 OBL 121 OBL 122 OBL 123 OBL 124 THA7.75 10/2 OBL 125 THA7.375 12/2 OBL 126 DBR6.75 7/4 DBR6.5 10/5 DBR6 1/6 DBR6 2/6 DBR6 1/7 DBR6 7/7 DBR5.25 1/8 DBR4.75 7/8

zi,1

zi,2

zi,3

zi,4

zi,5

104.41 102.08 104.91 102.76 109.33 103.69 102.14 110.48 101.11 109.74 109.09 105.80 105.94 103.90 102.89 101.81 113.09 107.64 118.30 104.18 114.51 113.75 115.70 115.15 116.98 114.58 111.97 105.62

103.82 100.44 102.86 98.69 104.86 99.10 96.61 103.89 94.66 102.51 100.86 96.75 94.41 91.57 89.80 87.92 97.76 92.87 102.50 88.77 93.70 89.48 91.23 90.37 89.92 86.27 83.45 76.72

103.24 98.83 100.84 94.78 100.58 94.73 91.39 97.70 88.63 95.77 93.26 88.50 84.21 80.78 78.45 76.00 84.63 80.31 89.06 75.79 77.03 70.87 72.64 71.62 70.05 65.84 63.28 56.68

102.66 97.24 98.87 91.02 96.49 90.55 86.45 91.89 82.98 89.48 86.25 80.99 75.20 71.34 68.61 65.77 73.37 69.61 77.61 64.85 63.63 56.57 58.47 57.38 55.40 51.01 48.92 42.67

102.09 95.68 96.94 87.42 92.57 86.56 81.78 86.45 77.71 83.61 79.77 74.16 67.24 63.07 60.07 56.98 63.71 60.50 67.85 55.64 52.85 45.54 47.64 46.53 44.54 40.17 38.62 32.80

estimates and the resultant discount factor curve are reported in Tables 3.20 and 3.21, respectively. The discount factor can be used for valuation, rich-cheap analysis or to generate the zero curve and the forward yield curves.

Model review The discount factors produced by the yield curve models are used for marking-tomarket of position and calculation of end-of-day gains/losses. Others bump the input cash and swap rates to the yield curve model to generate a new set of discount factors and revalue positions. This provides traders with an estimate of their exposure to different tenor and hedge ratios to manage risk of their positions. Models such as those of Heath, Jarrow and Morton (1992) and Brace, Gaterak and Musiela (1995) use the forward rates implied from the yield curve as a starting point to simulate the future evolution of the forward rate curve. Spot rate models such as those of Hull and White (1990), Black, Derman and Toy (1990) and Black and Karasinsky (1990) estimate parameters for the model by fitting it to the yield curve data. The model to

The yield curve

107 Table 3.19 DEM government bond xi,j regression data

Issue TOBL5 12/98 BKO3.75 3/99 DBR7 4/99 DBR7 10/99 TOBL7 11/99 BKO4.25 12/99 BKO4 3/0 DBR8.75 5/0 BKO4 6/0 DBR8.75 7/0 DBR9 10/0 OBL 118 OBL 121 OBL 122 OBL 123 OBL 124 THA7.75 10/2 OBL 125 THA7.375 12/2 OBL 126 DBR6.75 7/4 DBR6.5 10/5 DBR6 1/6 DBR6 2/6 DBR6 1/7 DBR6 7/7 DBR5.25 1/8 DBR4.75 7/8

yi

xi,1

xi,2

xi,3

xi,4

2.4427 6.7305 8.3987 16.1479 17.6444 18.0511 21.4836 25.3652 24.7094 27.5772 30.9865 33.4955 40.6304 42.8011 44.8833 46.9444 51.6844 49.2943 52.6131 50.8381 63.8172 69.5667 68.9955 69.5806 72.4097 74.1636 73.6259 73.5156

2.3240 6.4027 7.9702 15.3430 16.7562 17.1321 20.3622 24.0300 23.3980 26.1283 29.3115 31.6457 38.7041 40.8274 42.8227 44.8324 49.3862 47.1380 50.4555 48.5455 61.6541 68.2133 68.0584 68.6234 72.4375 74.4180 73.3496 72.8177

1.7381 4.7630 5.9182 11.2716 12.2852 12.5442 14.8299 17.4390 16.9496 18.8960 21.0826 22.5896 27.1683 28.4955 29.7297 30.9431 34.0537 32.3718 34.6570 33.1304 40.8501 43.9415 43.5888 43.8422 45.3753 46.1074 44.8310 43.9244

1.1555 3.1495 3.9064 7.3615 8.0077 8.1659 9.6033 11.2536 10.9182 12.1524 13.4866 14.3445 16.9745 17.7065 18.3797 19.0232 20.9184 19.8071 21.2112 20.1514 24.1722 25.3377 25.0013 25.0908 25.5126 25.6739 24.6579 23.8769

0.5761 1.5620 1.9339 3.6064 3.9153 3.9876 4.6653 5.4485 5.2768 5.8641 6.4741 6.8368 7.9648 8.2648 8.5376 8.7895 9.6586 9.1107 9.7599 9.2172 10.7782 11.0347 10.8359 10.8533 10.8640 10.8408 10.2981 9.8696

Table 3.20 DEM exponential model coefﬁcient estimations Coefﬁcient b a1 a2 a3 a4 a5

Estimate 4.13% 16.97 ñ77.59 139.55 ñ110.08 32.15

generate the yield curve model is not as complicated as some of the term structure models. However, any small error made while building the yield curve can have a progressively amplified impact on valuation and hedge ratios unless it has been reviewed carefully. We briefly outline some of the issues that must be kept in mind while validating them. First, the yield curve model should be arbitrage free. A quick check for this would

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The Professional’s Handbook of Financial Risk Management Table 3.21 DEM government bond market discount factor curve Time

Discount factor

Par coupon yield

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00

1.0000 0.9668 0.9353 0.9022 0.8665 0.8288 0.7904 0.7529 0.7180 0.6871 0.6613

3.44% 3.40% 3.49% 3.64% 3.80% 3.96% 4.09% 4.17% 4.20% 4.18%

be to verify if the discount factors generated by the yield curve model can produce the same cash and swap rates as those feed into the model. In addition there may be essentially four possible sources of errors – use of inappropriate market rates data, accrual factor calculations, interpolation algorithms, and curve-stitching. The rates used to build the curve for the short-term product will not be the same as the rates used for pricing long-term products. The individual desk primarily determines this so any curve builder model should offer flexibility to the user in selecting the source and the nature of rate data. Simple as it may seem, another common source of error is incorrect holiday calendar and market conventions for day count to calculate the accrual factors. Fortunately the computation of accrual factors is easy to verify. Interpolation algorithms expose the yield curve model to numerical instability. As we have mentioned earlier, some interpolation methods such as the cubic spline may be capable of producing a very smooth curve but performs poorly during computation of the hedge ratio. A preferable attribute for the interpolation method is to have a local impact on yield curve to changes in specific input data rather than affecting the entire curve. There are many systems that offer users a menu when it comes to the interpolation method. While it is good to have such flexibility, in the hands of a user with little understanding of the implications this may be risky. It is best to ensure that the model provides sensible alternatives and eliminate choices that may be considered unsuitable after sufficient research. In the absence of reasonable market data curve stitching can be achieved by interpolating either the stub rate or the discount factor. A linear interpolation can be applied for rates but not if it is a discount factor.

Summary In this chapter we have discussed the methodology to build the market yield curve for the swap market and the bond market. The market yield curve is one of the most important pieces of information required by traders and risk managers to price, trade, mark-to-market and control risk exposure. The yield curve can be described as discount factor curves, par curves, forward curves or zero curves. Since the

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discount factors are the most rudimentary information for valuing any stream of cash flows it is the most natural place to start. Unfortunately discount factors are not directly observable in the market, rather they have to be derived from the marketquoted interest rates and prices of liquid financial instruments. The swap market provides an abundance of par swap rates data for various tenors. This can be applied to extract the discount factors using the bootstrap method. The bootstrap approach produces a discount factor curve that is consistent with the market swap rates satisfying the condition of no-arbitrage condition. When a swap rate or discount factor for a specific date is not available then interpolation methods may need to be applied to determine the value. These methods must be carefully chosen since they can have significant impact on the resultant yield curve, valuations and risk exposure calculations. Linear interpolation for swap rates and exponential interpolation for discount factors is a recommended approach due to their simplicity and favorable performance attributes. In the bond market due to non-uniform price data on various tenors, coupon effect and liquidity factors a statistical method has to be applied to derive the discount factor curves. The objective is not necessarily to derive discount factors that will price every bond to the market exactly. Instead we estimate the parameters of the model that will minimize the sum of squares of pricing errors for the sample of bonds used. In theory many statistical models can be prescribed to fit the discount curve function. We have reviewed a few and provided details on the implementation of the exponential model. An important criterion for these models is that they satisfy certain basic constraints such as discount factor function equal to one on spot date, converge to zero for extremely long tenors, and be a decreasing function with respect to tenors.

Note 1

For an intuitive description of the futures convexity adjustment and calculations using this expression see Burghartt and Hoskins (1996). For other technical approaches to convexity adjustment see interest rate models such as Hull and White (1990) and Heath, Jarrow and Morton (1992).

References Anderson, N., Breedon, F., Deacon, M. and Murphy, G. (1997) Estimating and Interpreting the Yield Curve, John Wiley. Black, F., Derman, E. and Toy, W. (1990) ‘A one-factor model of interest rates and its application to treasury bond options’, Financial Analyst Journal, 46, 33–39. Black, F. and Karasinski, P. (1991) ‘Bond and option pricing when short rates are lognormal’, Financial Analyst Journal, 47, 52–9. Brace, A., Gatarek, D. and Musiela, M. (1997) ‘The market model of interest-rate dynamics’, Mathematical Finance, 7, 127–54. Burghartt, G. and Hoskins, B. (1996) ‘The convexity bias in Eurodollar futures’, in Konishi, A. and Dattatreya, R. (eds), Handbook of Derivative Instruments, Irwin Professional Publishing. Chambers, D., Carleton, W. and Waldman, D. (1984) ‘A new approach to estimation

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of the term structure of interest rates’, Journal of Financial and Quantitative Analysis, 19, 233–52. Coleman, T., Fisher, L. and Ibbotson, R. (1992) ‘Estimating the term structure of interest rates from data that include the prices of coupon bonds’, The Journal of Fixed Income, 85–116. Heath, D., Jarrow, R. and Morton, A. (1992) ‘Bond pricing and the term structure of interest rates: a new methodology for contingent claim valuation’, Econometrica 60, 77–105. Hull, J. and White, A. (1990) ‘Pricing interest rate derivative securities’, Review of Financial Studies, 3, 573–92. Jamshidian, F. (1997) ‘LIBOR and swap market models and measures’, Finance & Stochastics, 1, 261–91. McCulloch, J. H. (1971) ‘Measuring the term structure of interest rates’, Journal of Finance, 44, 19–31. McCulloch, J. H. (1975) ‘The tax-adjusted yield curve’, Journal of Finance, 30, 811–30. Nelson, C. R. and Siegel, A. F. (1987) ‘Parsimonious modeling of yield curves’, Journal of Business, 60, 473–89. Schaefer, S. M. (1981) ‘Measuring a tax-specific term structure of interest rates in the market for British government securities’, The Economic Journal, 91, 415–38. Shea, G. S. (1984) ‘Pitfalls in smoothing interest rate term structure data: equilibrium models and spline approximation’, Journal of Financial and Quantitative Analysis, 19, 253–69. Steeley, J. M. (1991) ‘Estimating the gilt-edged term structure: basis splines and confidence interval’, Journal of Business, Finance and Accounting, 18, 512–29. Vasicek, O. (1977) ‘An equilibrium characterization of the term structure’, Journal of Financial Economics, 5, 177–88. Vasicek, O. and Fong, H. (1982) ‘Term structure modeling using exponential splines’, Journal of Finance, 37, 339–56. Wiseman, J. (1994) European Fixed Income Research, 2nd edn, J. P. Morgan.

4

Choosing appropriate VaR model parameters and risk-measurement methods IAN HAWKINS Risk managers need a quantitative measure of market risk that can be applied to a single business, compared between multiple businesses, or aggregated across multiple businesses. The ‘Value at Risk’ or VaR of a business is a measure of how much money the business might lose over a period of time in the future. VaR has been widely adopted as the primary quantitative measure of market risk within banks and other financial service organizations. This chapter describes how we define VaR; what the major market risks are and how we measure the market risks in a portfolio of transactions; how we use models of market behavior to add up the risks; and how we estimate the parameters of those models. A sensible goal for risk managers is to implement a measure of market risk that conforms to industry best practice, with the proviso that they do so at reasonable cost. We will give two notes of caution. First, VaR is a necessary part of the firm-wide risk management framework, but not – on its own – sufficient to monitor market risk. VaR cannot replace the rich set of trading controls that most businesses accumulate over the years. These trading controls were either introduced to solve risk management problems in their own businesses or were implemented to respond to risk management problems that surfaced in other businesses. Over-reliance on VaR, or any other quantitative measure of risk, is simply an invitation for traders to build up large positions that fall outside the capabilities of the VaR implementation. Second, as with any model, VaR is subject to model risk, implementation risk and information risk. Model risk is the risk that we choose an inappropriate model to describe the real world. The real world is much more complicated than any mathematical model of the real world that we could create to describe it. We can only try to capture the most important features of the real world, as they affect our particular problem – the measurement of market risk – and, as the world changes, try to change our model quickly enough for the model to remain accurate (see Chapter 14).

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Implementation risk is the risk that we didn’t correctly translate our mathematical model into a working computer program – so that even if we do feed in the right numbers, we don’t get the answer from the program that we should. Finally, information risk is the risk that we don’t feed the right numbers into our computer program. VaR calculation is in its infancy, and risk managers have to accept a considerable degree of all three of these risks in their VaR measurement solutions.

Choosing appropriate VaR model parameters We will begin with a definition of VaR. Our VaR definition includes parameters, and we will go on to discuss how to choose each of those parameters in turn.

VaR deﬁnition The VaR of a portfolio of transactions is usually defined as the maximum loss, from an adverse market move, within a given level of confidence, for a given holding period. This is just one definition of risk, albeit one that has gained wide acceptance. Other possibilities are the maximum expected loss over a given holding period (our definition above without the qualifier of a given level of confidence) or the expected loss, over a specified confidence level, for a given holding period. If we used these alternative definitions, we would find it harder to calculate VaR; however, the VaR number would have more relevance. The first alternative definition is what every risk manager really wants to know – ‘How much could we lose tomorrow?’ The second alternative definition is the theoretical cost of an insurance policy that would cover any excess loss, over the standard VaR definition. For now, most practitioners use the standard VaR definition, while the researchers work on alternative VaR definitions and how to calculate VaR when using them (Artzner et al., 1997; Acar and Prieul, 1997; Embrechts et al., 1998; McNeil, 1998). To use our standard VaR definition, we have to choose values for the two parameters in the definition – confidence level and holding period.

Conﬁdence level Let’s look at the picture of our VaR definition shown in Figure 4.1. On the horizontal axis, we have the range of possible changes in the value of our portfolio of transactions. On the vertical axis, we have the probability of those possible changes occurring. The confidence levels commonly used in VaR calculations are 95% or 99%. Suppose we want to use 95%. To find the VaR of the portfolio, we put our finger on the right-hand side of the figure and move the finger left, until 95% of the possible changes are to the right of our finger and 5% of the changes are to the left of it. The number on the horizontal axis, under our finger, is the VaR of the portfolio. Using a 95% confidence interval means that, if our model is accurate, we expect to lose more than the VaR on only 5 days out of a 1001 . The VaR does not tell how much we might actually lose on those 5 days. Using a 99% confidence interval means that we expect to lose more than the VaR on only 1 day out of a 100. Most organizations use a confidence level somewhere between 95% and 99% for their inhouse risk management. The BIS (Bank for International Settlements) requirement for calculation of regulatory capital is a 99% confidence interval (Basel Committee on Banking Supervision, 1996). While the choice of a confidence level is a funda-

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Figure 4.1 VaR deﬁnition.

mental risk management statement for the organization, there is one modeling issue to bear in mind. The closer that the confidence level we choose is to 100%, the rarer are the events that lie to the left of our VaR line. That implies that we will have seen those events fewer times in the past, and that it will be harder for us to make accurate predictions about those rare events in the future. The standard VaR definition does not tell us much about the shape of the overall P/L distribution, other than the likelihood of a loss greater than the VaR. The distribution of portfolio change in value shown in Figure 4.2 results in the same VaR as the distribution in Figure 4.1, but obviously there is a greater chance of a loss of more than say, $4.5 million, in Figure 4.2 than in Figure 4.1.

Figure 4.2 VaR deﬁnition (2) – same VaR as Figure 4.1, but different tail risk.

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Holding period The holding period is the length of time, from today, to the horizon date at which we attempt to model the loss of our portfolio of transactions. There is an implicit assumption in VaR calculation that the portfolio is not going to change over the holding period. The first factor in our choice of holding period is the frequency with which new transactions are executed and the impact of the new transactions on the market risk of the portfolio. If those new transactions have a large impact on the market risk of the portfolio, it doesn’t make much sense to use a long holding period because it’s likely that the market risk of the portfolio will change significantly before we even reach the horizon date, and therefore our VaR calculation will be extremely inaccurate. The second factor in our choice of holding period is the frequency with which the data on the market risk of the portfolio can be assembled. We want to be able to look ahead at least to the next date on which we will have the data to create a new report. While banks are usually able to consolidate most of their portfolios at least once a day, for non-financial corporations, monthly or quarterly reporting is the norm. The third factor in our choice of holding period is the length of time it would take to hedge the risk positions of the portfolio at tolerable cost. The faster we try to hedge a position, the more we will move the market bid or offer price against ourselves, and so there is a balance between hedging risk rapidly to avoid further losses, and the cost of hedging. It’s unreasonable to try to estimate our maximum downside using a holding period that is significantly shorter than the time it would take to hedge the position. The ability to hedge a position is different for different instruments and different markets, and is affected by the size of the position. The larger the position is, in relation to the normal size of transactions traded in the market, the larger the impact that hedging that position will have on market prices. The impact of hedging on prices also changes over time. We can account for this third factor in a different way – by setting aside P/L reserves against open market risk positions. For most institutions, reserves are a much more practical way of incorporating liquidity into VaR than actually modeling the liquidation process. In banks, the most common choice of holding period for internal VaR calculations is 1 day. For bank regulatory capital calculations, the BIS specifies a 10-day holding period, but allows banks to calculate their 10-day VaR by multiplying their 1-day VaR by the square root of 10 (about 3.16). If, for example, a bank’s 1-day VaR was $1.5 million, its 10-day VaR would be $4.74 million ($1.5 million *3.16). Using the square root of time to scale VaR from one time horizon to another is valid if market moves are independently distributed over time (i.e. market variables do not revert to the mean, or show autocorrelation). For the purpose of calculating VaR, this assumption is close enough to reality, though we know that most market variables do actually show mean reversion and autocorrelation.

Applicability of VaR If all assets and liabilities are accounted for on a mark-to-market basis, for example financial instruments in a bank trading book or corporate Treasury, then we can use VaR directly. If assets and liabilities are not all marked to market, we can either

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estimate proxy market values of items that are accrual-accounted, and then use VaR, or use alternative measures of risk to VaR – that are derived from a more tradition ALM perspective. Some examples of alternative measures of performance to mark-to-market value are projected earnings, cash flow and cost of funds – giving rise to risk measures such as Earnings at Risk, Cash flow at Risk or Cost of Funds at Risk.

Uses of VaR To place the remainder of the chapter in context, we will briefly discuss some of the uses of VaR measures.

Regulatory market risk capital Much of the current interest in VaR has been driven by the desire of banks to align their regulatory capital with the bank’s perception of economic capital employed in the trading book, and to minimize the amount of capital allocated to their trading books, by the use of internal VaR models rather than the ‘BIS standardized approach’ to calculation of regulatory capital.

Internal capital allocation Given that VaR provides a metric for the economic capital that must be set aside to cover market risk, we can then use the VaR of a business to measure the returns of that business adjusted for the use of risk capital. There are many flavors of riskadjusted returns, and depending on the intended use of the performance measure, we may wish to consider the VaR of the business on a stand-alone basis, or the incremental VaR of the business as part of the whole organization, taking into account any reduction in risk capital due to diversification.

Market risk limits VaR can certainly be used as the basis of a limits system, so that risk-reducing actions are triggered when VaR exceeds a predefined level. In setting VaR limits, we must consider how a market loss typically arises. First we experience an adverse move and realize losses of the order of the VaR. Then over the next few days or weeks we experience more adverse moves and we lose more money. Then we review our position, the position’s mark to market, and the model used to generate the mark to market. Then we implement changes, revising the mark to market by writing down the value of our position, and/or introducing a new model, and/or reducing our risk appetite and beginning the liquidation of our position. We lose multiples of the VaR, then we rethink what we have been doing and take a further hit as we make changes and we pay to liquidate the position. The bill for the lot is more than the sum of the VaR and our liquidity reserves.

Risk measurement methods This section describes what the sources of market risk are, and how we measure them for a portfolio of transactions. First, a definition of market risk: ‘Market risk is

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the potential adverse change in the value of a portfolio of financial instruments due to changes in the levels, or changes in the volatilities of the levels, or changes in the correlations between the levels of market prices.’ 2 Risk comes from the combination of uncertainty and exposure to that uncertainty. There is no risk in holding a stock if we are completely certain about the future path of the stock’s market price, and even if we are uncertain about the future path of a stock’s market price, there is no risk to us if we don’t hold a position in the stock!

Market risk versus credit risk It is difficult to cleanly differentiate market risk from credit risk. Credit risk is the risk of loss when our counterpart in a transaction defaults on (fails to perform) its contractual obligations, due to an inability to pay (as opposed to an unwillingness to pay). The risk of default in a loan with a counterpart will usually be classified as credit risk. The risk of default in holding a counterpart’s bond, will also usually be classified as credit risk. However, the risk that the bond will change in price, because the market’s view of the likelihood of default by the counterpart changes, will usually be classified as a market risk. In most banks, the amount of market risk underwritten by the bank is dwarfed in size by the amount of credit risk underwritten by the bank. While banks’ trading losses attract a great deal of publicity, particularly if the losses involve derivatives, banks typically write off much larger amounts of money against credit losses from non-performing loans and financial guarantees.

General market risk versus speciﬁc risk. When analyzing market risk, we break down the risk of a transaction into two components. The change in the transaction’s value correlated with the behavior of the market as a whole is known as systematic risk, or general market risk. The change in the transaction’s value not correlated with the behavior of the market as a whole is known as idiosyncratic risk, or specific risk. The factors that contribute to specific risk are changes in the perception of the credit quality of the underlying issuer or counterpart to a transaction, as we discussed above, and supply and demand, which we will discuss below. Consider two bonds, with the same issuer, and of similar maturities, say 9 years and 11 years. The bonds have similar systematic risk profiles, as they are both sensitive to interest rates of around 10 years maturity. However, they are not fungible, (can’t be exchanged for each other). Therefore, supply and demand for each individual bond may cause the individual bond’s actual price movements to be significantly different from that expected due to changes in the market as a whole. Suppose we sell the 9-year bond short, and buy the same notional amount of the 11-year bond. Overall, our portfolio of two transactions has small net general market risk, as the exposure of the short 9-year bond position to the general level of interest rates will be largely offset by the exposure of the long 11-year bond position to the general level of interest rates. Still, the portfolio has significant specific risk, as any divergence in the price changes of our two bonds from that expected for the market as a whole will cause significant unexpected changes in portfolio value. The only way to reduce the specific risk of a portfolio is to diversify the portfolio holdings across a large number of different instruments, so that the contribution to changes in portfolio value from each individual instrument are small relative to the total changes in portfolio value.

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VaR typically only measures general market risk, while specific risk is captured in a separate risk measurement.

Sources of market risk Now we will work through the sources of market uncertainty and how we quantify the exposure to that uncertainty.

Price level risk The major sources of market risk are changes in the levels of foreign exchange rates, interest rates, commodity prices and equity prices. We will discuss each of these sources of market risk in turn.

FX rate risk In the most general terms, we can describe the value of a portfolio as its expected future cash flows, discounted back to today. Whenever a portfolio contains cash flows denominated in, or indexed to, a currency other than the base currency of the business, the value of the portfolio is sensitive to changes in the level of foreign exchange rates. There are a number of different ways to represent the FX exposure of a portfolio. Currency pairs A natural way to represent a portfolio of foreign exchange transactions is to reduce the portfolio to a set of equivalent positions in currency pairs: so much EUR-USD at exchange rate 1, so much JPY-USD at exchange rate 2, so much EUR-JPY at exchange rate 3. As even this simple example shows, currency pairs require care in handling positions in crosses (currency pairs in which neither currency is the base currency). Risk point method We can let our portfolio management system do the work, and have the system revalue the portfolio for a defined change in each exchange rate used to mark the portfolio to market, and report the change in value of the portfolio for each change in exchange rate. This process is known as the Risk Point Method, or more informally as ‘bump and grind’ (bump the exchange rate and grind through the portfolio revaluation). As with currency pairs, we need to make sure that exposure in crosses is not double counted, or inconsistent with the treatment of the underlying currency pairs. Cash ﬂow mapping A more atomic representation of foreign exchange exposure is to map each forward cash flow in the portfolio to an equivalent amount of spot cash flow in that currency: so many EUR, so many JPY, so many USD. Reference (risk) currency versus base (accounting) currency Global trading groups often denominate their results in US dollars (reference currency) and consider US dollars as the currency that has no foreign exchange risk.

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When those trading groups belong to business whose base currency is not US dollars, some care is required to make sure that the group is not subject to the risk that a change in the US dollar exchange rate to the base currency will cause unexpected over- or under-performance relative to the budget set in the base currency.

Interest rate risk It is fairly obvious that the level of interest rates affects fixed-income securities, but – as we have seen earlier in this book – the level of interest rates also affects the prices of all futures and forward contracts relative to spot prices. Buying a currency for delivery forward is equivalent to buying the currency spot and lending/borrowing the proceeds of the spot transaction. The interest rates at which the proceeds of the spot transaction are lent or borrowed determine the ‘no-arbitrage’ forward exchange rate relative to the spot exchange rate. Going back to our general statement that the value of a portfolio is the discounted value of its expected cash flows, it follows that a portfolio that has any future cash flows will be subject to some degree of interest rate risk. As with foreign exchange risk, there are several ways we can quantify interest rate exposure. Cash ﬂow mapping We can map each cash flow in the portfolio to a standard set of maturities from today out to, say, 30-years. Each cash flow will lie between two standard maturities. The cash flow can be allocated between the two maturities according to some rule. For instance, we might want the present value of the cash flow, and the sensitivity of the cash flow to a parallel shift in the yield curve, to equal the present value and sensitivity of the two cash flows after the mapping. Duration bucketing Alternatively, we can take a portfolio of bonds and summarize its exposure to interest rates by bucketing the PV01 of each bond position according to the duration of each bond. Risk point method Finally, as with foreign exchange, one can let the portfolio system do the work, and revalue the portfolio for a defined change in each interest rate, rather than each foreign exchange rate. Principal component analysis (PCA) Given one of these measures of exposure we now have to measure the uncertainty in interest rates, so we can apply the uncertainty measure to the exposure measure and obtain a possible change in mark-to-market value. We know that the level and shape of the yield curve changes in a complicated fashion over time – we see the yield curve move up and down, and back and forth between its normal, upwardsloping, shape and flat or inverted (downward-sloping) shapes. One way to capture this uncertainty is to measure the standard deviation of the changes in the yield at each maturity on the yield curve, and the correlation between the changes in the yields at each pair of maturities. Table 4.1 shows the results of analyzing CMT (Constant Maturity Treasury) data from the US Treasury’s H.15 release over the period from 1982 to 1998. Reading down to the bottom of the

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Table 4.1 CMT yield curve standard deviations and correlation matrix Maturity Standard in years deviation 0.25 0.5 1 2 3 5 7 10 20 30

1.26% 1.25% 1.26% 1.26% 1.25% 1.19% 1.14% 1.10% 1.05% 0.99%

Correlation 0.25

0.5

1

2

3

5

7

10

20

30

100% 93% 82% 73% 67% 60% 56% 54% 52% 50%

100% 96% 89% 85% 79% 75% 73% 70% 67%

100% 97% 94% 90% 87% 84% 80% 77%

100% 99% 97% 94% 92% 87% 85%

100% 99% 97% 95% 91% 89%

100% 99% 98% 94% 92%

100% 99% 96% 95%

100% 98% 97%

100% 98%

100%

standard deviation column we can see that the 30-year CMT yield changes by about 99 bps (basis points) per annum. Moving across from the standard deviation of the 30-year CMT to the bottom-left entry of the correlation matrix, we see that the correlation between changes in the 30-year CMT yield and the 3-month CMT yield is about 50%. For most of us, this table is a fairly unwieldy way of capturing the changes in the yield curve. If, as in this example, we use 10 maturity points on the yield curve in each currency that we have to model, then to model two curves, we will need a correlation matrix that has 400 (20î20) values. As we add currencies the size of the correlation matrix will grow very rapidly. For the G7 currencies we would require a correlation matrix with 4900 (70*70) values. There is a standard statistical technique, called principal component analysis, which allows us to approximate the correlation matrix with a much smaller data set. Table 4.2 shows the results of applying a matrix operation called eigenvalue/ eigenvector decomposition to the product of the standard deviation vector and the correlation matrix. The decomposition allows us to extract common factors from the correlation matrix, which describe how the yield curve moves as a whole. Looking Table 4.2 CMT yield curve factors Proportion of variance 86.8% 10.5% 1.8% 0.4% 0.2% 0.1% 0.1% 0.0% 0.0% 0.0%

Factor shocks 0.25

0.5

1

2

3

5

7

10

20

30

0.95% 1.13% 1.21% 1.25% 1.23% 1.16% 1.10% 1.04% 0.96% 0.89% ñ0.79% ñ0.53% ñ0.26% ñ0.03% 0.10% 0.23% 0.30% 0.33% 0.34% 0.34% ñ0.23% ñ0.01% 0.16% 0.19% 0.16% 0.08% 0.00% ñ0.08% ñ0.20% ñ0.24% ñ0.11% 0.10% 0.11% ñ0.01% ñ0.07% ñ0.08% ñ0.04% ñ0.01% 0.05% 0.05% 0.01% ñ0.06% 0.01% 0.05% 0.07% ñ0.03% ñ0.08% ñ0.07% 0.01% 0.08% ñ0.01% 0.03% ñ0.01% ñ0.03% 0.01% 0.01% 0.00% 0.03% ñ0.10% 0.07% 0.02% ñ0.06% 0.06% ñ0.01% ñ0.03% ñ0.01% 0.03% 0.01% ñ0.02% 0.00% 0.00% 0.01% ñ0.01% 0.00% 0.00% 0.00% 0.04% ñ0.05% 0.00% 0.01% 0.00% 0.00% ñ0.02% 0.06% ñ0.05% 0.01% 0.00% 0.00% ñ0.01% 0.01% 0.00% 0.00% ñ0.02% 0.02% 0.02% ñ0.06% 0.02% 0.02% ñ0.01% ñ0.01%

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down the first column of the table, we see that the first three factors explain over 95% of the variance of interest rates! The first three factors have an intuitive interpretation as a shift movement, a tilt movement and a bend movement – we can see this more easily from Figure 4.3, which shows a plot of the yield curve movements by maturity for each factor.

Figure 4.3 Yield curve factors.

In the shift movement, yields in all maturities change in the same direction, though not necessarily by the same amount: if the 3-month yield moves up by 95 bps, the 2-year yield moves up by 125 bps and the 30Y yield moves up by 89 bps. The sizes of the changes are annualized standard deviations. In the bend movement, the short and long maturities change in opposite directions: if the 3-month yield moves down by 79 bps, the 2-year yield moves down by 3 bps (i.e. it’s almost unchanged) and the 30Y yield moves up by 34 bps. In the bend movement, yields in the short and long maturities change in the same direction, while yields in the intermediate maturities change in the opposite direction: if the 3-month yield moves down by 23 bps, the 2year yield moves up by 19 bps and the 30Y yield moves down by 24 bps. As we move to higher factors, the sizes of the yield changes decrease, and the sign of the changes flips more often as we read across the maturities. If we approximate the changes in the yield curve using just the first few factors we significantly reduce the dimensions of the correlation matrix, without giving up a great deal of modeling accuracy – and the factors are, by construction, uncorrelated with each other.

Commodity price risk At first glance, commodity transactions look very much like foreign exchange transactions. However, unlike currencies, almost cost-less electronic ‘book-entry’ transfers of commodities are not the norm. Commodity contracts specify the form and location of the commodity that is to be delivered. For example, a contract to buy copper will specify the metal purity, bar size and shape, and acceptable warehouse locations that the copper may be sent to. Transportation, storage and insurance are significant factors in the pricing of forward contracts. The basic arbitrage relationship for a commodity forward is that

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the cost of buying the spot commodity, borrowing the money, and paying the storage and insurance must be more than or equal to the forward price. The cost of borrowing a commodity is sometimes referred to as convenience yield. Having the spot commodity allows manufacturers to keep their plants running. The convenience yield may be more than the costs of borrowing money, storage and insurance. Unlike in the foreign exchange markets, arbitrage of spot and forward prices in the commodity markets is not necessarily straightforward or even possible: Ω Oil pipelines pump oil only in one direction. Oil refineries crack crude to produce gasoline, and other products, but can’t be put into reverse and turn gasoline back into crude oil. Ω Arbitrage of New York and London gold requires renting armored cars, a Jumbo jet and a gold refinery (to melt down 400 oz good delivery London bars and cast them into 100 oz good delivery Comex bars). Ω Soft commodities spoil: you can’t let sacks of coffee beans sit in a warehouse forever – they rot! The impact on risk measurement of the constraints on arbitrage is that we have to be very careful about aggregating positions: whether across different time horizons, across different delivery locations or across different delivery grades. These mismatches are very significant sources of exposure in commodities, and risk managers should check, particularly if their risk measurement systems were developed for financial instruments, that commodity exposures are not understated by netting of longs and shorts across time, locations or deliverables that conceals significant risks.

Equity price risk Creating a table of standard deviations and correlations is unwieldy for modeling yield curves that are made up of ten or so maturities in each currency. To model equity markets we have to consider hundreds or possibly even thousands of listed companies in each currency. Given that the correlation matrix for even a hundred equities would have 10 000 entries, it is not surprising that factor models are used extensively in modeling equity market returns and risk. We will give a brief overview of some of the modeling alternatives.3 Single-factor models and beta Single-factor models relate the return on an equity to the return on a stock market index: ReturnOnStockóai òbi* ReturnOnIndexòei bi is cov(ReturnOnStock,ReturnOnIndex)/ var(ReturnOnIndex) ei is uncorrelated with the return on the index The return on the stock is split into three components: a random general market return, measured as b multiplied by the market index return, which can’t be diversified, (so you do get paid for assuming the risk); a random idiosyncratic return, e, which can be diversified (so you don’t get paid for assuming the risk); and an expected idiosyncratic return a. This implies that all stocks move up and down together, differing only in the magnitude of their movements relative to the market index, the b, and the magnitude of an idiosyncratic return that is uncorrelated with either the market index return

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or the idiosyncratic return of any other equity. In practice many market participants use b as the primary measure of the market risk of an individual equity and their portfolio as a whole. While this is a very simple and attractive model, a single factor only explains about 35% of the variance of equity returns, compared to the singlefactor yield curve model, which explained almost 90% of the yield curve variance. Multi-factor models/APT The low explanatory power of single-factor models has led researchers to use multifactor models. One obvious approach is to follow the same steps we described for interest rates: calculate the correlation matrix, decompose the matrix into eigenvalues and eigenvectors, and select a subset of the factors to describe equity returns. More commonly, analysts use fundamental factors (such as Price/Earnings ratio, Style[Value, Growth, . . .], Market capitalization and Industry [Banking, Transportation, eCommerce, . . .]), or macro-economic factors (such as the Oil price, the Yield curve slope, Inflation, . . .) to model equity returns. Correlation and concentration risk We can break equity risk management into two tasks: managing the overall market risk exposure to the factor(s) in our model, and managing the concentration of risk in individual equities. This is a lot easier than trying to use the whole variance–covariance matrix, and follows the same logic as using factor models for yield curve analysis. Dividend and stock loan risk As with any forward transaction, the forward price of an equity is determined by the cost of borrowing the two deliverables, in this case the cost of borrowing money, and the cost of borrowing stock. In addition, the forward equity price is affected by any known cash flows (such as expected dividend payments) that will be paid before the forward date. Relative to the repo market for US Treasuries, the stock loan market is considerably less liquid and less transparent. Indexing benchmark risk We are free to define ‘risk’. While banks typically have absolute return on equity (ROE) targets and manage profit or loss relative to a fixed budget, in the asset management business, many participants have a return target that is variable, and related to the return on an index. In the simplest case, the asset manager’s risk is of a shortfall relative to the index the manager tracks, such as the S&P500. In effect this transforms the VaR analysis to a different ‘currency’ – that of the index, and we look at the price risk of a portfolio in units of, say, S&P500s, rather than dollars.

Price volatility risk One obvious effect of a change in price volatility is that it changes the VaR! Option products, and more generally instruments that have convexity in their value with respect to the level of prices, are affected by the volatility of prices. We can handle exposure to the volatility of prices in the same way as exposure to prices. We measure the exposure (or vega, or kappa4) of the portfolio to a change in volatility, we measure the uncertainty in volatility, and we bring the two together in a VaR calculation. In

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much the same way as the price level changes over time, price volatility also changes over time, so we measure the volatility of volatility to capture this uncertainty.

Price correlation risk In the early 1990s there was an explosion in market appetite for structured notes, many of which contained option products whose payoffs depended on more than one underlying price. Enthusiasm for structured notes and more exotic options has cooled, at least in part due to the well-publicized problems of some US corporations and money market investors, the subsequent tightening of corporate risk management standards, and the subsequent tightening of the list of allowable investment products for money market funds. However, there are still many actively traded options that depend on two or more prices – some from the older generation of exotic products, such as options on baskets of currencies or other assets, and some that were developed for the structured note market, such as ‘diff swaps’ and ‘quantos’. Options whose payoffs depend on two or more prices have exposure to the volatility of each of the underlying prices and the correlation between each pair of prices.

Pre-payment variance risk Mortgage-backed-securities (MBS) and Asset-backed-securities (ABS) are similar to callable bonds. The investor in the bond has sold the borrower (ultimately a homeowner with a mortgage, or a consumer with credit card or other debt) the option to pay off their debt early. Like callable debt, the value of the prepayment option depends directly on the level and shape of the yield curve, and the level and shape of the volatility curve. For instance, when rates fall, homeowners refinance and the MBS prepays principal back to the investor that the MBS investor has to reinvest at a lower rate than the MBS coupon. MBS prepayment risk can be hedged at a macro level by buying receivers swaptions or CMT floors, struck below the money: the rise in the value of the option offsets the fall in the MBS price. Unlike callable debt, the value of the prepayment option is also indirectly affected by the yield curve, and by other factors, which may not be present in the yield curve data. First, people move! Mortgages (except GNMA mortgages) are not usually assumable by the new buyer. Therefore to move, the homeowner may pay down a mortgage even if it is uneconomic to do so. The housing market is seasonal – most people contract to buy their houses in spring and close in summer. The overall state of the economy is also important: in a downturn, people lose their jobs. Rather than submit to a new credit check, a homeowner may not refinance, even if it would be economic to do so. So, in addition to the yield curve factors, a prepayment model will include an econometric model of the impact of the factors that analysts believe determine prepayment speeds: pool coupon relative to market mortgage rates, pool size, pool seasoning/burn-out: past yield curve and prepayment history, geographic composition of the pool, and seasonality. A quick comparison of Street models on the Bloomberg shows a wide range of projected speeds for any given pool! Predicting future prepayments is still as much an art as a science. The MBS market is both large and mature. Figure 4.4 shows the relative risk and return of the plain vanilla pass-through securities, and two types of derived securities that assign the cash flows of a pass-through security in different ways – stable

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Figure 4.4 Relative risk of different types of MBS (after Cheyette, Journal of Portfolio Management, Fall 1996).

tranches, which provide some degree of protection from prepayment risk to some tranche holders at the expense of increased prepayment risk for other tranche holders, and IOs/POs which separate the principal and interest payments of a passthrough security. Under the general heading of prepayment risk we should also mention that there are other contracts that may suffer prepayment or early termination due to external factors other than market prices. Once prepayment or early termination occurs, the contracts may lose value. For example, synthetic guaranteed investment contracts (‘GIC wraps’), which provide for the return of principal on a pension plan investment, have a legal maturity date, but typically also have provisions for compensation of the plan sponsor if a significant number of employees withdraw from the pension plan early, and their investments have lost money. Exogenous events that are difficult to hedge against may trigger withdrawals from the plan: layoffs following a merger or acquisition, declining prospects for the particular company causing employees to move on, and so on.

Portfolio response to market changes Following our brief catalogue of market risks, we now look at how the portfolio changes in value in response to changes in market prices, and how we can summarize those changes in value.

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Linear versus non-linear change in value Suppose we draw a graph of the change in value of the portfolio for a change in a market variable. If it’s a straight line, the change in value is linear in that variable. If it’s curved, the change in value is non-linear in that variable. Figure 4.5 shows a linear risk on the left and a non-linear risk on the right.

Figure 4.5 Linear versus non-linear change in value.

If change in value of the portfolio is completely linear in a price, we can summarize the change in value with a single number, the delta, or first derivative of the change in value with respect to the price. If the change in value is not a straight line, we can use higher derivatives to summarize the change in value. The second derivative is known as the gamma of the portfolio. There is no standard terminology for the third derivative on. In practice, if the change in value of the portfolio can’t be captured accurately with one or two derivatives then we simply store a table of the change in value for the range of the price we are considering! Using two or more derivatives to approximate the change in value of a variable is known as a Taylor series expansion of the value of the variable. In our case, if we wanted to estimate the change in portfolio value given the delta and gamma, we would use the following formula: Change in portfolio valueóDELTA ¥ (change in price)ò1/2 GAMMA ¥ (change in price)2 If the formula looks familiar, it may be because one common application of a Taylor series expansion in finance is the use of modified duration and convexity to estimate the change in value of a bond for a change in yield. If that doesn’t ring a bell, then perhaps the equation for a parabola ( yóaxòbx 2), from your high school math class, does.

Discontinuous change in value Both the graphs in Figure 4.5 could be drawn with a smooth line. Some products, such as digital options, give risk to jumps, or discontinuities, in the graph of portfolio change in value, as we show in Figure 4.6. Discontinuous changes in value are difficult to capture accurately with Taylor series.

Path-dependent change in value Risk has a time dimension. We measure the change in the value of the portfolio over time. The potential change in value, estimated today, between today and a future

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Figure 4.6 Discontinuous change in value.

date, may depend on what happens in the intervening period of time, not just on the state of the world at the future date. This can be because of the payoff of the product (for example, a barrier option) or the actions of the trader (for example, a stop loss order). Let’s take the case of a stop loss order. Suppose a trader buys an equity for 100 USD. The trader enters a stop loss order to sell at 90 USD. Suppose at the end of the time period we are considering the equity is trading at 115 USD. Before we can determine the value of the trader’s portfolio, we must first find out whether the equity traded at 90 USD or below after the trader bought it. If so, the trader would have been stopped out, selling the position at 90 USD (more probably a little below 90 USD); would have lost 10 USD on the trade and would currently have no position. If not, the trader would have a mark-to-market gain on the position of 15 USD, and would still be long. Taylor series don’t help us at all with this type of behavior. When is a Taylor series inaccurate? Ω Large moves: if the portfolio change in value is not accurately captured by one or two derivatives,5 then the larger the change in price over which we estimate the change in value, the larger the slippage between the Taylor series estimate and the true portfolio change in value. For a large change in price, we have to change the inputs to our valuation model, recalculate the portfolio value, and take the difference from the initial value. Ω Significant cross-partials: our Taylor series example assumed that the changes in value of the portfolio for each different risk factor that affects the value of the portfolio are independent. If not, then we have to add terms to the Taylor series expansion to capture these cross-partial sensitivities. For example, if our vega changes with the level of market prices, then we need to add the derivative of vega with respect to prices to our Taylor series expansion to make accurate estimates of the change in portfolio value.

Risk reports Risk managers spend a great deal of time surveying risk reports. It is important that risk managers understand exactly what the numbers they are looking at mean. As we have discussed above, exposures are usually captured by a measure of the derivative of the change in value of a portfolio to a given price or other risk factor.

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The derivative may be calculated as a true derivative, obtained by differentiating the valuation formula with respect to the risk factor, or as a numerical derivative, calculated by changing the value of the risk factor and rerunning the valuation to see how much the value changes. Numerical derivatives can be calculated by just moving the risk factor one way from the initial value (a one-sided difference), or preferably, by moving the risk factor up and down (a central difference). In some cases, the differences between true, one-sided numerical, and central numerical derivatives are significant. Bucket reports versus factor reports A bucket report takes a property of the trade (say, notional amount), or the output of a valuation model (say, delta), and allocates the property to one or two buckets according to a trade parameter, such as maturity. For example, the sensitivity of a bond to a parallel shift in the yield curve of 1 bp, might be bucketed by the maturity of the bond. A factor report bumps model inputs and shows the change in the present value of the portfolio for each change in the model input. For example, the sensitivity of a portfolio of FRAs and swaps to a 1 bp change in each input to the swap curve (money market rates, Eurodollar futures prices, US Treasuries and swap spreads). Both types of report are useful, but risk managers need to be certain what they are looking at! When tracking down the sources of an exposure, we often work from a factor report (say, sensitivity of an options portfolio to changes in the volatility smile) to a bucket report (say, option notional by strike and maturity) to a transaction list (transactions by strike and maturity).

Hidden exposures The exposures that are not captured at all by the risk-monitoring systems are the ones that are most likely to lose risk managers their jobs. Part of the art of risk management is deciding what exposures to monitor and aggregate through the organization, and what exposures to omit from this process. Risk managers must establish criteria to determine when exposures must be included in the monitoring process, and must establish a regular review to monitor whether exposures meet the criteria. We will point out a few exposures, which are often not monitored, but may in fact be significant: Ω Swaps portfolios: the basis between Eurodollar money market rates, and the rates implied by Eurodollar futures; and the basis between 3M Libor and other floating rate frequencies (1M Libor, 6M Libor and 12M Libor). Ω Options portfolios: the volatility smile. Ω Long-dated option portfolios: interest rates, and borrowing costs for the underlying (i.e. repo rates for bond options, dividend and stock loan rates for equity options, metal borrowing rates for bullion options, . . .) Positions that are not entered in the risk monitoring systems at all are a major source of problems. Typically, new products are first valued and risk managed in spreadsheets, before inclusion in the production systems. One approach is to require that even spreadsheet systems adhere to a firm-wide risk-reporting interface and that spreadsheet systems hand off the exposures of the positions they contain to the

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production systems. A second alternative is to limit the maximum exposure that may be assumed while the positions are managed in spreadsheets. Risk managers have to balance the potential for loss due to inadequate monitoring with the potential loss of revenue from turning away transactions simply because the transactions would have to be managed in spreadsheets (or some other non-production system). In general, trading portfolios have gross exposures that are much larger than the net exposures. Risk managers have to take a hard look at the process by which exposures are netted against each other, to ensure that significant risks are not being masked by the netting process.

Market parameter estimation As we will see below, risk-aggregation methods rely on assumptions about the distributions of market prices and estimates of the volatility and correlation of market prices. These assumptions quantify the uncertainty in market prices. When we attempt to model the market, we are modeling human not physical behavior (because market prices are set by the interaction of traders). Human behavior isn’t always rational, or consistent, and changes over time. While we can apply techniques from the physical sciences to modeling the market, we have to remember that our calculations may be made completely inaccurate by changes in human behavior. The good news is that we are not trying to build valuation models for all the products in our portfolio, we are just trying to get some measure of the risk of those products over a relatively short time horizon. The bad news is that we are trying to model rare events – what happens in the tails of the probability distribution. By definition, we have a lot less information about past rare events than about past common events.

Choice of distribution To begin, we have to choose a probability distribution for changes in market prices – usually either the normal or the log-normal distribution. We also usually assume that the changes in one period are independent of the changes in the previous period, and finally we assume that the properties of the probability distribution are constant over time. There is a large body of research on non-parametric estimation of the distributions of market prices. Non-parametric methods are techniques for extracting the real-world probability distribution from large quantities of observed data, without making many assumptions about the actual distribution beforehand. The research typically shows that market prices are best described by complex mixtures of many different distributions that have changing properties over time (Ait-Sahalia, 1996, 1997; Wilmot et al., 1995). That said, we use the normal distribution, not because it’s a great fit to the data or the market, but because we can scale variance over time easily (which means we can translate VaR to a different time horizon easily), because we can calculate the variance of linear sums of normal variables easily (which means we can add risks easily), and because we can calculate the moments of functions of normal variables easily (which means we can approximate the behavior of some other distributions easily)!

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Figure 4.7 Normally and log-normally distributed variables.

As its name suggests, the log-normal distribution is closely related to the normal distribution. Figure 4.7 shows the familiar ‘bell curve’ shape of the probability density of a normally distributed variable with a mean of 100 and a standard deviation of 40. When we use the normal distribution to describe prices, we assume that up-anddown moves of a certain absolute size are equally likely. If the moves are large enough, the price can become negative, which is unrealistic if we are describing a stock price or a bond price. To avoid this problem, we can use the log-normal distribution. The log-normal distribution assumes that proportional moves in the stock price are equally likely.6 Looking at Figure 4.7 we see that, compared to the normal distribution, for the log-normal distribution the range of lower prices is compressed, and the range of higher prices is enlarged. In the middle of the price range the two distributions are very similar. One other note about the log-normal distribution – market participants usually refer to the standard deviation of a lognormal variable as the variable’s volatility. Volatility is typically quoted as a percentage change per annum. To investigate the actual distribution of rates we can use standardized variables. To standardize a variable we take each of the original values in turn, subtracting the average value of the whole data set and dividing through by the standard deviation of the whole data set: Standard variable valueó(original variable valueñaverage of original variable)/ standard deviation of original variable After standardization the mean of the standard variable is 0, and the standard deviation of the new variable is 1. If we want to compare different data sets, it is much easier to see the differences between the data sets if they all have the same mean and standard deviation to start with. We can do the comparisons by looking at probability density plots. Figure 4.8 shows the frequency of observations of market changes for four years

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Figure 4.8 Empirical FX data compared with the normal and log-normal distributions.

of actual foreign exchange rate data (DEM–USD, JPY–USD and GBP–USD), overlaid on the normal distribution. On the left-hand side of the figure the absolute rate changes are plotted, while on the right-hand side the changes in the logarithms of the rate are plotted. If foreign exchange rates were normally distributed, the colored points on the left-hand side of the figure would plot on top of the smooth black line representing the normal distribution. If foreign exchange rates were log-normally distributed, the colored points in the right-hand side of the figure would plot on top of the normal distribution. In fact, it’s quite hard to tell the difference between the two figures: neither the normal nor the log-normal distribution does a great job of matching the actual data. Both distributions fail to predict the frequency of large moves in the actual data – the ‘fat tails’ or leptokurtosis of most financial variables. For our purposes, either distribution assumption will do. We recommend using whatever distribution makes the calculations easiest, or is most politically acceptable to the organization! We could use other alternatives to capture the ‘fat tails’ in the actual data: such as the T-distribution; the distribution implied from the option volatility smile; or a mixture of two normal distributions (one regular, one fat). These alternatives are almost certainly not worth the additional effort required to implement them, compared to the simpler approach of using the results from the normal or log-normal distribution and scaling them (i.e. multiplying them by a fudge factor so the results fit the tails of the actual data better). Remember that we are really only concerned with accurately modeling the tails of the distribution at our chosen level of confidence, not the whole distribution.

Volatility and correlation estimation Having chosen a distribution, we have to estimate the parameters of the distribution. In modeling the uncertainty of market prices, we usually focus on estimating the standard deviations (or volatilities) of market prices, and the correlations of pairs of market prices. We assume the first moment of the distribution is zero (which is

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reasonable in most cases for a short time horizon), and we ignore higher moments than the second moment, the standard deviation. One consequence of using standard deviation as a measure of uncertainty is that we don’t take into account asymmetry in the distribution of market variables. Skewness in the price distribution may increase or decrease risk depending on the sign of the skewness and whether our exposure is long or short. Later, when we discuss risk aggregation methods, we’ll see that some of the methods do account for the higher moments of the price distribution.

Historical estimates One obvious starting point is to estimate the parameters of the distribution from historical market data, and then apply those parameters to a forward-looking analysis of VaR (i.e. we assume that past market behavior can tell us something about the future). Observation period and weighting First, we have to decide how much of the historical market data we wish to consider for our estimate of the parameters of the distribution. Let’s suppose we want to use 2 months’ of daily data. One way to achieve this is to plug 2 months’ data into a standard deviation calculation. Implicitly, what we are doing is giving equal weight to the recent data and the data from two months ago. Alternatively, we may choose to give recent observations more weight in the standard deviation calculation than data from the past. If recent observations include larger moves than most of the data, then the standard deviation estimate will be higher, and if recent observations include smaller moves than most of the data, the standard deviation estimate will be lower. These effects on our standard deviation estimate have some intuitive appeal. If we use 2 months of equally weighted data, the weighted-average maturity of the data is 1 month. To maintain the same average maturity while giving more weight to more recent data, we have to sample data from more than 2 months. We can demonstrate this with a concrete example using the exponential weighting scheme. In exponential weighting, the weight of each value in the data, working back from today, is equal to the previous value’s weight multiplied by a decay factor. So if the decay factor is 0.97, the weight for today’s value in the standard deviation calculation is 0.970 ó1, the weight of yesterday’s value in the standard deviation is 0.971 ó0.97, the weight of previous day’s value in the standard deviation is 0.972 ó0.9409, and so on. Figure 4.9 shows a graph of the cumulative weight of n days, values, working back from today, and Table 4.3 shows the value of n for cumulative weights of 25%, 50%, 75% and 99%. Twenty-two business days (about 1 calendar month) contribute 50% of the weight of the data, so the weighted-average maturity of the data is 1 month. In contrast to the equally weighted data, where we needed 2 months of data, now we need 151 business days or over 7 months of data to calculate our standard deviation, given that we are prepared to cut off data that would contribute less than 1% to the total weight. Exponential weighting schemes are very popular, but while they use more data for the standard deviation calculation than unweighted schemes, it’s important to note that they effectively sample much less data – a quarter of the weight is contributed by the first nine observations, and half the weight by 22 observations. Our decay factor of 0.97 is towards the high end of the range of values used, and lower decay factors sample even less data.

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Figure 4.9 Cumulative weight of n observations for a decay factor of 0.97.

Table 4.3 Cumulative weight for a given number of observations. Weight 25% 50% 75% 99%

Observations 9 22 45 151

GARCH estimates GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) models of standard deviation can be thought of as a more complicated version of the weighting schemes described above, where the weighting factor is determined by the data itself. In a GARCH model the standard deviation today depends on the standard deviation yesterday, and the size of the change in market prices yesterday. The model tells us how a large move in prices today affects the likelihood of there being another large move in prices tomorrow.

Estimates implied from market prices Readers familiar with option valuation will know that the formulae used to value options take the uncertainty in market prices as input. Conversely, given the price of an option, we can imply the uncertainty in market prices that the trader used to value the option. If we believe that option traders are better at predicting future uncertainty in prices than estimates from historical data, we can use parameters

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implied from the market prices of options in our VaR models. One drawback of this approach is that factors other than trader’s volatility estimates, such as market liquidity and the balance of market supply and demand, may be reflected in market prices for options.

Research on different estimation procedures Hendricks (1996) studies the performance of equally and exponentially weighted estimators of volatility for a number of different sample sizes in two different VaR methods. His results indicate that there is very little to choose between the different estimators. Boudoukh et al. (1997) study the efficiency of several different weighting schemes for volatility estimation. The ‘winner’ is non-parametric multivariate density estimation (MDE). MDE puts high weight on observations that occur under conditions similar to the current date. Naturally this requires an appropriate choice of state variables to describe the market conditions. The authors use yield curve level and slope when studying Treasury bill yield volatility. MDE does not seem to represent a huge forecasting improvement given the increased complexity of the estimation method but it is interesting that we can formalize the concept of using only representative data for parameter estimation. Naive use of a delta normal approach requires estimating and handling very large covariance matrices. Alexander and Leigh (1997) advocate a divide-and-conquer strategy to volatility and correlation estimation: break down the risk factors into a sets of highly correlated factors; then perform principal components analysis to create a set of orthogonal risk factors; then estimate variances of the orthogonal factors and covariances of the principal components. Alexander and Leigh also conclude from backtesting that there is little to choose between the regulatory-year equally weighted model and GARCH(1,1), while the RiskMetricsTM exponentially weighted estimator performs less well.

BIS quantitative requirements The BIS requires a minimum weighted average maturity of the historical data used to estimate volatility of 6 months, which corresponds to a historical observation period of at least 1 year for equally weighted data. The volatility data must be updated at least quarterly: and more often if market conditions warrant it. Much of the literature on volatility estimation recommends much shorter observation periods7 but these are probably more appropriate for volatility traders (i.e. option book runners) than risk managers. When we use a short observation period, a couple of weeks of quiet markets will significantly reduce our volatility estimate. It is hard to see that the market has really become a less risky place, just because it’s been quiet for a while. Another advantage of using a relatively long observation period, and revising volatilities infrequently, is that the units of risk don’t change from day to day – just the position. This makes it easier for traders and risk managers to understand why their VaR has changed.

Beta estimation Looking back at our equation for the return on a stock, we see that it is the equation of a straight line, where b represents the slope of the line we would plot through the

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scatter graph of stock returns plotted on the y-axis, against market returns plotted on the x-axis. Most of the literature on beta estimation comes from investment analysis, where regression analysis of stock returns on market returns is performed using estimation periods that are extremely long (decades) compared to our risk analysis horizon. While betas show a tendency to revert to towards 1, the impact of such reversion over the risk analysis horizon is probably negligible. One criticism of historical estimates of beta is that they do not respond quickly to changes in the operating environment – or capital structure – of the firm. One alternative is for the risk manager to estimate beta from regression of stock returns on accounting measures such as earnings variability, leverage, and dividend payout, or to monitor these accounting measures as an ‘early-warning system’ for changes, not yet reflected in historical prices, that may impact the stock’s beta in the future.

Yield curve estimation We discussed yield curve construction and interpolation in Chapter 3. Risk managers must have detailed knowledge of yield curve construction and interpolation to assess model risk in the portfolio mark to market, to understand the information in exposure reports and to be able to test the information’s integrity. However, for risk aggregation, the construction and interpolation methods are of secondary importance, as is the choice of whether risk exposures are reported for cash yields, zero coupon yields or forward yields. As we will emphasize later, VaR measurement is not very accurate, so we shouldn’t spend huge resources trying to make the VaR very precise. Suppose we collect risk exposures by perturbing cash instrument yields, but we have estimated market uncertainty analysing zero coupon yields. It’s acceptable for us to use the two somewhat inconsistent sets of information together in a VaR calculation – as long as we understand what we have done, and have estimated the error introduced by what we have done.

Risk-aggregation methods We have described how we measure our exposure to market uncertainty and how we estimate the uncertainty itself, and now we will describe the different ways we calculate the risks and add up the risks to get a VaR number.

Factor push VaR As its name implies, in this method we simply push each market price in the direction that produces the maximum adverse impact on the portfolio for that market price. The desired confidence level and horizon determine the amount the price is pushed.

Conﬁdence level and standard deviations Let’s assume we are using the 99% confidence level mandated by the BIS. We need to translate that confidence level into a number of standard deviations by which we will push our risk factor.

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Figure 4.10 Cumulative normal probability density.

Figure 4.10 shows the cumulative probability density for the normal distribution as a function of the number of standard deviations. The graph was plotted in Microsoft ExcelTM, using the cumulative probability density function, NORMSDIST (NumberOfStandardDeviations). Using this function, or reading from the graph, we can see that one standard deviation corresponds to a confidence level of 84.1%, and two standard deviations correspond to a confidence level of 97.7%. Working back the other way, we can use the ExcelTM NORMSINV(ConfidenceLevel) function to tell us how many standard deviations correspond to a particular confidence level. For example, a 95% confidence level corresponds to 1.64 standard deviations, and a 99% confidence level to 2.33 standard deviations.

Growth of uncertainty over time Now we know that for a 99% confidence interval, we have to push the risk factor 2.33 standard deviations. However, we also have to scale our standard deviation to the appropriate horizon period for the VaR measurement. Let’s assume we are using a 1-day horizon. For a normally distributed variable, uncertainty grows with the square root of time. Figure 4.11 shows the growth in uncertainty over time of an interest rate with a standard deviation of 100 bps per annum,8 for three different confidence levels (84.1% or one standard deviation, 95% or 1.64 standard deviations, 99% or 2.33 standard deviations). Table 4.4 shows a table of the same information. Standard deviations are usually quoted on an annual basis. We usually assume that all the changes in market prices occur on business days – the days when all the markets and exchanges are open and trading can occur. There are approximately 250 business days in the year.9 To convert an annual (250-day) standard deviation to a 1-day standard deviation we multiply by Y(1/250), which is approximately 1/16.

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Figure 4.11 Growth in uncertainty over time.

Table 4.4 Growth of uncertainty over time (annual standard deviation 1%) Horizon Tenor

Days

Conﬁdence 84.10% STDs 1.00

95.00% 1.64

99.00% 2.33

1D 2W 1M 3M 6M 9M 1Y

1 10 21 62.5 125 187.5 250

0.06% 0.20% 0.29% 0.50% 0.71% 0.87% 1.00%

0.10% 0.33% 0.48% 0.82% 1.16% 1.42% 1.64%

0.15% 0.47% 0.67% 1.16% 1.64% 2.01% 2.33%

So, if we wanted to convert an interest rate standard deviation of about 100 bps per annum to a daily basis, we would divide 100 by 16 and get about 6 bps per day. So, we can calculate our interest rate factor push as a standard deviation of 6 bps per day, multiplied by the number of standard deviations for a 99% confidence interval of 2.33, to get a factor push of around 14 bps. Once we know how to calculate the size of the push, we can push each market variable to its worst value, calculate the impact on the portfolio value, and add each of the individual results for each factor up to obtain our VaR: FactorPushVaRóSum of ABS(SingleFactorVaR) for all risk factors, where SingleFactorVaRóExposure*MaximumLikelyAdverseMove MaximumLikelyAdverseMoveóNumberOfStandardDeviations*StandardDeviation* YHorizon, and ABS(x) means the absolute value of x

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The drawback of this approach is that it does not take into account the correlations between different risk factors. Factor push will usually overestimate VaR10 because it does not take into account any diversification of risk. In the real world, risk factors are not all perfectly correlated, so they will not all move, at the same time, in the worst possible direction, by the same number of standard deviations.

FX example Suppose that we bought 1 million DEM and sold 73.6 million JPY for spot settlement. Suppose that the daily standard deviations of the absolute exchange rates are 0.00417 for USD–DEM and 0.0000729 for USD–JPY (note that both the standard deviations were calculated on the exchange rate expressed as USD per unit of foreign currency). Then the 95% confidence interval, 1-day single factor VaRs are: 1 000 000*1.64*0.00417*Y1ó6863 USD for the DEM position ñ73 594,191*1.64*0.0000729*Y1óñ8821 USD for the JPY position The total VaR using the Factor Push methodology is the sum of the absolute values of the single factor VaRs, or 6863 USD plus 8821 USD, which equals 15 683 USD. As we mentioned previously, this total VaR takes no account of the fact that USD–DEM and USD–JPY foreign exchange rates are correlated, and so a long position in one currency and a short position in the other currency will have much less risk than the sum of the two exposures.

Bond example Suppose on 22 May 1998 we have a liability equivalent in duration terms to $100 million of the current 10-year US Treasury notes. Rather than simply buying 10year notes to match our liability, we see value in the 2-year note and 30-year bond, and so we buy an amount of 2-year notes and 30-year bonds that costs the same amount to purchase as, and matches the exposure of, the 10-year notes. Table 4.5 shows the portfolio holdings and duration, while Table 4.6 shows the exposure to each yield curve factor, using the data from Table 4.2 for the annualized change in yield at each maturity, for each factor. Table 4.5 Example bond portfolio’s holdings and duration Holding (millions) Liability: 10-year note Assets: 2-year note 30-year bond

100 44 54.25

Coupon

Maturity

Price

Yield

Modiﬁed duration

v01

5 5/8

5/15/08

99-29

5.641

7.56

75 483

5 5/8 6 1/8

4/30/00 11/15/18

100-01 103-00

5.605 5.909

1.81 13.79

7 948 77 060

Total

85 008

To convert the net exposure numbers in each factor to 95% confidence level, 1-day VaRs, we multiply by 1.64 standard deviations to get a move corresponding to 95% confidence and divide by 16 to convert the annual yield curve changes to daily changes. Note that the net exposure to the first factor is essentially zero. This is

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Change in market value Factor 1 Shift

Factor 2 Tilt

Factor 3 Bend

Liability: 10-year note 100

104

33

ñ8

(7 850 242) (2 490 942)

603 865

Assets: 2-year note 30-year bond

125 89

ñ3 34

19 ñ24

(933 497) 23 844 (6 858 310) (2 620 028)

(151 012) 1 849 432

Total

(7 851 807) (2 596 184)

1 698 420

44 54.25

Net exposure Single factor VaR

1 565 162

105 242 10 916

(1 094 556) (113 530)

because we choose the amounts of the 2-year note and 30-year bond to make this so.11 Our total factor push VaR is simply the sum of the absolute values of the single factor VaRs: 162ò10 916ò113 530ó124 608 USD.

Covariance approach Now we can move on to VaR methods that do, either explicitly or implicitly, take into account the correlation between risk factors.

Delta-normal VaR The standard RiskMetricsTM methodology measures positions by reducing all transactions to cash flow maps. The volatility of the returns of these cash flows is assumed to be normal, i.e. the cash flows each follow a log-normal random walk. The change in the value of the cash flow is then approximated as the product of the cash flow and the return (i.e. using the first term of a Taylor series expansion of the change in value of a log-normal random variable, e x ). Cash flow mapping can be quite laborious and does not extend to other risks beyond price and interest rate sensitivities. The Delta-normal methodology is a slightly more general flavor of the standard RiskMetrics methodology, which considers risk factors rather than cash flow maps as a measure of exposure. The risk factors usually correspond to standard trading system sensitivity outputs (price risk, vega risk, yield curve risk), are assumed to follow a multivariate normal distribution and are all first derivatives. Therefore, the portfolio change in value is linear in the risk factors and the position in each factor and the math for VaR calculation looks identical to that for the RiskMetrics approach, even though the assumptions are rather different. Single risk factor delta-normal VaR Delta-normal VaR for a single risk factor is calculated the same way as for the factor push method.

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Multiple risk factor delta-normal VaR How do we add multiple single factor VaRs, taking correlation into account? For the two risk factor case: VaR Total óY(VaR 21 òVaR 22 ò2*o12*VaR 1*VaR 2), where o12 is the correlation between the first and second risk factors. For the three risk factor case: VaR Total óY(VaR 21 òVaR 22 òVaR 23 ò2*o12*VaR 1*VaR 2 ò2*o13*VaR 1*VaR 3 ò2*o32* VaR 3*VaR 2), For n risk factors: VaR Total óY(&i &j oij*VaR i*VaR j )12 As the number of risk factors increases, the long hand calculation gets cumbersome, so we switch to using matrix notation. In matrix form, the calculation is much more compact: VaR Total óY(V*C*V T ) where: V is the row matrix of n single-factor VaRs, one for each risk factor C is the n by n correlation matrix between each risk factor and T denotes the matrix Transpose operation ExcelTM has the MMULT() formula for matrix multiplication, and the TRANSPOSE() formula to transpose a matrix, so – given the input data – we can calculate VaR in a single cell, using the formula above.13 The essence of what we are doing when we use this formula is adding two or more quantities that have a magnitude and a direction, i.e. adding vectors. Suppose we have two exposures, one with a VaR of 300 USD, and one with a VaR of 400 USD. Figure 4.12 illustrates adding the two VaRs for three different correlation coefficients. A correlation coefficient of 1 (perfect correlation of the risk factors) implies that the VaR vectors point in the same direction, and that we can just perform simple addition to get the total VaR of 300ò400ó700 USD. A correlation coefficient of 0 (no correlation of the risk factors) implies that the VaR vectors point at right

Figure 4.12 VaR calculation as vector addition.

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angles to each other, and that we can use Pythagoras’s theorem to calculate the length of the hypotenuse, which corresponds to our total VaR. The total VaR is the square root of the sum of the squares of 300 and 400, which gives a total VaR of Y(3002 ò4002)ó500 USD. A correlation coefficient of ñ1 (perfect inverse correlation of the risk factors) implies that the VaR vectors point in the opposite direction, and we can just perform simple subtraction to get the total VaR of 300 – 400óñ100 USD. We can repeat the same exercise, of adding two VaRs for three different correlation coefficients, using the matrix math that we introduced on the previous page. Equations (4.1)– (4.3) show the results of working through the matrix math in each case. Perfectly correlated risk factors VaRó

ó

(300 400)

1 1 1 1

300 400

(300.1ò400.1 300.1ò400.1)

300 400

ó (700.300ò700.400)

(4.1)

ó (210 000ò280 000) ó (490 000) ó700 Completely uncorrelated risk factors VaRó

ó

(300 400)

1 0 0 1

300 400

(300.1ò400.0 300.0ò400.1)

ó (300.300ò400.400)

(4.2)

ó (90 000ò160 000) ó (250 000) ó500 Perfectly inversely correlated risk factors VaRó

(300 400)

1 ñ1 ñ1 1

300 400

300 400

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ó

(300.1ò400.ñ1 300.ñ1ò400.1)

141

300 400

ó (ñ100.300ò100.400)

(4.3)

ó (ñ30 000ò40 000) ó (10 000) ó100 Naturally in real examples the correlations will not be 1,0 or ñ1, but the calculations flow through the matrix mathematics in exactly the same way. Now we can recalculate the total VaR for our FX and bond examples, this time using the deltanormal approach to incorporate the impact of correlation. FX example The correlation between USD–DEM and USD–JPY for our sample data is 0.063. Equation (4.4) shows that the total VaR for the delta-normal approach is 7235 USD – more than the single-factor VaR for the DEM exposure but actually less than the single-factor VaR for the JPY exposure, and about half the factor push VaR.

VaRó

ó

(6863 ñ8821)

1 0.603 0.603 1

6863 ñ8821

(6863.1òñ8821.0.603 6863.0.603òñ8821.1)

ó (1543.6863òñ4682.ñ8921)

6863 ñ8821

(4.4)

ó (10 589 609ò41 768 122) ó 52 357 731) ó 7235 Bond example By construction, our yield curve factors are completely uncorrelated. Equation (4.5) shows that the total VaR for the delta-normal approach is 114 053 USD – more than any of the single-factor VaRs, and less than total factor push VaR.

VaRó

1 0 0

(162 10 916 ñ113 530) 0 1 0 0 0 1

162 10 916

ñ113 530

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ó

(162.1ò10 916.0òñ113 530.0 162.0ò10 916.1òñ113 530.0

162.0ò10 916.0òñ113 530.1)

162 10 916

ñ113 530

ó (162.162ò10 916.10 916òñ113 530.ñ113 530)

(4.5)

ó (26 244ò119 159 056ò12 889 060 900) ó (13 008 246 200) ó114 053 The bond example shows that there are indirect benefits from the infrastructure that we used to calculate our VaR. Using yield curve factors for our exposure analysis also helps identify the yield curve views implied by the hedge strategy, namely that we have a significant exposure to a bend in the yield curve. In general, a VaR system can be programmed to calculate the implied views of a portfolio, and the best hedge for a portfolio. Assuming that the exposure of a position can be captured entirely by first derivatives is inappropriate for portfolios containing significant quantities of options. The following sections describe various ways to improve on this assumption.

Delta-gamma VaR There are two different VaR methodologies that are called ‘delta-gamma VaR’. In both cases, the portfolio sensitivity is described by first and second derivatives with respect to risk factors. Tom Wilson (1996) works directly with normally distributed risk factors and a second-order Taylor series expansion of the portfolio’s change in value. He proposes three different solution techniques to calculate VaR, two of which require numerical searches. The third method is an analytic solution that is relatively straightforward, and which we will describe here. The gamma of a set of N risk factors can be represented by an NîN matrix, known as the Hessian. The matrix diagonal is composed of second derivatives – what most people understand by gamma. The offdiagonal or cross-terms describe the sensitivities of the portfolio to joint changes in a pair of risk factors. For example, a yield curve moves together with a change in volatility. Wilson transforms the risk factors to orthogonal risk factors. The transformed gamma matrix has no cross-terms – the impact of the transformed risk factors on the portfolio change in value is independent – so the sum of the worstcase change in value for each transformed risk factor will also be the worst-case risk for the portfolio. Wilson then calculates an adjusted delta that gives the same worstcase change in value for the market move corresponding to the confidence level as the original delta and the original gamma.

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Figure 4.13 Delta-gamma method.

Figure 4.13 shows the portfolio change in value for a single transformed risk factor as a black line. The delta of the portfolio is the grey line, the tangent to the black line at the origin of the figure, which can be projected out to the appropriate number of standard deviations to calculate a delta-normal VaR. The adjusted delta of the portfolio is the dotted line, which can be used to calculate the delta-gamma VaR. The adjusted delta is a straight line from the origin to the worst-case change in value for the appropriate number of standard deviations,14 where the straight line crosses the curve representing the actual portfolio change in value. Given this picture, we can infer that the delta-gamma VaR is correct only for a specified confidence interval and cannot be rescaled to a different confidence interval like a delta-normal VaR number. The adjusted delta will typically be different for a long and a short position in the same object. An ad-hoc version of this approach can be applied to untransformed risk factors – provided the cross-terms in the gamma matrix are small. To make things even simpler, we can require the systems generating delta information for risk measurement to do so by perturbing market rates by an amount close to the move implied by the confidence interval and then feed this number into our delta-normal VaR calculation. RiskMetrics15 takes a very different approach to extending the delta-normal framework. The risk factor delta and gamma are used to calculate the first four moments of the portfolio’s return distribution. A function of the normal distribution is chosen to match these moments. The percentile for the normal distribution can then be transformed to the percentile for the actual return distribution. If this sounds very complicated, think of the way we calculate the impact of a 99th percentile/2.33 standard deviation move in a log-normally distributed variable. We multiply the

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volatility by 2.33 to get the change in the normal variable, and then multiply the spot price by e change to get the up move and divide by e change to get the down move. This is essentially the same process. (Hull and White (1998) propose using the same approach for a slightly different problem.) Now let’s see how we can improve on the risk factor distribution and portfolio change in value assumptions we have used in delta-normal and delta-gamma VaR.

Historical simulation VaR So far we have assumed our risk factors are either normally or log-normally distributed. As we saw in our plots of foreign exchange rates, the distribution of real market data is not that close to either a normal or a log-normal distribution. In fact, some market data (electricity prices are a good example) has a distribution that is completely unlike either a normal or log-normal distribution. Suppose that instead of modeling the market data, we just ‘replayed the tape’ of past market moves? This process is called historical simulation. While the problems of modeling and estimating parameters for the risk factors are eliminated by historical simulation, we are obviously sensitive to whether the historic time series data we use is representative of the market, and captures the features we want – whether the features are fat tails, skewness, non-stationary volatilities or the presence of extreme events. We can break historical simulation down into three steps: generating a set of historical changes in our risk factors, calculating the change in portfolio value for each historical change, and calculating the VaR. Let’s assume that we are going to measure the changes in the risk factors over the same period as our VaR horizon – typically 1 day. While we said that we were eliminating modeling, we do have to decide whether to store the changes as absolute or percentage changes. If the overall level of a risk factor has changed significantly over the period sampled for the simulation, then we will have some sensitivity to the choice.16 Naturally, the absence of a historical time series for a risk factor you want to include in your analysis is a problem! For instance, volatility time series for OTC (over-the-counter) options are difficult to obtain (we usually have to go cap in hand to our option brokers) and entry into a new market for an instrument that has not been traded for long requires some method of ‘back-filling’ the missing data for the period prior to the start of trading. Next, we have to calculate the change in portfolio value. Starting from today’s market data, we apply one period’s historical changes to get new values for all our market data. We can then calculate the change in portfolio value by completely revaluing the whole portfolio, by using the sensitivities to each risk factor (delta, gamma, vega, . . .) in a Taylor series or by interpolating into precalculated sensitivity tables (a half-way house between full revaluation and risk factors). We then repeat the process applying the next period’s changes to today’s market data. Full revaluation addresses the problem of using only local measures of risk, but requires a huge calculation resource relative to using factor sensitivities.17 The space required for storing all the historical data may also be significant, but note that the time series takes up less data than the covariance matrix if the number of risk factors is more than twice the number of observations in the sample (Benson and Zangari, 1997). Finally, we have to calculate the VaR. One attractive feature of historical simulation

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is that we have the whole distribution of the portfolio’s change in value. We can either just look up the required percentile in the table of the simulation results, or we can model the distribution of the portfolio change and infer the change in value at the appropriate confidence interval from the distribution’s properties (Zangari, 1997; Butler and Schachter, 1996). Using the whole distribution uses information from all the observations to make inference about the tails, which may be an advantage for a small sample, and also allows us to project the behavior of the portfolio for changes that are larger than any changes that have been seen in our historic data.

FX example Suppose we repeat the VaR calculation for our DEM–JPY position, this time using the actual changes in FX rates and their impact on the portfolio value. For each day, and each currency, we multiply the change in FX rate by the cash flow in the currency. Then, we look up the nth smallest or largest change in value, using the ExcelTM SMALL() and LARGE() functions, where n is the total number of observations multiplied by (1-confidence interval). Table 4.7 FX example of historical simulation VaR versus analytic VaR Conﬁdence

Method

DEM

JPY

DEM–JPY

95%

AnalyticVaR SimulationVaR SimulationVaR/AnalyticVaR

6 863 6 740 98.2%

(8 821) (8 736) 99.0%

7 196 (7 162) 99.5%

99%

AnalyticVaR SimulationVaR SimulationVaR/AnalyticVaR

9 706 12 696 130.8%

(12 476) (16 077) 128.9%

10 178 (12 186) 119.7%

Table 4.7 shows the results of the analytic and historical simulation calculations. The simulation VaR is less than the analytic VaR for 95% confidence and below, and greater than the analytic VaR for 99% confidence and above. If we had shown results for long and short positions, they would not be equal.18

Monte Carlo simulation VaR Monte Carlo VaR replaces the first step of historical simulation VaR: generating a set of historic changes in our risk factors. Monte Carlo VaR uses a model, fed by a set of random variables, to generate complete paths for all risk factor changes from today to the VaR horizon date. Each simulation path provides all the market data required for revaluing the whole portfolio. For a barrier FX option, each simulation path would provide the final foreign exchange rate, the final foreign exchange rate volatility, and the path of exchange rates and interest rates. We could then determine whether the option had been ‘knocked-out’ at its barrier between today and the horizon date, and the value of the option at the horizon date if it survived. The portfolio values (one for each path) can then be used to infer the VaR as described for historical simulation. Creating a model for the joint evolution of all the risk factors that affect a bank’s portfolio is a massive undertaking that is almost certainly a hopeless task for any

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institution that does not already have similar technology tried and tested in the front office for portfolio valuation and risk analysis.19 This approach is also at least as computationally intensive as historical simulation with full revaluation, if not more so. Both Monte Carlo simulation and historical simulation suffer from the fact that the VaR requires a large number of simulations or paths before the value converges towards a single number. The potential errors in VaR due to convergence decrease with the square of the number of Monte Carlo paths – so we have to run four times as many paths to cut the size of the error by a factor of 2. While, in principle, Monte Carlo simulation can address both the simplifying assumptions we had to make for other methods in modeling the market and in representing the portfolio, it is naive to expect that most implementations will actually achieve these goals. Monte Carlo is used much more frequently as a research tool than as part of the production platform in financial applications, except possibly for mortgage-backed securities (MBS).

Current practice We have already discussed the assumptions behind VaR. As with any model, we must understand the sensitivity of our VaR model to the quality of its inputs. In a perfect world we would also have implemented more than one model and have reconciled the difference between the models’ results. In practice, this usually only happens as we refine our current model and try to understand the impact of each round of changes from old to new. Beder (1995) shows a range of VaR calculations of 14 times for the same portfolio using a range of models – although the example is a little artificial as it includes calculations based on two different time horizons. In a more recent regulatory survey of Australian banks, Gizycki and Hereford (1998) report an even larger range (more than 21 times) of VaR values, though they note that ‘crude, but conservative’ assumptions cause outliers at the high end of the range. Gizycki and Hereford also report the frequency with which the various approaches are being used: 12 Delta-Normal Variance–Covariance, 5 Historical Simulation, 3 Monte Carlo, 1 Delta-Normal Variance–Covariance and Historical Simulation. The current best practice in the industry is historical simulation, using factor sensitivities, while participants are moving towards historical simulation, using full revaluation, or Monte Carlo. Note that most implementations study the terminal probabilities of events, not barrier probabilities. Consider the possibility of the loss event happening at any time over the next 24 hours rather than the probability of the event happening when observed at a single time, after 24 hours have passed. Naturally, the probability of exceeding a certain loss level at any time over the next 24 hours is higher than the probability of exceeding a certain loss level at the end of 24 hours. This problem in handling time is similar to the problem of using a small number of terms in the Taylor series expansion of a portfolio’s P/L function. Both have the effect of masking large potential losses inside the measurement boundaries. The BIS regulatory multiplier (Stahl, 1997; Hendricks and Hirtle, 1998) takes the VaR number we first calculated and multiplies it by at least three – and more if the regulator deems necessary – to arrive at the required regulatory capital. Even though this goes a long way to addressing the modeling uncertainties in VaR, we would still not recommend VaR as a measure of downside on its own. Best practice requires

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that we establish market risk reserves (Group of Thirty, 1993) and model risk reserves (Beder, 1995). Model risk reserves should include coverage for potential losses that relate to risk factors that are not captured by the modeling process and/or the VaR process. Whether such reserves should be included in VaR is open to debate.20

Robust VaR Just how robust is VaR? In most financial applications we choose fairly simple models and then abuse the input data outside the model to fit the market. We also build a set of rules about when the model output is likely to be invalid. VaR is no different. As an example, consider the Black–Scholes–Merton (BSM) option pricing model: one way we abuse the model is by varying the volatility according to the strike. We then add a rule not to sell very low delta options at the model value because even with a steep volatility smile we just can’t get the model to charge enough to make it worth our while to sell these options. A second BSM analogy is the modeling of stochastic volatility by averaging two BSM values, one calculated using market volatility plus a perturbation, and one using market volatility minus a perturbation, rather than building a more complicated model which allows volatility to change from its initial value over time. Given the uncertainties in the input parameters (with respect to position, liquidation strategy, time horizon and market model) and the potential mis-specification of the model itself, we can estimate the uncertainty in the VaR. This can either be done formally, to be quoted on our risk reports whenever the VaR value is quoted, or informally, to determine when we should flag the VaR value because it is extremely sensitive to the input parameters or to the model itself. Here is a simple analysis of errors for single risk factor VaR. Single Risk factor VaR is given by Exposure*NumberOfStandardDeviations*StandardDeviation*YHorizon. If the exposure is off by 15% and the standard deviation is off by 10% then relative error of VaR is 15ò10ó25%! Note that this error estimate excludes the problems of the model itself. The size of the error estimate does not indicate that VaR is meaningless – just that we should exercise some caution in interpreting the values that our models produce.

Speciﬁc risk The concept of specific risk is fairly simple. For any instrument or portfolio of instruments for which we have modeled the general market risk, we can determine a residual risk that is the difference between the actual change in value and that explained by our model of general market risk. Incorporating specific risk in VaR is a current industry focus, but in practice, most participants use the BIS regulatory framework to calculate specific risk, and that is what we describe below.

Interest rate speciﬁc risk model The BIS specific risk charge is intended to ‘protect against adverse movement in the price of an individual security owing to factors relating to the individual issuer’. The charge is applied to the gross positions in trading book instruments – banks can

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only offset matched positions in the identical issue – weighted by the factors in Table 4.8. Table 4.8 BIS speciﬁc risk charges Issuer category Government Qualifying issuers: e.g. public sector entities, multilateral development banks and OECD banks

Other

Weighting factor 0% 3.125%

12.5% 20% 100%

Capital charge 0% 0.25% residual term to ﬁnal maturity \6M

1.0% residual term to ﬁnal maturity 6–24M 1.6% residual term to ﬁnal maturity 24Mò 8%

Interest rate and currency swaps, FRAs, forward foreign exchange contracts and interest rate futures are not subject to a specific risk charge. Futures contracts where the underlying is a debt security, are subject to charge according to the credit risk of the issuer.

Equity-speciﬁc risk model The BIS specific risk charge for equities is 8% of gross equity positions, unless the portfolio is ‘liquid and well-diversified’ according to the criteria of the national authorities, in which case the charge is 4%. The charge for equity index futures, forwards and options is 2%.

Concentration risk Diversification is one of the cornerstones of risk management. Just as professional gamblers limit their stakes on any one hand to a small fraction of their net worth, so they will not be ruined by a run of bad luck, so professional risk-taking enterprises must limit the concentration of their exposures to prevent any one event having a significant impact on their capital base. Concentrations may arise in a particular market, industry, region, tenor or trading strategy. Unfortunately, there is a natural tension between pursuit of an institution’s core competencies and comparative advantages into profitable market segments or niches, which produces concentration, and the desire for diversification of revenues and exposures.

Conclusion We can easily criticize the flaws in the VaR models implemented at our institutions, but the simplicity of the assumptions behind our VaR implementations is actually an asset that facilitates education of both senior and junior personnel in the organization, and helps us retain intuition about the models and their outputs. In fact, VaR models perform surprisingly well, given their simplicity. Creating the modeling, data, systems and intellectual infrastructure for firm-wide quantitative

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risk management is a huge undertaking. Successful implementation of even a simple VaR model is a considerable achievement and an ongoing challenge in the face of continually changing markets and products.

Acknowledgements Thanks to Lev Borodovsky, Randi Hawkins, Yong Li, Marc Lore, Christophe Rouvinez, Rob Samuel and Paul Vogt for encouragement, helpful suggestions and/or reviewing earlier drafts. The remaining mistakes are mine. Please email any questions or comments to [email protected] This chapter represents my personal opinions, and is supplied without any warranty of any kind.

Notes 1

Rather than saying ‘lose’, we should really say ‘see a change in value below the expected change of ’. 2 Here, and throughout this chapter, when we use price in this general sense, we take price to mean a price, or an interest rate or an index. 3 The classic reference on this topic is Elton and Gruber (1995). 4 Option traders like using Greek letters for exposure measures. Vega isn’t a Greek letter, but is does begin with a ‘V’, which is easy to remember for a measure of volatility exposure. Classically trained option traders use Kappa instead. 5 For instance, when the portfolio contains significant positions in options that are about to expire, or significant positions in exotic options. 6 Stricly speaking, changes in the logarithm of the price are normally distributed. 7 The Riskmetrics Group recommends a estimator based on daily observations with a decay factor of 0.94, and also provides a regulatory data set with monthly observations and a decay factor of 0.97 to meet the BIS requirements. Their data is updated daily. 8 To keep the examples simple, we have used absolute standard deviations for market variables throughout the examples. A percentage volatility can be converted to an absolute standard deviation by multiplying the volatility by the level of the market variable. For example, if interest rates are 5%, or 500 bps, and volatility is 20% per year, then the absolute standard deviation of interest rates is 500*0.2ó100 bps per year. 9 Take 365 calendar days, multiply by 5/7 to eliminate the weekends, and subtract 10 or so public holidays to get about 250 business days. 10 However, in some cases, factor push can underestimate VaR. 11 Note also that, because our factor model tells us that notes and bonds of different maturities experience yield curve changes of different amounts, the 2-year note and 30-year bond assets do not have the same duration and dollar sensitivity to an 01 bp shift (v01) as the 10year liability. Duration and v01 measure sensitivity to a parallel shift in the yield curve. 12 As a reference for the standard deviation of functions of random variables see Hogg and Craig (1978). 13 This is not intended to be an (unpaid!) advertisement for ExcelTM. ExcelTM simply happens to be the spreadsheet used by the authors, and we thought it would be helpful to provide some specific guidance on how to implement these calculations. 14 The worst-case change in value may occur for an up or a down move in the risk factor – in this case it’s for a down move. 15 RiskMetrics Technical Document, 4th edition, pp. 130–133 at http://www.riskmetrics.com/ rm/pubs/techdoc.html

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16

Consider the extreme case of a stock that has declined in price from $200 per share to $20 per share. If we chose to store absolute price changes, then we might have to try to apply a large decline from early in the time series, say $30, that is larger than today’s starting price for the stock! 17 In late 1998, most businesses could quite happily run programs to calculate their deltanormal VaR, delta-gamma VaR or historical simulation VaR using risk factor exposures on a single personal computer in under an hour. Historical simulation VaR using full revaluation would require many processors (more than 10, less than 100) working together (whether over a network or in a single multi-processor computer), to calculate historical simulation VaR using full revaluation within one night. In dollar terms, the PC hardware costs on the order of 5000 USD, while the multi-processor hardware costs on the order of 500 000 USD. 18 Assuming the antithetic variable variance reduction technique was not used. 19 The aphorism that springs to mind is ‘beautiful, but useless’. 20 Remember that VaR measures uncertainty in the portfolio P/L, and reserves are there to cover potential losses. Certain changes in the P/L or actual losses, even if not captured by the models used for revaluation, should be included in the mark to market of the portfolio as adjustments to P/L.

References Acar, E. and Prieve, D. (1927) ‘Expected minimum loss of financial returns’, Derivatives Week, 22 September. Ait-Sahalia, Y. (1996) ‘Testing continuous time models of the spot rate’, Review of Financial Studies, 2, No. 9, 385–426. Ait-Sahalia, Y. (1997) ‘Do interest rates really follow continuous-time Markov diffusions?’ Working Paper, Graduate School of Business, University of Chicago. Alexander, C. (1997) ‘Splicing methods for VaR’, Derivatives Week, June. Alexander, C. and Leigh, J. (1997) ‘On the covariance matrices used in VaR models’, Journal of Derivatives, Spring, 50–62. Artzner, P. et al. (1997) ‘Thinking coherently’, Risk, 10, No. 11. Basel Committee on Banking Supervision (1996) Amendment to the Capital Accord to incorporate market risks, January. http://www.bis.org/publ/bcbs24.pdf Beder, T. S. (1995) ‘VaR: seductive but dangerous’, Financial Analysts Journal, 51, No. 5, 12–24, September/October or at http://www.cmra.com/ (registration required). Beder, T. S. (1995) Derivatives: The Realities of Marking to Model, Capital Market Risk Advisors at http://www.cmra.com/ Benson, P. and Zangari, P. (1997) ‘A general approach to calculating VaR without volatilities and correlations’, RiskMetrics Monitor, Second Quarter. Boudoukh, J., Richardson, M. and Whitelaw, R. (1997) ‘Investigation of a class of volatility estimators’, Journal of Derivatives, Spring. Butler, J. and Schachter, B. (1996) ‘Improving Value-at-Risk estimates by combining kernel estimation with historic simulation’, OCC Report, May. Elton, E. J. and Gruber, M. J. (1995) Modern Portfolio Theory and Investment Analysis, Wiley, New York. Embrechs, P. et al. (1998) ‘Living on the edge’, Risk, January. Gizycki, M. and Hereford, N. (1998) ‘Differences of opinion’, Asia Risk, 42–7, August. Group of Thirty (1993) Derivatives: Practices and Principles, Recommendations 2 and 3, Global Derivatives Study Group, Washington, DC.

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Hendricks, D. (1996) ‘Evaluation of VaR models using historical data’, Federal Reserve Bank of New York Economic Policy Review, April, or http://www.ny.frb.org/ rmaghome/econ_pol/496end.pdf Hendricks, D. and Hirtle, B. (1998) ‘Market risk capital’, Derivatives Week, 6 April. Hogg, R. and Craig, A. (1978) Introduction to Mathematical Statistics, Macmillan, New York. Hull, J. and White, A. (1998) ‘Value-at-Risk when daily changes in market variables are not normally distributed’, Journal of Derivatives, Spring. McNeil, A. (1998) ‘History repeating’, Risk, January. Stahl, G. (1997) ‘Three cheers’, Risk, May. Wilmot, P. et al. (1995) ‘Spot-on modelling’, Risk, November. Wilson, T. (1996) ‘Calculating risk capital’, in Alexander, C. (ed.), The Handbok of Risk Management, Wiley, New York. Zangari, P. (1997) ‘Streamlining the market risk measurement process’, RiskMetrics Monitor, First Quarter.

Part 2

Market risk, credit risk and operational risk

5

Yield curve risk factors: domestic and global contexts WESLEY PHOA

Introduction: handling multiple risk factors Methodological introduction Traditional interest rate risk management focuses on duration and duration management. In other words, it assumes that only parallel yield curve shifts are important. In practice, of course, non-parallel shifts in the yield curve often occur, and represent a significant source of risk. What is the most efficient way to manage non-parallel interest rate risk? This chapter is mainly devoted to an exposition of principal component analysis, a statistical technique that attempts to provide a foundation for measuring non-parallel yield curve risk, by identifying the ‘most important’ kinds of yield curve shift that occur empirically. The analysis turns out to be remarkably successful. It gives a clear justification for the use of duration as the primary measure of interest rate risk, and it also suggests how one may design ‘optimal’ measures of non-parallel risk. Principal component analysis is a popular tool, not only in theoretical studies but also in practical risk management applications. We discuss such applications at the end of the chapter. However, it is first important to understand that principal component analysis has limitations, and should not be applied blindly. In particular, it is important to distinguish between results that are economically meaningful and those that are statistical artefacts without economic significance. There are two ways to determine whether the results of a statistical analysis are meaningful. The first is to see whether they are consistent with theoretical results; the Appendix gives a sketch of this approach. The second is simply to carry out as much exploratory data analysis as possible, with different data sets and different historical time periods, to screen out those findings which are really robust. This chapter contains many examples. In presenting the results, our exposition will rely mainly on graphs rather than tables and statistics. This is not because rigorous statistical criteria are unnecessary – in fact, they are very important. However, in the exploratory phase of any empirical study it is critical to get a good feel for the results first, since statistics can easily

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mislead. The initial goal is to gain insight; and visual presentation of the results can convey the important findings most clearly, in a non-technical form. It is strongly suggested that, after finishing this chapter, readers should experiment with the data themselves. Extensive hands-on experience is the only way to avoid the pitfalls inherent in any empirical analysis.

Non-parallel risk, duration bucketing and partial durations Before discussing principal component analysis, we briefly review some more primitive approaches to measuring non-parallel risk. These have by no means been superseded: later in the chapter we will discuss precisely what role they continue to play in risk management. The easiest approach is to group securities into maturity buckets. This is a very simple way of estimating exposure to movements at the short, medium and long ends of the yield curve. But it is not very accurate: for example, it ignores the fact that a bond with a higher coupon intuitively has more exposure to movements in the short end of the curve than a lower coupon bond with the same maturity. Next, one could group securities into duration buckets. This approach is somewhat more accurate because, for example, it distinguishes properly between bonds with different coupons. But it is still not entirely accurate because it does not recognize that the different individual cash flows of a single security are affected in different ways by a non-parallel yield curve shift. Next, one could group security cash flows into duration buckets. That is, one uses a finer-grained unit of analysis: the cash flow, rather than the security. This makes the results much more precise. However, bucketed duration exposures have no direct interpretation in terms of changes in some reference set of yields (i.e. a shift in some reference yield curve), and can thus be tricky to interpret. More seriously, as individual cash flows shorten they will move across bucket duration boundaries, causing discontinuous changes in bucket exposures which can make risk management awkward. Alternatively, one could measure partial durations. That is, one directly measures how the value of a portfolio changes when a single reference yield is shifted, leaving the other reference yields unchanged; note that doing this at the security level and at the cashflow level gives the same results. There are many different ways to define partial durations: one can use different varieties of reference yield (e.g. par, zero coupon, forward rate), one can choose different sets of reference maturities, one can specify the size of the perturbation, and one can adopt different methods of interpolating the perturbed yield curve between the reference maturities. The most popular partial durations are the key rate durations defined in Ho (1992). Fixing a set of reference maturities, these are defined as follows: for a given reference maturity T, the T-year key rate duration of a portfolio is the percentage change in its value when one shifts the T-year zero coupon yield by 100 bp, leaving the other reference zero coupon yields fixed, and linearly interpolating the perturbed zero coupon curve between adjacent reference maturities (often referred to as a ‘tent’ shift). Figure 5.1 shows some examples of key rate durations. All the above approaches must be used with caution when dealing with optionembedded securities such as callable bonds or mortgage pools, whose cashflow timing will vary with the level of interest rates. Option-embedded bonds are discussed in detail elsewhere in this book.

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Figure 5.1 Key rate durations of non-callable Treasury bonds.

Limitations of key rate duration analysis Key rate durations are a popular and powerful tool for managing non-parallel risk, so it is important to understand their shortcomings. First, key rate durations can be unintuitive. This is partly because ‘tent’ shifts do not occur in isolation, and in fact have no economic meaning in themselves. Thus, using key rate durations requires some experience and familiarization. Second, correlations between shifts at different reference maturities are ignored. That is, the analysis treats shifts at different points in the yield curve as independent, whereas different yield curve points tend to move in correlated ways. It is clearly important to take these correlations into account when measuring risk, but the key rate duration methodology does not suggest a way to do so. Third, the key rate duration computation is based on perturbing a theoretical zero coupon curve rather than observed yields on coupon bonds, and is therefore sensitive to the precise method used to strip (e.g.) a par yield curve. This introduces some arbitrariness into the results, and more significantly makes them hard to interpret in terms of observed yield curve shifts. Thus swap dealers (for example) often look at partial durations computed by directly perturbing the swap curve (a par curve) rather than perturbing a zero coupon curve. Fourth, key rate durations for mortgage-backed securities must be interpreted with special care. Key rate durations closely associated with specific reference maturities which drive the prepayment model can appear anomalous; for example, if the mortgage refinancing rate is estimated using a projected 10-year Treasury yield, 10-year key rate durations on MBS will frequently be negative. This is correct according to the definition, but in this situation one must be careful in constructing MBS hedging strategies using key rate durations. Fifth, key rate durations are unwieldy. There are too many separate interest rate

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risk measures. This leads to practical difficulties in monitoring risk, and inefficiencies in hedging risk. One would rather focus mainly on what is ‘most important’. To summarize: while key rate durations are a powerful risk management tool, it is worth looking for a more sophisticated approach to analyzing non-parallel risk that will yield deeper insights, and that will provide a basis for more efficient risk management methodologies.

Principal component analysis Deﬁnition and examples from US Treasury market As often occurs in finance, an analogy with physical systems suggests an approach. Observed shifts in the yield curve may seem complex and somewhat chaotic. In principle, it might seem that any point on the yield curve can move independently in a random fashion. However, it turns out that most of the observed fluctuation in yields can be explained by more systematic yield shifts: that is, bond yields moving ‘together’, in a correlated fashion, but perhaps in several different ways. Thus, one should not focus on fluctuations at individual points on the yield curve, but on shifts that apply to the yield curve as a whole. It is possible to identify these systematic shifts by an appropriate statistical analysis; as often occurs in finance, one can apply techniques inspired by the study of physical systems. The following concrete example, taken from Jennings and McKeown (1992), may be helpful. Consider a plank with one end fixed to a wall. Whenever the plank is knocked, it will vibrate. Furthermore, when it vibrates it does not deform in a completely random way, but has only a few ‘vibration modes’ corresponding to its natural frequencies. These vibration modes have different degrees of importance, with one mode – a simple back-and-forth motion – dominating the others: see Figure 5.2. One can derive these vibration modes mathematically, if one knows the precise physical characteristics of the plank. But one should also be able to determine them empirically by observing the plank. To do this, one attaches motion sensors at different points on the plank, to track the motion of these points through time. One will find that the observed disturbances at each point are correlated. It is possible to extract the vibration modes, and their relative importance, from the correlation matrix. In fact, the vibration modes correspond to the eigenvalues of the matrix: in other words, the eigenvectors, plotted in graphical form, will turn out to look exactly as in Figure 5.2. The relative importance of each vibration mode is measured by the size of the corresponding eigenvectors. Let us recall the definitions. Let A be a matrix. We say that v is an eigenvector of A, with corresponding eigenvalue j, if A . vójv. The eigenvalues of a matrix must be mutually orthogonal, i.e. ‘independent’. Note that eigenvectors are only defined up to a scalar multiple, but that eigenvalues are uniquely defined. Suppose A is a correlation matrix, e.g. derived from some time series of data; then it must be symmetric and also positive definite (i.e. v . A . v[0 for all vectors v). One can show that all the eigenvalues of such a matrix must be real and positive. In this case it makes sense to compare their relative sizes, and to regard them as ‘weights’ which measure the importance of the corresponding eigenvectors. For a physical system such as the cantilever, the interpretation is as follows. The

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Figure 5.2 Vibration modes of the cantilever.

eigenvectors describe the independent vibration modes: each eigenvector has one component for each sensor, and the component is a (positive or negative) real number which describes the relative displacement of that sensor under the given vibration mode. The corresponding eigenvalue measures how much of the observed motion of the plank can be attributed to that specific vibration mode. This suggests that we can analyze yield curve shifts analogously, as follows. Fix a set of reference maturities for which reasonably long time series of, say, daily yields are available: each reference maturity on the yield curve is the analog of a motion sensor on the plank. Construct the time series of daily changes in yield at each reference maturity, and compute the correlation matrix. Next, compute the eigenvectors and eigenvalues of this matrix. The eigenvectors can then be interpreted as independent ‘fundamental yield curve shifts’, analogous to vibration modes; in other words, the actual change in the yield curve on any particular day may be regarded as a combination of different, independent, fundamental yield curve shifts. The relative sizes of the eigenvalues tells us which fundamental yield curve shifts tend to dominate.

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Daily changes 1 0 ñ2 5 0

1 1 ñ2 4 0

1 2 ñ2 3 1

1 3 ñ2 2 0

Correlation matrix 1.00 0.97 0.83 0.59

0.97 1.00 0.92 0.77

0.83 0.92 1.00 0.90

0.59 0.77 0.90 1.00

Eigenvalues and eigenvectors [A] [B] [C] [D]

0.000 0.037 0.462 3.501

(0%) (1%) (11%) (88%)

0.607 ñ0.155 ñ0.610 0.486

ñ0.762 ñ0.263 ñ0.274 0.524

0.000 0.827 0.207 0.522

0.225 ñ0.471 0.715 0.465

For a toy example, see Table 5.1. The imaginary data set consists of five days of observed daily yield changes at four unnamed reference maturities; for example, on days 1 and 3 a perfectly parallel shift occurred. The correlation matrix shows that yield shifts at different maturity points are quite correlated. Inspecting the eigenvalues and eigenvectors shows that, at least according to principal component analysis, there is a dominant yield curve shift, eigenvector (D), which represents an almost parallel shift: each maturity point moves by about 0.5. The second most important eigenvector (C) seems to represent a slope shift or ‘yield curve tilt’. The third eigenvector (B) seems to appear because of the inclusion of day 5 in the data set. Note that the results might not perfectly reflect one’s intuition. First, the dominant shift (D) is not perfectly parallel, even though two perfectly parallel shifts were included in the data set. Second, the shift that occurred on day 2 is regarded as a combination of a parallel shift (D) and a slope shift (C), not a slope shift alone; shift (C) has almost the same shape as the observed shift on day 2, but it has been ‘translated’ so that shifts of type (C) are uncorrelated with shifts of type (D). Third, eigenvector (A) seems to have no interpretation. Finally, the weight attached to (D) seems very high – this is because the actual shifts on all five days are regarded as having a parallel component, as we just noted. A technical point: in theory, one could use the covariance matrix rather than the correlation matrix in the analysis. However using the correlation matrix is preferable when observed correlations are more stable than observed covariances – which is usually the case in financial data where volatilities are quite unstable. (For further discussion, see Buhler and Zimmermann, 1996.) In the example of Table 5.1, very similar results are obtained using the covariance matrix. Table 5.2 shows the result of a principal component analysis carried out on actual US Treasury bond yield data from 1993 to 1998. In this case the dominant shift is a virtually parallel shift, which explains over 90% of observed fluctuations in bond yields. The second most important shift is a slope shift or tilt in which short yields fall and long yields rise (or vice versa). The third shift is a kind of curvature shift, in

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Table 5.2 Principal component analysis of US Treasury yields, 1993–8

0.3% 0.3% 0.2% 0.4% 0.6% 1.1% 5.5% 91.7%

1 year

2 year

3 year

5 year

7 year

10 year

20 year

30 year

0.00 0.00 0.01 ñ0.05 0.21 0.70 ñ0.59 0.33

0.05 ñ0.08 ñ0.05 ñ0.37 ñ0.71 ñ0.30 ñ0.37 0.35

ñ0.20 0.49 ñ0.10 0.65 0.03 ñ0.32 ñ0.23 0.36

0.31 ñ0.69 0.25 0.27 0.28 ñ0.30 ñ0.06 0.36

ñ0.63 0.06 0.30 ñ0.45 0.35 ñ0.19 0.14 0.36

0.50 0.27 ñ0.52 ñ0.34 0.34 ñ0.12 0.20 0.36

0.32 0.30 0.59 0.08 ñ0.27 0.28 0.44 0.35

ñ0.35 ñ0.34 ñ0.48 0.22 ñ0.26 0.32 0.45 0.35

Historical bond yield data provided by the Federal Reserve Board.

which short and long yields rise while mid-range yields fall (or vice versa); the remaining eigenvectors have no meaningful interpretation and are statistically insignificant. Note that meaningful results will only be obtained if a consistent set of yields is used: in this case, constant maturity Treasury yields regarded as a proxy for a Treasury par yield curve. Yields on physical bonds should not be used, since the population of bonds both ages and changes composition over time. The analysis here has been carried out using CMT yields reported by the US Federal Reserve Bank. An alternative is to use a dataset consisting of historical swap rates, which are par yields by definition. The results of the analysis turn out to be very similar.

Meaningfulness of factors: dependence on dataset It is extremely tempting to conclude that (a) the analysis has determined that there are exactly three important kinds of yield curve shift, (b) that it has identified them precisely, and (c) that it has precisely quantified their relative importance. But we should not draw these conclusions without looking more carefully at the data. This means exploring datasets drawn from different historical time periods, from different sets of maturities, and from different countries. Risk management should only rely on those results which turn out to be robust. Figure 5.3 shows a positive finding. Analyzing other 5-year historical periods, going back to 1963, we see that the overall results are quite consistent. In each case the major yield curve shifts turn out to be parallel, slope and curvature shifts; and estimates of the relative importance of each kind of shift are reasonably stable over time, although parallel shifts appear to have become more dominant since the late 1970s. Figures 5.4(a) and (b) show that some of the results remain consistent when examined in more detail: the estimated form of both the parallel shift and the slope shift are very similar in different historical periods. Note that in illustrating each kind of yield curve shift, we have carried out some normalization to make comparisons easier: for example, estimated slope shifts are normalized so that the 10-year yield moves 100 bp relative to the 1-year yield, which remains fixed. See below for further discussion of this point. However, Figure 5.4(c) does tentatively indicate that the form of the curvature shift has varied over time – a first piece of evidence that results on the curvature shift may be less robust than those on the parallel and slope shifts.

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Figure 5.3 Relative importance of principal components, 1963–98.

Figure 5.5 shows the effect of including 3- and 6-month Treasury bill yields in the 1993–8 dataset. The major yield curve shifts are still identified as parallel, slope and curvature shifts. However, an analysis based on the dataset including T-bills attaches somewhat less importance to parallel shifts, and somewhat more importance to slope and curvature shifts. Thus, while the estimates of relative importance remain qualitatively significant, they should not be regarded as quantitatively precise. Figures 5.6(a) and (b) show that the inclusion of T-bill yields in the dataset makes almost no difference to the estimated form of both the parallel and slope shifts. However, Figure 5.6(c) shows that the form of the curvature shift is totally different. Omitting T-bills, the change in curvature occurs at the 3–5-year part of the curve; including T-bills, it occurs at the 1-year part of the curve. There seem to be some additional dynamics associated with yields on short term instruments, which become clear once parallel and slope shifts are factored out; this matter is discussed further in Phoa (1998a,b). The overall conclusions are that parallel and slope shifts are unambiguously the most important kinds of yield curve shift that occur, with parallel shifts being dominant; that the forms of these parallel and slope shifts can be estimated fairly precisely and quite robustly; but that the existence and form of a third, ‘curvature’ shift are more problematic, with the results being very dependent on the dataset used in the analysis. Since the very form of a curvature shift is uncertain, and specifying it precisely requires making a subjective judgment about which dataset is ‘most relevant’, the curvature shift is of more limited use in risk management. The low weight attached to the curvature factor also suggests that it may be less important than other (conjectural) phenomena which might somehow have been missed by the analysis. The possibility that the analysis has failed to detect some important yield curve risk factors, which potentially outweigh curvature risk, is discussed further below. International bond yield data are analyzed in the next section. The results are

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Figure 5.4 1963–98.

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Shapes of (a) ‘parallel’ shift, 1963–98, (b) ‘slope’ shift, 1963–98, (c) ‘curvature’ shift,

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Figure 5.5 Relative importance of principal components, with/without T-bills.

broadly consistent, but also provide further grounds for caution. The Appendix provides some theoretical corroboration for the positive findings. We have glossed over one slightly awkward point. The fundamental yield curve shifts estimated by a principal component analysis – in particular, the first two principal components representing parallel and slope shifts – are, by definition, uncorrelated. But normalizing a ‘slope shift’ so that the 1-year yield remains fixed introduces a possible correlation. This kind of normalization is convenient both for data analysis, as above, and for practical applications; but it does mean that one then has to estimate the correlation between parallel shifts and normalized slope shifts. This is not difficult in principle, but, as shown in Phoa (1998a,b), this correlation is time-varying and indeed exhibits secular drift. This corresponds to the fact that, while the estimated (non-normalized) slope shifts for different historical periods have almost identical shapes, they have different ‘pivot points’. The issue of correlation risk is discussed further below.

Correlation structure and other limitations of the approach It is now tempting to concentrate entirely on parallel and slope shifts. This approach forms the basis of most useful two factor interest rate models: see Brown and Schaefer (1995). However, it is important to understand what is being lost when one focuses only on two kinds of yield curve shift. First, there is the question of whether empirical correlations are respected. Figure 5.7(a) shows, graphically, the empirical correlations between daily Treasury yield shifts at different maturity points. It indicates that, as one moves to adjacent maturities, the correlations fall away rather sharply. In other words, even adjacent yields quite often shift in uncorrelated ways. Figure 5.7(b) shows the correlations which would have been observed if only parallel and slope shifts had taken place. These slope away much more gently as one moves to adjacent maturities: uncorrelated shifts in adjacent yields do not occur. This observation is due to Rebonato and Cooper (1996), who prove that the correlation structure implied by a two-factor model must always take this form.

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Figure 5.6 (a) Estimated ‘parallel’ shift, with/without T-bills, (b) estimated ‘slope’ shift, with/without T-bills, (c) estimated ‘curvature’ shift, with/without T-bills.

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Figure 5.7 (a) Empirical Treasury yield correlations, (b) theoretical Treasury yield correlations, twofactor model.

What this shows is that, even though the weights attached to the ‘other’ eigenvectors seemed very small, discarding these other eigenvectors radically changes the correlation structure. Whether or not this matters in practice will depend on the specific application. Second, there is the related question of the time horizon of risk. Unexplained yield shifts at specific maturities may be unimportant if they quickly ‘correct’; but this will clearly depend on the investor’s time horizon. If some idiosyncratic yield shift occurs, which has not been anticipated by one’s risk methodology, this may be disastrous for a hedge fund running a highly leveraged trading book with a time horizon of

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hours or days; but an investment manager with a time horizon of months or quarters, who is confident that the phenomenon is transitory and who can afford to wait for it to reverse itself, might not care as much.

Figure 5.8 Actual Treasury yield versus yield predicted by two-factor model.

This is illustrated in Figure 5.8. It compares the observed 10-year Treasury yield from 1953 to 1996 to the yield which would have been predicted by a model in which parallel and slope risk fully determine (via arbitrage pricing theory) the yields of all Treasury bonds. The actual yield often deviates significantly from the theoretical yield, as yield changes unrelated to parallel and slope shifts frequently occurred. But deviations appear to mean revert to zero over periods of around a few months to a year; this can be justified more rigorously by an analysis of autocorrelations. Thus, these deviations matter over short time frames, but perhaps not over long time frames. See Phoa (1998a,b) for further details. Third, there is the question of effects due to market inhomogeneity. In identifying patterns of yield shifts by maturity, principal component analysis implicitly assumes that the only relevant difference between different reference yields is maturity, and that the market is homogeneous in every other way. If it is not – for example, if there are differences in liquidity between different instruments which, in some circumstances, lead to fluctuations in relative yields – then this assumption may not be sound. The US Treasury market in 1998 provided a very vivid example. Yields of onthe-run Treasuries exhibited sharp fluctuations relative to off-the-run yields, with ‘liquidity spreads’ varying from 5 bp to 25 bp. Furthermore, different on-the-run issues were affected in different ways in different times. A principal component analysis based on constant maturity Treasury yields would have missed this source of risk entirely; and in fact, even given yield data on the entire population of Treasury bonds, it would have been extremely difficult to design a similar analysis which would have been capable of identifying and measuring some systematic ‘liquidity

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spread shift’. In this case, risk management for a Treasury book based on principal component analysis needs to be supplemented with other methods. Fourth, there is the possibility that an important risk factor has been ignored. For example, suppose there is an additional kind of fundamental yield curve shift, in which 30- to 100-year bond yields move relative to shorter bond yields. This would not be identified by a principal component analysis, for the simple reason that this maturity range is represented by only one point in the set of reference maturities. Even if the 30-year yield displayed idiosyncratic movements – which it arguably does – the analysis would not identify these as statistically significant. The conjectured ‘long end’ risk factor would only emerge if data on other longer maturities were included; but no such data exists for Treasury bonds. An additional kind of ‘yield curve risk’, which could not be detected at all by an analysis of CMT yields, is the varying yield spread between liquid and illiquid issues as mentioned above. This was a major factor in the US Treasury market in 1998; in fact, from an empirical point of view, fluctuations at the long end of the curve and fluctuations in the spread between on- and off-the-run Treasuries were, in that market, more important sources of risk than curvature shifts – and different methods were required to measure and control the risk arising from these sources. To summarize, a great deal more care is required when using principal component analysis in a financial, rather than physical, setting. One should always remember that the rigorous justifications provided by the differential equations of physics are missing in financial markets, and that seemingly analogous arguments such as those presented in the Appendix are much more heuristic. The proper comparison is with biology or social science rather than physics or engineering.

International bonds Principal component analysis for international markets All our analysis so far has used US data. Are the results applicable to international markets? To answer this question, we analyze daily historical bond yield data for a range of developed countries, drawn from the historical period 1986–96. In broad terms, the results carry over. In almost every case, the fundamental yield curve shifts identified by the analysis are a parallel shift, a slope shift and some kind of curvature shift. Moreover, as shown in Figure 5.9, the relative importance of these different yield curve shifts is very similar in different countries – although there is some evidence that parallel shifts are slightly less dominant, and slope shifts are slightly more important, in Europe and Japan than in USD bloc countries. It is slightly worrying that Switzerland appears to be an exception: the previous results simply do not hold, at least for the dataset used. This proves that one cannot simply take the results for granted; they must be verified for each individual country. For example, one should not assume that yield curve risk measures developed for use in the US bond market are equally applicable to some emerging market. Figure 5.10(a) shows the estimated form of a parallel shift in different countries. Apart from Switzerland, the results are extremely similar. In other words, duration is an equally valid risk measure in different countries. Figure 5.10(b) shows the estimated form of a slope shift in different countries; in this case, estimated slope shifts have been normalized so that the 3-year yield

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Figure 5.9 Relative importance of principal components in various countries. (Historical bond yield data provided by Deutsche Bank Securities).

remains fixed and the 10-year yield moves by 100 bp. Unlike the parallel shift, there is some evidence that the slope shift takes different forms in different countries; this is consistent with the findings reported in Brown and Schaefer (1995). For risk management applications it is thus prudent to estimate the form of the slope shift separately for each country rather than, for example, simply using the US slope shift. Note that parallel/slope correlation also varies between countries, as well as over time. Estimated curvature shifts are not shown, but they are quite different for different countries. Also, breaking the data into subperiods, the form of the curvature shift typically varies over time as it did with the US data. This is further evidence that there is no stable ‘curvature shift’ which can reliably be used to define an additional measure of non-parallel risk.

Co-movements in international bond yields So far we have only used principal component analysis to look at data within a single country, to identify patterns of co-movement between yields at different maturities. We derived the very useful result that two major kinds of co-movement explain most variations in bond yields. It is also possible to analyze data across countries, to identify patterns of comovements between bond yields in different countries. For example, one could carry out a principal component analysis of daily changes in 10-year bond yields for various countries. Can any useful conclusions be drawn? The answer is yes, but the results are significantly weaker. Figure 5.9 shows the dominant principal component identified from three separate datasets: 1970–79, 1980–89 and 1990–98. As one might hope, this dominant shift is a kind of ‘parallel shift’, i.e. a simultaneous shift in bond yields, with the same direction and magnitude, in each country. In other words, the notion of ‘global duration’ seems to make sense:

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Figure 5.10 Shape of (a) ‘parallel’ shift and (b) ‘slope’ shift in different countries.

the aggregate duration of a global bond portfolio is a meaningful risk measure, which measures the portfolio’s sensitivity to an empirically identifiable global risk factor. However, there are three important caveats. First, the ‘global parallel shift’ is not as dominant as the term structure parallel shift identified earlier. In the 1990s, it explained only 54% of variation in global bond yields; in the 1970s, it explained only 29%. In other words, while duration captures most of the interest rate risk of a domestic bond portfolio, ‘global duration’ captures only half, or less, of the interest rate risk of a global bond portfolio: see Figure 5.11.

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Figure 5.11 Dominant principal component, global 10-year bond yields.

Second, the shift in bond yields is not perfectly equal in different countries. It seems to be lower for countries like Japan and Switzerland, perhaps because bond yields have tended to be lower in those countries. Third, the ‘global parallel shift’ is not universal: not every country need be included. For example, it seems as if Australian and French bond yields did not move in step with other countries’ bond yields in the 1970s, and did so only partially in the 1980s. Thus, the relevance of a global parallel shift to each specific country has to be assessed separately. Apart from the global parallel shift, the other eigenvectors are not consistently meaningful. For example, there is some evidence of a ‘USD bloc shift’ in which US, Canadian, Australian and NZ bond yields move while other bond yields remain fixed, but this result is far from robust. To summarize, principal component analysis provides some guidelines for global interest rate risk management, but it does not simplify matters as much as it did for yield curve risk. The presence of currency risk is a further complication; we return to this topic below.

Correlations: between markets, between yield and volatility Recall that principal component analysis uses a single correlation matrix to identify dominant patterns of yield shifts. The results imply something about the correlations themselves: for instance, the existence of a global parallel shift that explains around 50% of variance in global bond yields suggests that correlations should, on average, be positive. However, in global markets, correlations are notoriously time-varying: see Figure 5.12. In fact, short-term correlations between 10-year bond yields in different coun-

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Figure 5.12 Historical 12-month correlations between 10-year bond yields.

tries are significantly less stable than correlations between yields at different maturities within a single country. This means that, at least for short time horizons, one must be especially cautious in using the results of principal component analysis to manage a global bond position. We now discuss a somewhat unrelated issue: the relationship between yield and volatility, which has been missing from our analysis so far. Principal component analysis estimates the form of the dominant yield curve shifts, namely parallel and slope shifts. It says nothing useful about the size of these shifts, i.e. about parallel and slope volatility. These can be estimated instantaneously, using historical or implied volatilities. But for stress testing and scenario analysis, one needs an additional piece of information: whether there is a relationship between volatility and (say) the outright level of the yield curve. For example, when stress testing a trading book under a ò100 bp scenario, should one also change one’s volatility assumption? It is difficult to answer this question either theoretically or empirically. For example, most common term structure models assume that basis point (parallel) volatility is either independent of the yield level, or proportional to the yield level; but these assumptions are made for technical convenience, rather that being driven by the data. Here are some empirical results. Figures 5.13(a)–(c) plot 12-month historical volatilities, expressed as a percentage of the absolute yield level, against the average yield level itself. If basis point volatility were always proportional to the yield level, these graphs would be horizontal lines; if basis point volatility were constant, these graphs would be hyperbolic. Neither seems to be the case. The Japanese dataset suggests that when yields are under around 6–7%, the graph is hyperbolic. All three datasets suggest that when yields are in the 7–10% range, the graph is horizontal. And the US dataset suggests that when yields are over 10%, the graph actually slopes upward: when yields rise,

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Figure 5.13 (a) US, (b) Japan and (c) Germany yield/volatility relationships.

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volatility rises more than proportionately. But in every case, the results are confused by the presence of volatility spikes. The conclusion is that, when stress testing a portfolio, it is safest to assume that when yields fall, basis point volatility need not fall; but when yields rise, basis point volatility will also rise. Better yet, one should run different volatility scenarios as well as interest rate scenarios.

Practical implications Risk management for a leveraged trading desk This section draws some practical conclusions from the above analysis, and briefly sketches some suggestions about risk measurement and risk management policy; more detailed proposals may be found elsewhere in this book. Since parallel and slope shifts are the dominant yield curve risk factors, it makes sense to focus on measures of parallel and slope risk; to structure limits in terms of maximum parallel and slope risk rather than more rigid limits for each point of the yield curve; and to design flexible hedging strategies based on matching parallel and slope risk. If the desk as a whole takes proprietary interest rate risk positions, it is most efficient to specify these in terms of target exposures to parallel and slope risk, and leave it to individual traders to structure their exposures using specific instruments. Rapid stress testing and Value-at-Risk estimates may be computed under the simplifying assumption that only parallel and slope risk exist. This approach is not meant to replace a standard VaR calculation using a covariance matrix for a whole set of reference maturities, but to supplement it. A simplified example of such a VaR calculation appears in Table 5.3, which summarizes both the procedure and the results. It compares the Value-at-Risk of three positions, each with a net market value of $100 million: a long portfolio consisting of a single position in a 10-year par bond; a steepener portfolio consisting of a long position in a 2-year bond and a short position in a 10-year bond with offsetting durations, i.e. offsetting exposures to parallel risk; and a butterfly portfolio consisting of long/short positions in cash and 2-, 5- and 10-year bonds with zero net exposure to both parallel and slope risk. For simplicity, the analysis assumes a ‘total volatility’ of bond yields of about 100 bp p.a., which is broadly realistic for the US market. The long portfolio is extremely risky compared to the other two portfolios; this reflects the fact that most of the observed variance in bond yields comes from parallel shifts, to which the other two portfolios are immunized. Also, the butterfly portfolio appears to have almost negligible risk: by this calculation, hedging both parallel and slope risk removes over 99% of the risk. However, it must be remembered that the procedure assumes that the first three principal components are the only sources of risk. This calculation was oversimplified in several ways: for example, in practice the volatilities would be estimated more carefully, and risk computations would probably be carried out on a cash flow-by-cash flow basis. But the basic idea remains straightforward. Because the calculation can be carried out rapidly, it is easy to vary assumptions about volatility/yield relationships and about correlations, giving additional insight into the risk profile of the portfolio. Of course, the calculation is

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Table 5.3 Simpliﬁed Value-at-Risk calculation using principal components Deﬁnitions di vi pi VaRi VaR

‘duration’ relative to factor i variance of factor i bp volatility of factor i Value-at-Risk due to factor i

ë duration . (factor i shift) ë factor weight óv1/2 i ëpi . di . T

aggregate Value-at-Risk

ó ; VaR2i

Long portfolio

1/2

i

$100m

10-year par bond

Steepener portfolio

$131m ñ$31m

2-year par bond 10-year par bond

Butterﬂy portfolio

$64m $100m ñ$93m $29m

cash 2-year par bond 5-year par bond 10-year par bond

Calculations Assume 100 bp p.a. ‘total volatility’, factors and factor weights as in Table 5.2. Ignore all but the ﬁrst three factors (those shown in Figure 5.4). Parallel Long

Steepener

Butterﬂy

10yr durn Total durn Risk (VaR)

7.79 7.79 5.75%

Slope

Curvature

1 s.d. risk

Daily VaR

1.50 1.50 0.27%

ñ0.92 ñ0.92 ñ0.07%

5.95%

$376 030

2yr durn 10yr durn Total durn Risk (VaR)

2.39 ñ2.39 0.00 0.00

ñ0.87 ñ0.46 ñ1.33 ñ0.24

ñ0.72 0.28 ñ0.44 ñ0.04

0.28%

$17 485

Cash durn 2yr durn 5yr durn 10yr durn Total durn Risk (VaR)

0.00 1.83 ñ4.08 2.25 0.00 0.00%

0.00 ñ0.67 0.24 0.43 0.00 0.00%

0.00 ñ0.55 1.20 ñ0.26 0.38 0.03%

0.03%

$1 954

approximate, and in practice large exposures at specific maturities should not be ignored. That would tend to understate the risk of butterfly trades, for example. However, it is important to recognize that a naive approach to measuring risk, which ignores the information about co-movements revealed by a principal component analysis, will tend to overstate the risk of a butterfly position; in fact, in some circumstances a butterfly position is no riskier than, say, an exposure to the spread between on- and off-the-run Treasuries. In other words, the analysis helps risk managers gain some sense of perspective when comparing the relative importance of different sources of risk. Risk management for a global bond book is harder. The results of the analysis are mainly negative: they suggest that the most prudent course is to manage each country exposure separately. For Value-at-Risk calculations, the existence of a ‘global parallel shift’ suggests an alternative way to estimate risk, by breaking it into two

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components: (a) risk arising from a global shift in bond yields, and (b) countryspecific risk relative to the global component. This approach has some important advantages over the standard calculation, which uses a covariance matrix indexed by country. First, the results are less sensitive to the covariances, which are far from stable. Second, it is easier to add new countries to the analysis. Third, it is easier to incorporate an assumption that changes in yields have a heavy-tailed (non-Gaussian) distribution, which is particularly useful when dealing with emerging markets. Again, the method is not proposed as a replacement for standard VaR calculations, but as a supplement.

Total return management and benchmark choice For an unleveraged total return manager, many of the proposals are similar. It is again efficient to focus mainly on parallel and slope risk when setting interest rate risk limits, implementing an interest rate view, or designing hedging strategies. This greatly simplifies interest rate risk management, freeing up the portfolio manager’s time to focus on monitoring other forms of risk, on assessing relative value, and on carrying out more detailed scenario analysis. Many analytics software vendors, such as CMS, provide measures of slope risk. Investment managers should ensure that such a measure satisfies two basic criteria. First, it should be consistent with the results of a principal component analysis: a measure of slope risk based on an unrealistic slope shift is meaningless. Second, it should be easy to run, and the results should be easy to interpret: otherwise, it will rarely be used, and slope risk will not be monitored effectively. The above comments on risk management of global bond positions apply equally well in the present context. However, there is an additional complication. Global bond investors tend to have some performance benchmark, but it is most unclear how an ‘optimal’ benchmark should be constructed, and how risk should be measured against it. For example, some US investors simply use a US domestic index as a benchmark; many use a currency unhedged global benchmark. (Incidentally, the weights of a global benchmark are typically determined by issuance volumes. This is somewhat arbitrary: it means that a country’s weight in the index depends on its fiscal policy and on the precise way public sector borrowing is funded. Mason has suggested using GDP weights; this tends to lower the risk of the benchmark.) Figures 5.14(a)–(c) may be helpful. They show the risk/return profile, in USD terms, of a US domestic bond index; currency unhedged and hedged global indexes; and the full range of post hoc efficient currency unhedged and hedged portfolios. Results are displayed separately for the 1970s, 1980s and 1990s datasets. The first observation is that the US domestic index has a completely different (and inferior) risk/return profile to any of the global portfolios. It is not an appropriate benchmark. The second observation is that hedged and unhedged portfolios behave in completely different ways. In the 1970s, hedged portfolios were unambiguously superior; in the 1980s, hedged and unhedged portfolios behaved almost like two different asset types; and in the 1990s, hedged and unhedged portfolios seemed to lie on a continuous risk/return scale, with hedged portfolios at the less risky end. If a benchmark is intended to be conservative, a currency hedged benchmark is clearly appropriate. What, then, is a suitable global benchmark? None of the post hoc efficient portfolios will do, since the composition of efficient portfolios is extremely unstable over time – essentially because both returns and covariances are unstable. The most plausible

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Figure 5.14 Global bond efﬁcient frontier and hedged index: (a) 1970s, (b) 1980s and (c) 1990s. (Historical bond and FX data provided by Deutsche Bank Securities).

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candidate is the currency hedged global index. It has a stable composition, has relatively low risk, and is consistently close to the efficient frontier. Once a benchmark is selected, principal component analysis may be applied as follows. First, it identifies countries which may be regarded as particularly risk relative to the benchmark; in the 1970s and 1980s this would have included Australia and France (see Figure 5.11). Note that this kind of result is more easily read off from the analysis than by direct inspection of the correlations. Second, it helps managers translate country-specific views into strategies. That it, by estimating the proportion of yield shifts attributable to a global parallel shift (around 50% in the 1990s) it allows managers will a bullish or bearish view on a specific country to determine an appropriate degree of overweighting. Third, it assists managers who choose to maintain open currency risk. A more extensive analysis can be used to identify ‘currency blocs’ (whose membership may vary over time) and to estimate co-movements between exchange rates and bond yields. However, all such results must be used with great caution.

Asset/liability management and the use of risk buckets For asset/liability managers, the recommendations are again quite similar. One should focus on immunizing parallel risk (duration) and slope risk. If these two risk factors are well matched, then from an economic point of view the assets are an effective hedge for the liabilities. Key rate durations are a useful way to measure exposure to individual points on the yield curve; but it is probably unnecessary to match all the key rate durations of assets and liabilities precisely. However, one does need to treat both the short and the very long end of the yield curve separately. Regarding the long end of the yield curve, it is necessary to ensure that really longdated liabilities are matched by similarly long-dated assets. For example, one does not want to be hedging 30-year liabilities with 10-year assets, which would be permitted if one focused only on parallel and slope risk. Thus, it is desirable to ensure that 10-year to 30-year key rate durations are reasonably well matched. Regarding the short end of the yield curve, two problems arise. First, for maturities less than about 18–24 months – roughly coinciding with the liquid part of the Eurodollar futures strip – idiosyncratic fluctuations at the short end of the curve introduce risks additional to parallel and slope risk. It is safest to identify and hedge these separately, either using duration bucketing or partial durations. Second, for maturities less than about 12 months, it is desirably to match actual cashflows and not just risks. That is, one needs to generate detailed cashflow forecasts rather than simply matching interest rate risk measures. To summarize, an efficient asset/liability management policy might be described as follows: from 0–12 months, match cash flows in detail; from 12–24 months, match partial durations or duration buckets in detail; from 2–15 years, match parallel and slope risk only; beyond 15 years, ensure that partial durations are roughly matched too. Finally, one must not forget optionality. If the assets have very different option characteristics from the liabilities – which may easily occur when callable bonds or mortgage-backed securities are held – then it is not sufficient to match interest rate exposure in the current yield curve environment. One must also ensure that risks are matched under different interest rate and volatility scenarios. Optionality is treated in detail elsewhere in this book. In conclusion: principal component analysis suggests a simple and attractive

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solution to the problem of efficiently managing non-parallel yield curve risk. It is easy to understand, fairly easy to implement, and various off-the-shelf implementations are available. However, there are quite a few subtleties and pitfalls involved. Therefore, risk managers should not rush to implement policies, or to adopt vendor systems, without first deepening their own insight through experimentation and reflection.

Appendix: Economic factors driving the curve Macroeconomic explanation of parallel and slope risk This appendix presents some theoretical explanations for why (a) parallel and slope shifts are the dominant kinds of yield curve shift that occur, (b) curvature shifts are observed but tend to be both transitory and inconsistent in form, and (c) the behavior of the short end of the yield curve is quite idiosyncratic. The theoretical analysis helps to ascertain which empirical findings are really robust and can be relied upon: that is, an empirical result is regarded as reliable if it has a reasonable theoretical explanation. For reasons of space, the arguments are merely sketched. We first explain why parallel and slope shifts emerge naturally from a macroeconomic analysis of interest rate expectations. For simplicity, we use an entirely standard linear macroeconomic model, shown in Table 5A.1; see Frankel (1995) for details. Table 5A.1 A macroeconomic model of interest rate expectations Model deﬁnitions: i short-term nominal interest rate ne expected long-term inﬂation rate re expected long-term real interest rate y log of output y¯ log of normal or potential output m log of the money supply p log of the price level c, {, j, o constant model parameters (elasticities) Model assumptions: The output gap is related to the current real interest rate through investment demand: yñy¯ óñc(iñneñr e ) Real money demand depends positively on income and negatively on the interest rate: mñpó{ yñji Price changes are determined by excess demand and expected long-term inﬂation: dp óo( yñy¯ )òne dt Theorem (Frankel, 1995): The expected rate of change of the interest rate is given by: oc di óñd(iñne ñr e ), where dó dt {còj

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The model is used in the following way. Bond yields are determined by market participants’ expectations about future short-term interest rates. These in turn are determined by their expectations about the future path of the economy: output, prices and the money supply. It is assumed that market participants form these expectations in a manner consistent with the macroeconomic model. Now, the model implies that the short-term interest rate must evolve in a certain fixed way; thus, market expectations must, ‘in equilibrium’, take a very simple form. To be precise, it follows from the theorem stated in Table 5A.1 that if i 0 is the current short-term nominal interest rate, i t is the currently expected future interest rate at some future time t, and i ê is the long-term expected future interest rate, then rational interest rate expectations must take the following form in equilibrium: i t ói ê ò(i 0 ñi ê )eñ dt In this context, a slope shift corresponds to a change in either i ê or i 0 , while a parallel shift corresponds to a simultaneous change in both. Figure 5A.1 shows, schematically, the structure of interest rate expectations as determined by the model. The expected future interest rate at some future time is equal to the expected future rate of inflation, plus the expected future real rate. (At the short end, some distortion is possible, of which more below.)

Figure 5A.1 Schematic breakdown of interest rate expectations.

In this setting, yield curve shifts occur when market participants revise their expectations about future interest rates – that is, about future inflation and output growth. A parallel shift occurs when both short- and long-term expectations change at once, by the same amount. A slope shift occurs when short-term expectations change but long-term expectations remain the same, or vice versa. Why are parallel shifts so dominant? The model allows us to formalize the following simple explanation: in financial markets, changes in long-term expectations are

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primarily driven by short-term events, which, of course, also drive changes in shortterm expectations. For a detailed discussion of this point, see Keynes (1936). Why is the form of a slope shift relatively stable over time, but somewhat different in different countries? In this setting, the shape taken by a slope shift is determined by d, and thus by the elasticity parameters c,{ j, o of the model. These parameters depend in turn on the flexibility of the economy and its institutional framework – which may vary from country to country – but not on the economic cycle, or on the current values of economic variables. So d should be reasonably stable. Finally, observe that there is nothing in the model which ensures that parallel and slope shifts should be uncorrelated. In fact, using the most natural definition of ‘slope shift’, there will almost certainly be a correlation – but the value of the correlation coefficient is determined by how short-term events affect market estimates of the different model variables, not by anything in the underlying model itself. So the model does not give us much insight into correlation risk.

Volatility shocks and curvature risk We have seen that, while principal component analysis seems to identify curvature shifts as a source of non-parallel risk, on closer inspection the results are somewhat inconsistent. That is, unlike parallel and slope shifts, curvature shifts do not seem to take a consistent form, making it difficult to design a corresponding risk measure. The main reason for this is that ‘curvature shifts’ can occur for a variety of quite different reasons. A change in mid-range yields can occur because (a) market volatility expectations have changed, (b) the ‘term premium’ for interest rate risk has changed, (c) market segmentation has caused a temporary supply/demand imbalance at specific maturities, or (d) a change in the structure of the economy has caused a change in the value d above. We briefly discuss each of these reasons, but readers will need to consult the References for further details. Regarding (a): The yield curve is determined by forward short-term interest rates, but these are not completely determined by expected future short-term interest rates; forward rates have two additional components. First, forward rates display a downward ‘convexity bias’, which varies with the square of maturity. Second, forward rates display an upward ‘term premium’, or risk premium for interest rate risk, which (empirically) rises at most linearly with maturity. The size of both components obviously depends on expected volatility as well as maturity. A change in the market’s expectations about future interest rate volatility causes a curvature shift for the following reason. A rise in expected volatility will not affect short maturity yields since both the convexity bias and the term premium are negligible. Yields at intermediate maturities will rise, since the term premium dominates the convexity bias at these maturities; but yields at sufficiently long maturities will fall, since the convexity bias eventually dominates. The situation is illustrated in Figure 5A.2. The precise form taken by the curvature shift will depend on the empirical forms of the convexity bias and the term premium, neither of which are especially stable. Regarding (b): The term premium itself, as a function of maturity, may change. In theory, if market participants expect interest rates to follow a random walk, the term premium should be a linear function of maturity; if they expect interest rates to range trade, or mean revert, the term premium should be sub-linear (this seems to be observed in practice). Thus, curvature shifts might occur when market participants revise their expectations about the nature of the dynamics of interest rates,

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Figure 5A.2 Curvature shift arising from changing volatility expectations.

perhaps because of a shift in the monetary policy regime. Unfortunately, effects like this are nearly impossible to measure precisely. Regarding (c): Such manifestations of market ineffiency do occur, even in the US market. They do not assume a consistent form, but can occur anywhere on the yield curve. Note that, while a yield curve distortion caused by a short-term supply/ demand imbalance may have a big impact on a leveraged trading book, it might not matter so much to a typical mutual fund or asset/liability manager. Regarding (d): It is highly unlikely that short-term changes in d occur, although it is plausible that this parameter may drift over a secular time scale. There is little justification for using ‘sensitivity to changes in d’ as a measure of curvature risk. Curvature risk is clearly a complex issue, and it may be dangerous to attempt to summarize it using a single stylized ‘curvature shift’. It is more appropriate to use detailed risk measures such as key rate durations to manage exposure to specific sections of the yield curve.

The short end and monetary policy distortions The dynamics of short maturity money market yields is more complex and idiosyncratic than that of longer maturity bond yields. We have already seen a hint of this in Figure 5.6(c), which shows that including T-bill yields in the dataset radically changes the results of a principal component analysis; the third eigenvector represents, not a ‘curvature shift’ affecting 3–5 year maturities, but a ‘hump shift’ affecting maturities around 1 year. This is confirmed by more careful studies. As with curvature shifts, hump shifts might be caused by changes in the term premium. But there is also an economic explanation for this kind of yield curve shift: it is based on the observation that market expectations about the path of interest rates in the near future can be much more complex than longer term expectations.

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For example, market participants may believe that monetary policy is ‘too tight’ and can make detailed forecasts about when it may be eased. Near-term expected future interest rates will not assume the simple form predicted by the macroeconomic model of Figure 5.4 if investors believe that monetary policy is ‘out of equilibrium’. This kind of bias in expectations can create a hump or bowl at the short end of the yield curve, and is illustrated schematically in Figure 5A.1. One would not expect a ‘hump factor’ to take a stable form, since the precise form of expectations, and hence of changes in expectations, will depend both on how monetary policy is currently being run and on specific circumstances. Thus, one should not feed money market yields to a principal component analysis and expect it to derive a reliable ‘hump shift’ for use in risk management. For further discussion and analysis, see Phoa (1998a,b). The overall conclusion is that when managing interest rate risk at the short end of the yield curve, measures of parallel and slope risk must be supplemented by more detailed exposure measures. Similarly, reliable hedging strategies cannot be based simply on matching parallel and slope risk, but must make use of a wider range of instruments such as a whole strip of Eurodollar futures contracts.

Acknowledgements The research reported here was carried out while the author was employed at Capital Management Sciences. The author has attempted to incorporate several useful suggestions provided by an anonymous reviewer.

References The following brief list of references is provided merely as a starting point for further reading, which might be structured as follows. For general background on matrix algebra and matrix computations, both Jennings and McKeown (1992) and the classic Press et al. (1992) are useful, though there are a multitude of alternatives. On principal components analysis, Litterman and Scheinkman (1991) and Garbade (1996) are still well worth reading, perhaps supplemented by Phoa (1998a,b) which contain further practical discussion. This should be followed with Buhler and Zimmermann (1996) and Hiraki et al. (1996) which make use of additional statistical techniques not discussed in the present chapter. However, at this point it is probably more important to gain hands-on experience with the techniques and, especially, the data. Published results should not be accepted unquestioningly, even those reported here! For numerical experimentation, a package such as Numerical Python or MATLABTM is recommended; attempting to write one’s own routines for computing eigenvectors is emphatically not recommended. Finally, historical bond data for various countries may be obtained from central banking authorities, often via the World Wide Web.1 Brown, R. and Schaefer, S. (1995) ‘Interest rate volatility and the shape of the term structure’, in Howison, S., Kelly, F. and Wilmott, P. (eds), Mathematical Models in Finance, Chapman and Hall.

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Buhler, A. and Zimmermann, H. (1996) ‘A statistical analysis of the term structure of interest rates in Switzerland and Germany’, Journal of Fixed Income, December. Frankel, J. (1995) Financial Markets and Monetary Policy, MIT Press. Garbade, K. (1996) Fixed Income Analytics, MIT Press. Hiraki, T., Shiraishi, N. and Takezawa, N. (1996) ‘Cointegration, common factors and the term structure of Yen offshore interest rates’, Journal of Fixed Income, December. Ho, T. (1992) ‘Key rate durations: measures of interest rate risk’, Journal of Fixed Income, September. Jennings, A. and McKeown, J. (1992) Matrix Computation (2nd edn), Wiley. Keynes, J. M. (1936) The General Theory of Employment, Interest and Money, Macmillan. Litterman, R. and Scheinkman, J. (1991) ‘Common factors affecting bond returns’, Journal Fixed Income, June. Phoa, W. (1998a) Advanced Fixed Income Analytics, Frank J. Fabozzi Associates. Phoa, W. (1998b) Foundations of Bond Market Mathematics, CMS Research Report. Press, W., Teukolsky, S., Vetterling, W. and Flannery, B. (1992) Numerical Recipes in C: The Art of Scientific Computing (2nd edn), Cambridge University Press. Rebonato, R., and Cooper, I. (1996) ‘The limitations of simple two-factor interest rate models’, Journal Financial Engineering, March.

Note 1

The International datasets used here were provided by Sean Carmody and Richard Mason of Deutsche Bank Securities. The author would also like to thank them for many useful discussions.

6

Implementation of a Value-at-Risk system ALVIN KURUC

Introduction In this chapter, we discuss the implementation of a value-at-risk (VaR) system. The focus will be on the practical nuts and bolts of implementing a VaR system in software, as opposed to a critical review of the financial methodology. We have therefore taken as our primary example a relatively simple financial methodology, a first-order variance/covariance approach. The prototype of this methodology is the basic RiskMetricsT M methodology developed by J. P. Morgan [MR96].1 Perhaps the main challenge in implementing a VaR system is in coming up with a systematic way to express the risk of a bewilderingly diverse set of financial instruments in terms of a relatively small set of risk factors. This is both a financial-engineering and a system-implementation challenge. The body of this chapter will focus on some of the system-implementation issues. Some of the financial-engineering issues are discussed in the appendices.

Overview of VaR methodologies VaR is distinguished from other risk-management techniques in that it attempts to provide an explicit probabilistic description of future changes in portfolio value. It requires that we estimate the probability distribution of the value of a financial portfolio at some specific date in the future, termed the target date. VaR at the 1ña confidence level is determined by the a percentile of this probability distribution. Obviously, this estimate must be based on information that is known today, which we term the anchor date. Most procedures for estimating VaR are based on the concept that a given financial portfolio can be valued in terms of a relatively small of factors, which we term risk factors. These can be prices of traded instruments that are directly observed in the market or derived quantities that are computed from such prices. One then constructs a probabilistic model for the risk factors and derives the probability distribution of the portfolio value as a consequence. To establish a VaR methodology along these lines, we need to 1 Define the risk factors. 2 Establish a probabilistic model for the evolution of these risk factors.

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3 Determine the parameters of the model from statistical data on the risk factors. 4 Establish computational procedures for obtaining the distribution of the portfolio value from the distribution of the risk factors. For example, the RiskMetrics methodology fits into this framework as follows: 1 The risk factors consist of FX rates, zero-coupon discount factors for specific maturities, spot and forward commodity prices, and equity indices. 2 Returns on the risk factors are modeled as being jointly normal with zero means. 3 The probability distribution of the risk factors is characterized by a covariance matrix, which is estimated from historical time series and provided in the RiskMetrics datasets. 4 The portfolio value is approximated by its first-order Taylor-series expansion in the risk factors. Under this approximation, the portfolio value is normally distributed under the assumed model for the risk factors.

Deﬁning the risk factors The risk factors should contain all market factors for which one wishes to assess risk. This will depend upon the nature of one’s portfolio and what data is available. Important variables are typically foreign-exchange (FX) and interest rates, commodity and equity prices, and implied volatilities for the above. In many cases, the market factors will consist of derived quantities, e.g. fixed maturity points on the yield curve and implied volatilities, rather than directly observed market prices. Implied volatilities for interest rates are somewhat problematic since a number of different mathematical models are used and these models can be inconsistent with one another. It should be noted that the risk factors for risk management might differ from those used for pricing. For example, an off-the-run Treasury bond might be marked to market based on a market-quoted price, but be valued for risk-management purposes from a curve built from on-the-run bonds in order to reduce the number of risk factors that need to be modeled. A key requirement is that it should be possible to value one’s portfolio in terms of the risk factors, at least approximately. More precisely, it should be possible to assess the change in value of the portfolio that corresponds to a given change in the risk factors. For example, one may capture the general interest-rate sensitivity of a corporate bond, but not model the changes in value due to changes in the creditworthiness of the issuer. Another consideration is analytical and computational convenience. For example, suppose one has a portfolio of interest-rate derivatives. Then the risk factors must include variables that describe the term structure of interest rates. However, the term structure of interest rates can be described in numerous equivalent ways, e.g. par rates, zero-coupon discount rates, zero-coupon discount factors, etc. The choice will be dictated by the ease with which the variables can be realistically modeled and further computations can be supported.

Probabilistic model for risk factors The basic dichotomy here is between parametric and non-parametric models. In parametric models, the probability distribution of the risk factors is assumed to be of a specific functional form, e.g. jointly normal, with parameters estimated from historical time series. In non-parametric models, the probability distribution of

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changes in the risk factors is taken to be precisely the distribution that was observed empirically over some interval of time in the past. The non-parametric approach is what is generally known as historical VaR. Parametric models have the following advantages: 1 The relevant information from historical time series is encapsulated in a relatively small number of parameters, facilitating its storage and transmission. 2 Since the distribution of the market data is assumed to be of a specific functional form, it may be possible to derive an analytic form for the distribution of the value of the portfolio. An analytic solution can significantly reduce computation time. 3 It is possible to get good estimates of the parameters even if the individual time series have gaps, due to holidays, technical problems, etc. 4 In the case of RiskMetrics, the necessary data is available for free. Parametric models have the following disadvantages: 1 Real market data is, at best, imperfectly described by the commonly used approximating distributions. For example, empirical distributions of log returns have skewed fat tails and it is intuitively implausible to model volatilities as being jointly normal with their underlyings. 2 Since one does not have to worry about model assumptions, non-parametric models are more flexible, i.e. it is easy to add new variables. 3 In the opinion of some, non-parametric models are more intuitive.

Data for probabilistic model For non-parametric models, the data consists of historical time-series of risk factors. For parametric models, the parameters are usually estimated from historical time series of these variables, e.g. by computing a sample covariance matrix. Collection, cleaning, and processing of historical time-series can be an expensive proposition. The opportunity to avoid this task has fueled the popularity of the RiskMetrics datasets.

Computing the distribution of the portfolio value If the risk factors are modeled non-parametrically, i.e. for historical VaR, the time series of changes in the risk factors are applied one by one to the current values of the risk factors and the portfolio revalued under each scenario. The distribution of portfolio values is given simply by the resulting histogram. A similar approach can be used for parametric models, replacing the historical perturbations by pseudorandom ones drawn from the parametric model. This is termed Monte Carlo VaR. Alternatively, if one makes certain simplifying assumptions, one can compute the distribution of portfolio values analytically. For example, this is done in the RiskMetrics methodology. The benefit of analytic methodologies is that their computational burden is much lower. In addition, analytic methods may be extended to give additional insight into the risk profile of a portfolio.

Variance/covariance methodology for VaR We will take as our primary example the variance/covariance methodology, specifically RiskMetrics. In this section, we provide a brief overview of this methodology.

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Fundamental assets For the purposes of this exposition, it will be convenient to take certain asset prices as risk factors. We will term these fundamental assets. In what follows, we will see that this choice leads to an elegant formalism for the subsequent problem of estimating the distribution of portfolio value. We propose using three types of fundamental assets: 1 Spot foreign-exchange (FX) rates. We fix a base currency. All other currencies will be termed foreign currencies. We express spot prices of foreign currencies as the value of one unit of foreign currency in units of base currency. For example, if the base currency were USD, the value of JPY would be in the neighborhood of USD 0.008. 2 Spot asset prices. These prices are expressed in the currency unit that is most natural and convenient, with that currency unit being specified as part of the price. We term this currency the native currency for the asset. For example, shares in Toyota would be expressed in JPY. Commodity prices would generally be expressed in USD, but could be expressed in other currencies if more convenient. 3 Discount factors. Discount factors for a given asset, maturity, and credit quality are simply defined as the ratio of the value of that asset for forward delivery at the given maturity by a counterparty of a given credit quality to the value of that asset for spot delivery. The most common example is discount factors for currencies, but similar discount factors may be defined for other assets such as commodities and equities. Thus, for example, we express the value of copper for forward delivery as the spot price of copper times a discount factor relative to spot delivery. In the abstract, there is no essential difference between FX rates and asset prices. However, we distinguish between them for two reasons. First, while at any given time we work with a fixed base currency, we need to be able to change this base currency and FX rates need to be treated specially during a change of base currency. Second, it is useful to separate out the FX and asset price components of an asset that is denominated in a foreign currency.

Statistical model for fundamental assets Single fundamental assets The essential assumption behind RiskMetrics is that short-term, e.g. daily, changes in market prices of the fundamental assets can be approximated by a zero-mean normal distribution. Let vi (t) denote the present market value of the ith fundamental asset at time t. Define the relative return on this asset over the time interval *t by ri (t)•[vi (tò*t)ñvi (t)]/vi (t). The relative return is modeled by a zero-mean normal distribution. We will term the standard deviation of this distribution the volatility, and denote it by mi (t). Under this model, the absolute return vi (tò*t)ñvi (t) is normally distributed with zero mean and standard deviation pi (t)óvi (t)mi (t). Multiple fundamental assets The power of the RiskMetrics approach comes from modeling the changes in the prices of the fundamental assets by a joint normal distribution which takes into account the correlations of the asset prices as well as their volatilities. This makes it possible to quantify the risk-reduction effect of portfolio diversification.

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Suppose there are m fundamental assets and define the relative-return vector r•[r1 , r2 , . . . , rm ]T (T here denotes vector transpose). The relative-return vector is modeled as having a zero-mean multivariate normal distribution with covariance matrix $.

Statistical data The covariance matrix is usually constructed from historical volatility and correlation data. In the particular case of the RiskMetrics methodology, the data needed to construct the covariance matrix is provided in the RiskMetrics datasets, which are published over the Internet free of charge. RiskMetrics datasets provide a volatility vector (t) and a correlation matrix R to describe the distribution of r. The covariance matrix is given in terms of the volatility vector and correlation matrix by $óT ¥ R where ¥ denotes element-by-element matrix multiplication.

Distribution of portfolio value Primary assets The fundamental assets are spot positions in FX relative to base currency, spot positions in base- and foreign-currency-denominated assets, and forward positions in base currency. We define a primary asset as a spot or forward position in FX or in another base- or foreign-currency-denominated asset expressed in base currency. We will approximate the sensitivity of any primary asset to changes in the fundamental assets by constructing an approximating portfolio of fundamental assets. The process of going from a given position to the approximating portfolio is termed mapping. The rules are simple. A primary asset with present value v has the following exposures in fundamental assets: 1 Spot and forward positions in non-currency assets have an exposure to the corresponding spot asset price numerically equal to its present value. 2 Forward positions have an exposure to the discount factor for the currency that the position is denominated in numerically equal to its present value. 3 Foreign-currency positions and positions in assets denominated in foreign currencies have a sensitivity to FX rates for that foreign currency numerically equal to its present value. Example Suppose the base currency is USD. A USD-denominated zero-coupon bond paying USD 1 000 000 in one year that is worth USD 950 000 today has an exposure of USD 950 000 to the 1-year USD discount factor. A GBP-denominated zero-coupon bond paying GBP 1 000 000 in one year that is worth USD 1 500 000 today has an exposure of USD 1 500 000 to both the 1-year GBP discount factor and the GBP/USD exchange rate. A position in the FTSE 100 that is worth USD 2 000 000 today has an exposure of USD 2 000 000 to both the FTSE 100 index and the GBP/USD exchange rate. Example Suppose the current discount factor for the 1-year USD-denominated zero-coupon

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bond is 0.95, and the current daily volatility of this discount factor is 0.01. Then the present value of the bond with face value USD 1 000 000 is USD 950 000 and the standard deviation of the change in its value is USD 9500. Portfolios of primary assets Gathering together the sum of the portfolio exposures into a total exposure vector v•[vi , v2 , . . . , vm ]T, the model assumed for the fundamental assets implies that the absolute return of the portfolio, i.e. m

m

; vi (tò*t )ñvi (t)ó ; vi (t)ri (t) ió1

ió1

has a zero-mean normal distribution. The distribution is completely characterized by its variance, p2 óvT $v. Example Consider a portfolio consisting of fixed cashflows in amounts USD 1 000 000 and USD 2 000 000 arriving in 1 and 2 years, respectively. Suppose the present value of 1 dollar paid 1 and 2 years from now is 0.95 and 0.89, respectively. The presentvalue vector is then vó[950 000 1 780 000]T Suppose the standard deviations of daily relative returns in 1- and 2-year discount factors are 0.01 and 0.0125, respectively, and the correlation of these returns is 0.8. The covariance matrix of the relative returns is then given by ó

ó

0.012

0.8 · 0.01 · 0.00125

0.8 · 0.01 · 0.00125

0.01252

0.0001

0.0001

0.0001

0.00015625

The variance of the valuation function is given by pó[950 000 1 780 000]

0.0001

0.0001

0.0001

0.00015625

950 000

1 780 000

ó0.9025î108 ò3.3820î108 ò4.9506î108 ó9.2351î108 and the standard deviation of the portfolio is USD 30 389. Value at risk Given that the change in portfolio value is modeled as a zero-mean normal distribution with standard deviation p, we can easily compute the probability of sustaining a loss of any given size. The probability that the return is less than the a percentile point of this distribution is, by definition, a. For example, the 5th percentile of the normal distribution is Bñ$1.645p, so the probability of sustaining a loss of greater than 1.645p is 5%. In other words, at the 95% confidence level, the VaR is 1.645p.

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The 95% confidence level is, of course, arbitrary and can be modified by the user to fit the requirements of a particular application. For example, VaR at the 99% confidence level is B2.326p. Example The 1-year zero-coupon bond from the above examples has a daily standard deviation of USD 9500. Its daily VaR at the 95% and 99% confidence levels is thus USD 15 627.50 and USD 22 097.00, respectively.

Asset-ﬂow mapping Interpolation of maturity points Earlier we outlined the VaR calculation for assets whose prices could be expressed in terms of fundamental asset prices. Practical considerations limit the number of fundamental assets that can be included as risk factors. In particular, it is not feasible to include discount factors for every possible maturity. In this section, we look at approximating assets not included in the set of fundamental assets by linear combinations of fundamental assets. As a concrete example, consider the mapping of future cashflows. The fundamental asset set will contain discount factors for a limited number of maturities. For example, RiskMetrics datasets cover zero-coupon bond prices for bonds maturing at 2, 3, 4, 5, 7, 9, 10, 15, 20, and 30 years. To compute VaR for a real coupon-bearing bond, it is necessary to express the principal and coupon payments maturities in terms of these vertices. An obvious approach is to apportion exposures summing to the present value of each cashflow to the nearest maturity or maturities in the fundamental asset set. For example, a payment of USD 1 000 000 occurring in 6 years might have a present value of USD 700 000. This exposure might be apportioned to exposures to the 5- and 7-year discount factors totaling USD 700 000. In the example given in the preceding paragraph, the condition that the exposures at the 5- and 7-year points sum to 700 000 is obviously insufficient to determine these exposures. An obvious approach would be to divide these exposures based on a simple linear interpolation. RiskMetrics suggests a more elaborate approach in which, for example, the volatility of the 6-year discount factor would be estimated by linear interpolation of the volatilities of the 5- and 7-year discount factors. Exposures to the 5- and 7-year factors would then be apportioned such that the volatility obtained from the VaR calculation agreed with the interpolated 6-year volatility. We refer the interested reader to Morgan and Reuters (1996) for details.

Summary of mapping procedure At this point, it will be useful to summarize the mapping procedure for asset flows in a systematic way. We want to map a spot or forward position in an asset. This position is characterized by an asset identifier, a credit quality, and a maturity. The first step is to compute the present value (PV) of the position. To do so, we need the following information: 1 The current market price of the asset in its native currency.

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2 If the native currency of the asset is not the base currency, we need the current FX rate for the native currency. 3 If the asset is for forward delivery, we need the current discount factor for the asset for delivery at the given maturity by a counterparty of the given credit quality. To perform the PV calculation in a systemic manner, we require the following data structures: 1 We need a table, which we term the asset-price table, which maps the identifying string for each asset to a current price in terms of a native currency and amount. An example is given in Table 6.1. Table 6.1 Example of an asset-price table Asset Copper British Airways

Currency

Price

USD GBP

0.64 4.60

2 We need a table, which we term the FX table, which maps the identifying string for each currency to its value in base currency. An example is given in Table 6.2. Table 6.2 Example of an FX table with USD as base currency Currency USD GBP JPY

Value 1.0 1.6 0.008

3 We need an object, which we term a discounting term structure (DTS), that expresses the value of assets for forward delivery in terms of their value for spot delivery. DTS are specified by an asset-credit quality pair, e.g. USD-Treasury or Copper-Comex. The essential function of the DTS is to provide a discount factor for any given maturity. A simple implementation of a DTS could be based on an ordered list of maturities and discount factors. Discount factors for dates not on the list would be computed by log-linear interpolation. An example is given in Table 6.3.

Table 6.3 Example DTS for Copper-Comex Maturity (years) 0.0 0.25 0.50

Discount factor 1.0 0.98 0.96

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The steps in the PV calculation are then as follows: 1 Based on the asset identifier, look up the asset price in the asset price table. 2 If the price in the asset price table is denominated in a foreign currency, convert the asset price to base currency using the FX rate stored in the FX table. 3 If the asset is for forward delivery, discount its price according to its maturity using the DTS for the asset and credit quality. Example Consider a long 3-month-forward position of 100 000 lb of copper with a base currency of USD. From Table 6.1, the spot value of this amount of copper is USD 64 000. From Table 6.2, the equivalent value in base currency is USD 64 000. From Table 6.3, we see that this value should be discounted by a factor of 0.98 for forward delivery, giving a PV of USD 62 720. Having computed the PV, the next step is to assign exposures to the fundamental assets. We term the result an exposure vector. To facilitate this calculation, it is useful to introduce another object, the volatility term structure (VTS). The data for this object is an ordered, with respect to maturity, sequence of volatilities for discount factors for a given asset and credit quality. Thus, for example, we might have a VTS for USD-Treasury or GBP-LIBOR. The most common example is discount factors for currencies, but similar discount factors may be defined for other assets such as commodities and equities. An example is given in Table 6.4. Table 6.4 Example VTS for Copper-Comex Term (years) 0.0 0.25 0.5

Volatility 0.0 0.01 0.0125

The steps to compute the exposure vector from the PV are as follows: 1 If the asset is not a currency, add an exposure in the amount of the PV to the asset price. 2 If the asset is denominated in a foreign currency, add an exposure in the amount of the PV to the native FX rate. 3 If the asset is for forward delivery, have the VTS add an exposure in the amount of the PV to its discount factor. If the maturity of the asset is between the maturities represented in the fundamental asset set, the VTS will apportion this exposure to the one or two adjacent maturity points. Example Continuing our copper example from above, we get exposures of USD 62 720 to the spot price of copper and the 3-month discount factor for copper. A good example of the utility of the VTS abstraction is given by the specific problem of implementing the RiskMetrics methodology. RiskMetrics datasets supply volatilities for three types of fixed-income assets: money market, swaps, and government bonds. Money-market volatilities are supplied for maturities out to one year, swap-rate volatilities are supplied for maturities from 2 up to 10 years, and government bond rate volatilities are supplied for maturities from 2 out to 30 years. In a

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software implementation, it is desirable to isolate the mapping process from these specifics. One might thus construct two VTS from these data, a Treasury VTS from the money market and government-bond points and a LIBOR VTS from the moneymarket and swap rates, followed by government-bond points past the available swap rates. The mapping process would only deal with the VTS abstraction and has no knowledge of where the underlying data came from.

Mapping derivatives At this point, we have seen how to map primary assets. In particular, in the previous section we have seen how to map asset flows at arbitrary maturity points to maturity points contained within fundamental asset set. In this section, we shall see how to map assets that are derivatives of primary assets contained within the set of risk factors. We do this by approximating the derivative by primary asset flows.

Primary asset values The key to our approach is expressing the value of derivatives in terms of the values of primary asset flows. We will do this by developing expressions for the value of derivatives in terms of basic variables that represent the value of primary asset flows. We shall term these variables primary asset values (PAVs). An example of such a variable would be the value at time t of a US dollar to be delivered at (absolute) time T by the US government. We use the notation USD TTreasury (t) for this variable. We interpret these variables as equivalent ways of expressing value. Thus, just as one might express length equivalently in centimeters, inches, or cubits, one can express value equivalently in units of spot USD or forward GBP for delivery at time T by a counterparty of LIBOR credit quality. Just as we can assign a numerical value to the relative size of two units of length, we can compare any two of our units of value. For example, the spot FX rate GBP/ USD at time t, X GBP/USD (t), can be expressed as X GBP/USD (t)óGBPt (t)/USDt (t)2 Since both USDt (t) and GBPt (t) are units of value, the ratio X GBP/USD(t) is a pure unitless number. The physical analogy is inch/cmó2.54 which makes sense since both inches and centimeters are units of the same physical quantity, length. The main conceptual difference between our units of value and the usual units of length is that the relative sizes of our units of value change with time. The zero-coupon discount factor at time t for a USD cashflow with maturity qóTñt, D USD (t), can be expressed as q (t)óUSDT (t)/USDt (t) D USD q (We use upper case D here just to distinguish it typographically from other uses of d below.) Thus, the key idea is to consider different currencies as completely fungible units for expressing value rather than as incommensurable units. While it is meaningful to assign a number to the relative value of two currency units, an expression like

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USDt (t) is a pure unit of value and has no number attached to it; the value of USDt (t) is like the length of a centimeter. Example The PV as of t 0 ó30 September 1999 of a zero-coupon bond paying USD 1 000 000 at time Tó29 September 2000 by a counterparty of LIBOR credit quality would normally be written as vóUSD 1 000 000 D USD-LIBOR (t 0 ) q where D USD-LIBOR (t 0 ) denotes the zero-coupon discount factor (ZCDF) for USD by a q counterparty of LIBOR credit quality for a maturity of qóTñt 0 years at time t 0 . Using the identity D USD-LIBOR (t 0 )óUSD LIBOR (t 0 )/USD LIBOR (t 0 ) q T t we can rewrite this expression in terms of PAVs by vóUSD LIBOR (t 0 ) 1 000 000 USD LIBOR (t 0 )/USD LIBOR (t 0 ) t0 T t0 ó1 000 000 USD LIBOR (t 0 ) T Example The PV as of time t of a forward FX contract to pay USD 1 600 000 in exchange for GBP 1 000 000 at time Tó29 September 2000, is given by vó1 000 000 GBPT (t)ñ1 600 000 USDT (t) Example The Black–Scholes value for the PV on 30 September 1999 of a GBP call/USD put option with notional principal USD 1 600 000, strike 1.60 USD/GBP, expiration 29 September 2000, and volatility 20% can be expressed in terms of PAVs as vó1 000 000[GBPT (t)'(d1 )ñ1.60 USDT (t)'(d2 )] with d1,2 ó

ln[GBPT (t)/1.60 USDT (t)]ô0.02 0.20

where ' denotes the standard cumulative normal distribution.

Delta-equivalent asset ﬂows Once we have expressed the value of a derivative in terms of PAVs, we obtain approximating positions in primary assets by taking a first-order Taylor-series expansions in the PAVs. The Taylor-series expansion in terms of PAVs provides a first-order proxy in terms of primary asset flows. We term these fixed asset flows the delta-equivalent asset flows (DEAFs) of the instrument. Example Expanding the valuation function from the first example in the previous subsection in a Taylor series in the PAVs, we get dv(t)ó1 000 000 dUSDT (t)

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This says that the delta-equivalent position is a T-forward position in USD of size 1 000 000. This example is a trivial one since the valuation function is linear in PAVs. But it illustrates the interpretation of the coefficients of the first-order Taylor-series expansion as DEAFs. Primary assets have the nice property of being linear in PAVs. More interesting is the fact that many derivatives are linear in PAVs as well. Example Expanding the valuation function from the second example in the previous subsection in a Taylor series in the PAVs, we get dv(t)ó1 000 000 dGBPT (t)ñ1 600 000 dUSDT (t) This says that the delta-equivalent positions are a long T-forward position in GBP of size 1 000 000 and a short T-forward position in USD of size 1 600 000. Of course, options will generally be non-linear in PAVs. Example If the option is at the money, the DEAFs from the third example in the previous subsection are (details are given later in the chapter) dv(t)ó539 828 dGBPT (t)ñ863 725 dUSDT (t) This says that the delta-equivalent positions are T-forward positions in amounts GBP 539 828 and USD ñ863 725.

Gathering portfolio information from source systems Thus far, we have taken a bottom-up approach to the mapping problem; starting with the fundamental assets, we have been progressively expanding the range of assets that can be incorporated into the VaR calculation. At this point, we switch to a more top-down approach, which is closer to the point of view that is needed in implementation. Mathematically speaking, the VaR calculation, at least in the form presented here, is relatively trivial. However, gathering the data that is needed for this calculation can be enormously challenging. In particular, gathering the portfolio information required for the mapping process can present formidable problems. Information on the positions of a financial institution is typically held in a variety of heterogeneous systems and it is very difficult to gather this information together in a consistent way. We recommend the following approach. Each source system should be responsible for providing a description of its positions in terms of DEAFs. The VaR calculator is then responsible for converting DEAFs to exposure vectors for the fundamental assets. The advantage of this approach is that the DEAFs provide a well-defined, simple, and stable interface between the source systems and the VaR computational engine. DEAFs provide an unambiguous financial specification for the information that source systems must provide about financial instruments. They are specified in terms of basic financial-engineering abstractions rather than system-specific concepts, thus facilitating their implementation in a heterogeneous source-system environment. This specification is effectively independent of the particular selections made for the fundamental assets. Since the latter is likely to change over time, it is highly

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desirable to decouple the source–system interface from the internals of the risk system. Changes in the source–system interface definitions are expensive to implement, as they require analysis and changes in all of the source systems. It is therefore important that this interface is as stable as possible. In the presence of heterogeneous source systems, one reasonable approach to gathering the necessary information is to use a relational database. In the following two subsections, we outline a simple two-table design for this information.

The FinancialPosition table The rows in this table correspond to financial positions. These positions should be at the lowest level of granularity that is desired for reporting. One use of the table is as an index to the DEAFs, which are stored in the LinearSensitivities table described in the following subsection. For this purpose, the FinancialPosition table will need columns such as: mnemonicDescription A mnemonic identifier for the position. positionID This is the primary key for the table. It binds this table to the LinearSensitivities table entries. A second use of the FinancialPosition table is to support the selection of subportfolios. For example, it will generally be desirable to support limit setting and reporting for institutional subunits. For this purpose, one might include columns such as: book A string used to identify subportfolios within an institution. counterparty Counterparty institution for the position. For bonds, this will be the issuer. For exchange-traded instruments, this will be the exchange. currency The ISO code for currency in which presentValue is denominated, e.g. ‘USD’ or ‘GBP’. dealStatus An integer flag that describes that execution status of the position. Typically statuses might include analysis, executed, confirmed, etc. entity The legal entity within an institution that is party to the position, i.e. the internal counterpart of counterparty. instrumentType The type of instrument (e.g. swap, bond option, etc.). notional The notional value of the position. The currency units are determined by the contents of the currency column (above). Third, the FinancialPosition table would likely be used for operational purposes. Entries for such purpose might include: linearity An integer flag that describes qualitatively ‘how linear’ the position is. A simple example is described in Table 6.5. Such information might be useful in updating the table. The DEAFs for non-linear instruments will change with market conditions and therefore need to be updated frequently. The DEAFs for linear Table 6.5 Interpretation of linearity ﬂag Value

Accuracy

0 1 2

Linear sensitivities give ‘almost exact’ pricing. Linear sensitivities are not exact due to convexity. Linear sensitivities are not exact due to optionality.

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instruments remain constant between instrument lifecycle events such as resets and settlements. lastUpdated Date and time at which the FinancialPosition and LinearSensitivities table entries for this position were last updated. presentValue The present value (PV) of the position (as of the last update). The currency units for presentValue are determined by the contents of the currency column (above). source Indicates the source system for the position. unwindingPeriod An estimate of the unwinding period for the instrument in business days. (The unwinding period is the number of days it would take to complete that sale of the instrument after the decision to sell has been made.) A discussion of the use of this field is given later in this chapter. validUntil The corresponding entries in the LinearSensitivities table (as of the most recent update) remain valid on or before this date. This data could be used to support as-needed processing for LinearSensitivities table updates. An abbreviated example is shown in Table 6.6. Table 6.6 Example of a FinancialPosition table positionID instrumentType 1344 1378

FRA Swap

currency

notional

presentValue

USD USD

5 000 000 10 000 000

67 000 ñ36 000

The LinearSensitivities table This table is used to store DEAFs. Each row of the LinearSensitivities table describes a DEAF for one of the positions in the FinancialPosition table. This table might be designed as follows: amount The amount of the DEAF. For fixed cashflows, this is just the undiscounted amount of the cashflow. asset For DEAFs that correspond to currency assets, this is the ISO code for the currency, e.g. ‘USD’ or ‘GBP’. For commodities and equities, it is an analogous identifier. date The date of the DEAF. positionID This column indicates the entry in the FinancialPosition table that the DEAF corresponds to. termStructure For cash flows, this generally indicates credit quality or debt type, e.g. ‘LIBOR’ or ‘Treasury’. An example is given in Table 6.7.

Table 6.7 Example of a LinearSensitivity table positionID

Asset

termStructure

Date

Amount

1344 1344 1378

USD USD USD

LIBOR LIBOR LIBOR

15 Nov. 1999 15 Nov. 2000 18 Oct. 1999

ñ5 000 000 5 000 000 ñ35 000

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Translation tables The essential information that the source systems supply to the VaR system is DEAFs. As discussed in the previous section, it is desirable to decouple the generation of DEAFs from the specific choice of fundamental assets in the VaR system. A convenient means of achieving this decoupling is through the use of a ‘translation table’. This is used to tie the character strings used as asset and quality-credit identifiers in the source system to the strings used as fundamental-asset identifiers in the VaR system at run time. For example, suppose the base currency is USD and the asset in question is a corporate bond that pays in GBP. The source system might provide DEAFs in currency GBP and credit quality XYZ_Ltd. The first step in the calculation of the exposure vector for these DEAFs is to compute their PVs. To compute the PV, we need to associate the DEAF key GBP-XYZ_Ltd with an appropriate FX rate and discounting term structure (DTS). The translation table might specify that DEAFs with this key are assigned to the FX rate GBP and the DTS GBP-AA. The second step in the calculation of the exposure vector is assignment to appropriate volatility factors. The translation table might specify that DEAFs with this key are assigned to the FX volatility for GBP and the VTS GBP-LIBOR. The translation table might be stored in a relational database table laid out in the following way: externalPrimaryKey This key would generally be used to identify the asset in question. For example, for a currency one might use a standard currency code, e.g. USD or GBP. Similarly, one might identify an equity position by its symbol, and so forth. externalSecondaryKey This key would generally be used to specify discounting for forward delivery. For example, currency assets could be specified to be discounted according to government bond, LIBOR, and so forth. DTSPrimaryKey This key is used to identify asset prices in the asset price table as well as part of the key for the DTS. DTSSecondaryKey Secondary key for DTS. VTSPrimaryKey This key is used to identify asset volatilities in the asset price volatility table as well as part of the key for the VTS. VTSSecondaryKey Secondary key for VTS. A timely example for the need of the translation table comes with the recent introduction of the Euro. During the transition, many institutions will have deals denominated in Euros as well as DEM, FRF, and so on. While it may be convenient to use DEM pricing and discounting, it will generally be desirable to maintain just a Euro VTS. Thus, for example, it might be desirable to map a deal described as DEMLIBOR in a source system, to a DEM-Euribor DTS and a Euro-Euribor VTS. This would result in the table entries shown in Table 6.8. At this point, it may be worthwhile to point out that available discounting data will generally be richer than available volatility data. Thus, for example, we might have a AA discounting curve available, but rely on the RiskMetrics dataset for volatility information, which covers, at most, two credit-quality ratings.

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DEM LIBOR DEM LIBOR EUR Euribor

Design strategy summary There is no escaping the need to understand the semantics of instrument valuation. The challenge of doing this in a consistent manner across heterogeneous systems is probably the biggest single issue in building a VaR system. In large part, the design presented here has been driven by the requirement to make this as easy as possible. To summarize, the following analysis has to be made for each of the source systems: 1 For each instrument, write down a formula that expresses the present value in terms of PAVs. 2 Compute the sensitivities of the present value with respect to each of the PAVs. 3 If the Taylor-series expansion of the present value function in PAVs has coefficient ci with respect to the ith PAV, its first-order sensitivity to changes in the PAVs is the same as that of a position in amount ci in the asset corresponding to the PAV. Thus the sensitivities with respect to PAVs may be interpreted as DEAFs. In addition, we need to map the risk factors keys in each source system to appropriate discounting and volatility keys in the VaR system.

Covariance data Construction of volatility and correlation estimates At the heart of the variance/covariance methodology for computing VaR is a covariance matrix for the relative returns. This is generally computed from historical time series. For example, if ri (t j ), jó1, . . . , n, are the relative returns of the ith asset over nò1 consecutive days, then the variance of relative returns can be estimated by the sample variance n

m i2 ó ; r j2 (t j )/n jó1

In this expression, we assume that relative returns have zero means. The rationale for this is that sample errors for estimating the mean will often be as large as the mean itself (see Morgan and Reuters, 1996). Volatility estimates provided in the RiskMetrics data sets use a modification of this formula in which more recent data are weighted more heavily. This is intended to make the estimates more responsive to changes in volatility regimes. These estimates are updated daily. Estimation of volatilities and correlations for financial time series is a complex subject, which will not be treated in detail here. We will content ourselves with pointing out that the production of good covariance estimates is a laborious task.

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First, the raw data needs to be collected and cleaned. Second, since many of the risk factors are not directly observed, financial abstractions, such as zero-coupon discount factor curves, need to be constructed. Third, one needs to deal with a number of thorny practical issues such as missing data due to holidays and proper treatment of time-series data from different time zones. The trouble and expense of computing good covariance matrices has made it attractive to resort to outside data providers, such as RiskMetrics.

Time horizon Ideally, the time interval between the data points used to compute the covariance matrix should agree with the time horizon used in the VaR calculation. However, this is often impractical, particularly for longer time horizons. An alternative approach involves scaling the covariance matrix obtained for daily time intervals. Assuming relative returns are statistically stationary, the standard deviation of changes in portfolio value over n days is n times that over 1 day. The choice of time horizon depends on both the nature of the portfolio under consideration and the perspective of the user. To obtain a realistic estimate of potential losses in a portfolio, the time horizon should be at least on the order of the unwinding period of the portfolio. The time horizon of interest in an investment environment is generally longer than that in a trading environment.

Heterogeneous unwinding periods and liquidity risk As mentioned in the previous subsection a realistic risk assessment needs to incorporate the various unwinding periods present in a portfolio. If a position takes 5 days to liquidate, then the 1-day VaR does not fully reflect the potential loss associated with the position. One approach to incorporating liquidity effects into the VaR calculation involves associating an unwinding period with each instrument. Assuming that changes in the portfolio’s value over non-overlapping time intervals are statistically independent, the variance of the change in the portfolio’s value over the total time horizon is equal to the sum of the variances for each time-horizon interval. In this way, the VaR computation can be extended to incorporate liquidity risk. Example Consider a portfolio consisting of three securities, A, B, and C, with unwinding periods of 1, 2, and 5 days, respectively. The total variance estimate is obtained by adding a 1-day variance estimate for a portfolio containing all three securities, a 1-day variance estimate for a portfolio consisting of securities B and C, and a 3-day variance estimate for a portfolio consisting only of security C. The above procedure is, of course, a rather crude characterization of liquidity risk and does not capture the risk of a sudden loss of liquidity. Nonetheless, it may be better than nothing at all. It might be used, for example, to express a preference for instrument generally regarded as liquid for the purpose of setting limits.

Change of base currency With the system design that we have described, a change of base currency is relatively simple. First of all, since the DEAFs are defined independently of base currency,

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these remain unchanged. The only aspect of the fundamental assets that needs to be changed is the FX rates. The FX rate for the new base currency is removed from the set of risk factors and replaced by the FX rate for the old base currency. In addition, the definition of all FX rates used in the system need to be changed so that they are relative to the new base currency. Thus the rates in the FX table need to be recomputed. In addition, the volatilities of the FX rates and the correlations of FX rates with all other risk factors need to be recomputed. Fortunately, no new information is required, the necessary volatilities and correlations can be computed from the previously ones. The justification for this statement is given later in this chapter.

Information access It is useful to provide convenient access to the various pieces of information going into the VaR calculation as well as the intermediate and final results.

Input information Input information falls into three broad categories, portfolio data, current market data, and historical market data. Portfolio data In the design outline presented here, the essential portfolio data are the DEAF proxies stored in the FinancialPosition and LinearSensitivities tables. Current market data This will generally consist of spot prices and DTSs. It is convenient to have a graphical display available for the DTS, both in the form of zero-coupon discount factors as well as in the form of zero-coupon discount rates. Historical market data Generally speaking, this will consist of the historical covariance matrix. It may be more convenient to display this as a volatility vector and a correlation matrix. Since the volatility vector and correlation matrix will be quite large, some thought needs to be given as to how to display them in a reasonable manner. For example, reasonable size portions of the correlation matrix may be specified by limiting each axis to factors relevant to a single currency. In addition, it will be desirable to provide convenient access to the VTS, in order to resolve questions that may arise about the mapping of DEAFs.

Intermediate results – exposure vectors It is desirable to display the mapped exposure vectors. In the particular case of RiskMetrics, we have found it convenient to display mapped exposure vectors in the form of a matrix. The rows of this matrix correspond to asset class and maturity identifiers in the RiskMetrics data sets and the columns of this matrix correspond to currency codes in the RiskMetrics data set.

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VaR results In addition to total VaR estimates, it is useful to provide VaR estimate for individual asset classes (interest, equities, and FX) as well as for individual currencies.

Portfolio selection and reporting The VaR system must provide flexible and powerful facilities to select deals and portfolios for analysis. This is conveniently done using relational database technology. This allows the users to ‘slice and dice’ the portfolio across a number of different axes, e.g. by trader, currency, etc. It will be convenient to have persistent storage of the database queries that define these portfolios. Many institutions will want to produce a daily report of VaR broken out by sub-portfolios.

Appendix 1: Mathematical description of VaR methodologies One of the keys to the successful implementation of a VaR system is a precise financial-engineering design. There is a temptation to specify the design by providing simple examples and leave the details of the treatment of system details to the implementers. The result of this will often be inconsistencies and confusing behavior. To avoid this, it is necessary to provide an almost ‘axiomatic’ specification that provides an exact rule to handle the various contingencies that can come up. This will typically require an iterative approach, amending the specification as the implementers uncover situations that are not clearly specified. Thus, while the descriptions that follow may appear at first unnecessarily formal, experience suggests that a high level of precision in the specification pays off in the long run. The fundamental problem of VaR, based on information known at the anchor time, t 0 , is to estimate the probability distribution of the value of one’s financial position at the target date, T. In principle, one could do this in a straightforward way by coming up with a comprehensive probabilistic model of the world. In practice, it is necessary to make heroic assumptions and simplifications, the various ways in which these assumptions and simplifications are made lead to the various VaR methodologies. There is a key abstraction that is fundamental to almost all of the various VaR methodologies that have been proposed. This is that one restricts oneself to estimating the profit and loss of a given trading strategy that is due to changes in the value of a relatively small set of underlying variables termed risk factors. This assumption can be formalized by taking the risk factors to be the elements of a m-dimensional vector m t that describes the ‘instantaneous state’ of the market as it evolves over time. One then assumes that the value of one’s trading strategy at the target date expressed in base currency is given by a function vt 0,T : Rm î[t 0 , T ]R of the trajectory of m t for t é [t 0 , T ]. We term a valuation function of this form a future valuation function since it gives the value of the portfolio in the future as a function of the evolution of the risk factors between the anchor and target date.3 Note that vt 0,T is defined so that any dependence on market variables prior to the anchor date t 0 , e.g. due to resets, is assumed to be known and embedded in the valuation function vt 0,T .

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The same is true for market variables that are not captured in m t . For example, the value of a trading strategy may depend on credit spreads or implied volatilities that are not included in the set of risk factors. These may be embedded in the valuation function vt 0,T , but are then treated as known, i.e. deterministic, quantities. To compute VaR, one postulates the existence of a probability distribution kT on the evolution of m t in the interval [t 0 , T ]. Defining M[t 0,T ] •{m t : t é [t 0 , T ]}, the quantity vt 0,T (M[t 0,T ] ), where M[t 0,T ] is distributed according to kT , is then a real-valued random variable. VaR for the time horizon Tñt0 at the 1ña confidence level is defined to be the a percentile of this random variable. More generally, one would like to characterize the entire distribution of vt 0,T (M[t 0,T ] ). The problem of computing VaR thus comes down to computing the probability distribution of the random variable vt 0,T (M[t 0,T ] ). To establish a VaR methodology, we need to 1 2 3 4

Define the market-state vector m t . Establish a probabilistic model for M[t 0,T ] . Determine the parameters of the model for M[t 0,T ] based on statistical data. Establish computational procedures for obtaining the distribution of vt 0,T (M[t 0,T ] ).

In fact, most existing procedures for computing VaR have in common a stronger set of simplifying assumptions. Instead of explicitly treating a trading strategy whose positions may evolve between the anchor and target date, they simply consider the existing position as of the anchor date. We denote the valuation function as of the anchor date as a function of the risk factors by vt 0 : Rm R. We term a valuation function of this form a spot valuation function. Changes in the risk factors, which we term perturbations, are then modeled as being statistically stationary. The VaR procedure then amounts to computing the probability distribution of vt 0 (m t 0 ò*m), where *m is a stochastic perturbation.

Appendix 2: Variance/covariance methodologies In this appendix, we formalize a version of the RiskMetrics variance/covariance methodology. The RiskMetrics Technical Document sketches a number of methodological choices by example rather than a single rigid methodology. This has the advantage that the user can tailor the methodology somewhat to meet particular circumstances. When it comes time for software implementation, however, it is advantageous to formalize a precise approach. One source of potential confusion is in the description of the statistical model for portfolio value. In some places, it is modeled as normally distributed (see Morgan and Reuters, 1996, §1.2). In other places it is modeled as log-normally distributed (Morgan and Reuters, 1996, §1.1). The explanation for this apparent inconsistency is that RiskMetrics depends on an essential approximation. The most natural statistical model for changes in what we earlier termed fundamental asset prices is a log-normal model. An additional attractive feature of the log-normal model for changes in fundamental asset prices is that it implies that primary asset prices are log-normal as well. However, the difficulty with the log-normal model is that the distribution of a portfolio containing more than one asset is analytically intractable. To get around this difficulty, the RiskMetrics methodology uses an approximation in which relative changes in the primary assets are equated to changes in the log prices of these assets. Since relative returns over

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a short period of time tend to be small, the approximation is a reasonable one. For example, for a relative change of 1%, ln1.01ñln1.0óln(1.01) B0.00995. Perhaps the main danger is the possibility of conceptual confusion, the approximation means that a primary asset has a different statistical model when viewed as a market variable than when viewed as a financial position. In this appendix, we provide a formalization of these ideas that provides a consistent approach to multiple-asset portfolios.

Risk factors While in the body of this chapter we loosely referred to the fundamental assets as the risk factors, we now more precisely take the risk factors to be the logarithms of the fundamental asset prices. We denote the log price of the ith fundamental asset at time t by l i (t) for ió1, . . . , m.

Statistical model for risk factors Changes in the log asset prices between the anchor and target dates are assumed to have a jointly normal distribution with zero means. We denote the covariance matrix of this distribution by $. When *t is small, e.g. on the order of one day, the mean of this distribution is small and is approximated as being equal to zero.

Distribution of portfolio value We assume that we can write the valuation function of the portfolio as a function of the risk-factor vector l•[l 1 , . . . , l m ]T. Our procedure for computing the distribution of the portfolio is then defined by approximating the valuation function by its firstorder linear approximation in l. Thus, the first-order approximation to the change in the valuation function is given by dvB

Lv dl Ll

where

Lv Lv • Ll Ll2

Lv Ll m

We thus formalize what we termed an exposure to the ith risk factor as the partial derivative of the valuation function with respect to l i . Under this approximation, it follows that dv is normally distributed with mean zero and variance p2 ó

Lv LvT $ Ll Ll

Mapping of primary assets We now show that the above definitions agree with the mapping procedure for primary assets given in the body of this chapter. To begin with, consider, for example,

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a fundamental asset such as a zero-coupon bond in the base currency USD with face value N and maturity q. The valuation function for this bond is given by vt 0 óUSD ND USD (t 0 ) q We have Lvt 0 Lv óD USD (t 0 ) USDt 0 q USD L ln D q (t 0 ) LD q (t 0 ) óvt 0 We thus see that the required exposure to the discount factor is the present value of the bond. As another example, consider a primary asset such as a zero-coupon bond in a foreign currency with face value GBP N and maturity q. The valuation function for this bond from a USD perspective is given by vt 0 óUSD NX GBP/USD (t 0 )D qGBP (t 0 ) where X(t 0 ) is the FX rate for the foreign currency. A calculation similar to the one in the preceding paragraph shows that Lvt 0 Lvt 0 ó óvt 0 GBP L ln D q (t 0 ) L ln X GBP/USD (t 0 ) We thus see that the required exposure to the discount factor and the FX rate are both equal to the present value of the bond in the base currency. In summary, we can express the value of any primary asset for spot or forward delivery as a product of fundamental asset prices. It follows that a primary asset has an exposure to the fundamental asset prices affecting its value equal to the present value of the primary asset. This is the mathematical justification for the mapping rule presented in the body of this chapter.

Mapping of arbitrary instruments Arbitrary instruments, in particular derivatives, are treated by approximating them by positions in primary instruments. The procedure is to express the valuation function of the instrument in terms of PAVs. The approximating positions in primary instruments are then given by the coefficients of the first-order Taylor series expansion of the valuation function in the PAVs. To see this, suppose the valuation function v for a given derivative depends on m PAVs, which we denote PAVj , ió1, . . . , m. We write out the first-order expansion in PAVs: m

Lv dPAVi ió1 LPAVi

dvó ;

Now consider a portfolio consisting of positions in amounts Lv/LPAVi of the primary asset corresponding to the ith PAV. It is clear that the sensitivity of this portfolio to the PAVs is given by the right-hand side of the above equation. To summarize, the Taylor-series expansion in PAVs provides a first-order proxy in terms of primary asset flows. We term these fixed asset flows the delta-equivalent asset flows (DEAFs) of the instrument. One of the attractive features of expressing valuation functions in terms of PAVs is

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the base-currency independence of the resulting expressions. Consider, for example, the PV at time t of a GBP-denominated zero-coupon bond, paying amount N at time T. The expression in PAVs is given by vt óN GBPT (t) regardless of the base currency. In order to appreciate the base-currency independence of this expression, start from a USD perspective: vt óUSDt (t)NX GBP/USD (t)D qGBP (t) óUSDt (t)N

GBPt (t)GBPT (t) USDt (t)GBPt (t)

óN GBPT (t) As an example of a non-trivial DEAF calculation, we consider an FX option. The Black–Scholes value for the PV at time t of a GBP call/USD put option with strike ¯ GBP/USD with time qóTñt to expiry is given by X ¯ GBP/USD D USD vt óUSDt (t)[X GBP/USD (t)D qGBP (t)'(d1 )ñX (t)'(d2 )] q ¯ GBP/USD is the strike, ' denotes the cumulative probability distribution function where X for a standard normal distribution, and d1,2 ó

¯ GBP/USD )òln(D qGBP /D USD )ôp2q/2 ln(X GBP/USD /X q pq

where p is the volatility of the FX rate (Hull, 1997). Making use of the identities X GBP/USD (t)óGBPt (t)/USDt (t) D qGBP (t)GBPt (t)óGBPT (t) and (t)USDt (t)óUSDT (t) D USD q the equivalent expression in terms of PAVs is given by ¯ GBP/USD USDT '(d2 ) vt óGBPT '(d1 )ñX with d1,2 ó

¯ GBP/USD )ôp2q/2 ln[GBPT (t)/USDT (t)]ñln(X pq

Some calculation then gives the first-order Taylor-series expansion ¯ GBP/USD '(d2 )dUSDT (t) dvt ó'(d1 )dGBPT (t)ñX This says that the delta-equivalent positions are T-forward positions in amounts ¯ GBP/USD '(d1 ). GBP '(d1 ) and USD X

Change of base currency A key and remarkable fact is that the log-normal model for FX risk factors is invariant under a change of base currency. That is, if one models the FX rates relative to a

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given base currency as jointly log normal, the FX rates relative to any other currency induced by arbitrage relations are jointly log normal as well. Moreover, the covariance matrix for rates relative to a new base currency is determined from the covariance matrix for the old base currency. These two facts make a change of base currency a painless operation. These nice properties hinge on the choice of log FX rates as risk factors. For example, suppose the original base currency is USD so that the FX risk factors consist of log FX rates relative to USD. The statistical model for risk factors is that changes in log asset prices have a joint normal distribution. If we were to change base currency to, say GBP, then the new risk factors would be log FX rates relative to GBP. Thus, for example, we would have to change from a risk factor of ln X GBP/USD to ln X USD/GBP . But, using the PAV notation introduced earlier, ln X USD/GBP óln

USD GBP

ó ñln

GBP USD

ó ñln X GBP/USD Thus the risk factor from a GBP respective is just a scalar multiple of the risk factor from a USD perspective. For a third currency, say JPY, we have ln X JPY/GBP óln óln

JPY GBP JPY GBP ñln USD USD

óln X JPY/USD ñln X GBP/USD Thus the log FX rate for JPY relative to GBP is a linear combination of the log FX rates for JPY and GBP relative to USD. We see that the risk factors with GBP as base currency are just linear combinations of the risk factors with USD as base currency. Standard results for the multivariate normal distribution show that linear combination of zero-mean jointly normal random variables are also zero-mean and jointly normal. Moreover, the covariance matrix for the new variables can be expressed in terms of the covariance matrix for the old variables. We refer the reader to Morgan and Reuters (1996, §8.4) for the details of these calculations.

Appendix 3: Remarks on RiskMetrics The methodology that we described in this chapter agrees quite closely with that presented in RiskMetrics, although we deviate from it on some points of details. In this appendix, we discuss the motivations for some of these deviations.

Mapping of non-linear instruments In the previous appendix we described a general approach to treating instruments whose value depends non-linearly on the primary assets. This approach involved computing a first-order Taylor series expansion in what we termed PAVs. The

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coefficients of this expansion gave positions in primary assets that comprised a firstorder proxy portfolio to the instrument in question. The RiskMetrics Technical Document (Morgan and Reuters, 1996) describes a similar approach to mapping non-linear FX and/or interest-rate derivatives. The main difference is that the firstorder Taylor series expansion of the PV function is taken with respect to FX rates and zero-coupon discount factors (ZCDF). Using this expansion, it is possible to construct a portfolio of proxy instruments whose risk is, to first order, equivalent. This approach, termed the delta approximation, is illustrated by a number of relatively simple examples in Morgan and Reuters (1996). While the approach described in Morgan and Reuters (1996) is workable, it has a number of significant drawbacks relative to the DEAF approach: 1 As will be seen below, the proxy instrument constructed to reflect risk with respect to ZCDFs in foreign currencies does not correspond to a commonly traded asset; it is effectively a zero-coupon bond in a foreign currency whose FX risk has been removed. In contrast, the proxy instrument for the DEAF approach, a fixed cashflow, is both simple and natural. 2 Different proxy types are used for FX and interest-rate risk. In contrast, the DEAF approach captures both FX and interest-rate risk with a single proxy type. 3 The value of some primary instruments is non-linear in the base variables, with the proxy-position depending on current market data. This means they need to be recomputed whenever the market data changes. In contrast, primary instruments are linear in the DEAF approach, so the proxy positions are independent of current market data. 4 The expansion is base-currency dependent, and thus needs to be recomputed every time the base currency is changed. In contrast, proxies in the DEAF approach are base-currency independent. In general, when an example of first-order deal proxies is given in Morgan and Reuters (1996), the basic variable is taken to be the market price. This is stated in Morgan and Reuters (1996, table 6.3), where the underlying market variables are stated to be FX rates, bond prices, and stock prices. For example, in Morgan and Reuters (1996, §1.2.2.1), the return on a DEM put is written as dDEM/USD , where rDEM/USD is the return on the DEM/USD exchange rate and d is the delta for the option.4 Consider the case of a GBP-denominated zero-coupon bond with a maturity of q years. For a USD-based investor, the PV of this bond is given by vt 0 óUSD NX GBP/USD (t 0 )D qGBP (t 0 ) where X GBP/USD is the (unitless) GBP/USD exchange rate, D qGBP (t 0 ) denotes the q-year ZCDF for GBP, and N is the principal amount of the bond. We see that the PV is a non-linear function of the GBP/USD exchange rate and the ZCDF for GBP. (While the valuation function is a linear function of the risk factors individually, it is quadratic in the set of risk factors as a whole.) Expanding the valuation function in a first-order Taylor series in these variables, we get dvt ó

Lv Lv dD qGBP ò dX GBP/USD LD qGBP LX GBP/USD

óUSD N(X GBP/USD dD qGBP òD qGBP dX GBP/USD )

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This says that the delta-equivalent position is a spot GBP position of ND TGBP and a q-year GBP N ZCDF position with an exchange rate locked at X GBP/USD . Note that these positions are precisely what one would obtain by following the standard mapping procedure for foreign cashflows in RiskMetrics (see example 2 in Morgan and Reuters, 1996). This non-linearity contrasts with the DEAF formulation, where we have seen that the value of this instrument is a linear function of the PAV GBPT (T) and the delta-equivalent position is just a fixed cashflow of GBP N at time T.

Commodity prices Our treatment of commodities is somewhat different from the standard RiskMetrics approach, although it should generally give similar results. The reason for this is that the standard RiskMetrics approach is essentially ‘dollar-centric’, the RiskMetrics data sets give volatilities for forward commodities in forward dollars. The problem with this is that it conflates the commodity term structure with the dollar term structure. For example, to express the value of a forward copper position in JPY, we convert price in forward dollars to a price in spot dollars, using the USD yield curve, and then to JPY, based on the JPY/USD spot rate. As a result, a simple forward position becomes enmeshed with USD interest rates without good reason. To carry out our program of treating commodities in the same way as foreign currencies, we need to convert the volatility and correlation data for commodities in the RiskMetrics data sets so that they reflect future commodity prices expressed in terms of spot commodity instead of future dollars. Fortunately, the volatilities and correlations for the commodity discount factors can be derived from those provided in the RiskMetrics data sets. The flavor of the calculations is similar to that for the change of base currency described in the previous appendix. This transformation would be done when the RiskMetrics files are read in.

Appendix 4: Valuation-date issues We noted above that most VaR methodologies do not explicitly deal with the entire evolution of the risk factors and the valuation function between the anchor date and target date. Rather they just evaluate the valuation function as of the anchor date at perturbed values of the risk factors for the anchor date. This simplification, while done for strong practical reasons, can lead to surprising results.

Need for future valuation functions Consider, for example, a cashflow in amount USD N in the base currency payable one year after the anchor date. We would write the valuation function with respect to the USD base currency in terms of our risk factors as vt 0 óUSD ND1 (t 0 ) where D1(t 0 ) denotes the 1-year discount factor at time t 0 . This equation was the starting point for the analysis in the example on page 191, where we calculated VaR for a 1-day time horizon. Scaling this example to a 1-year time horizon, as discussed on page 201 would give a VaR approximately 16 times as large. But this is crazy! The value of the bond in one year is N, so there is no risk at all and VaR should be equal to zero. What went wrong? The problem is that we based

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our calculation on the valuation function as of the anchor date. But at the target date, the valuation function is given simply by vT óND0 (T ) óN Thus the formula that gives the value of the instrument in terms of risk factors changes over time. This is due to the fact that our risk factors are fixed-maturity assets. (Changing to risk factors with fixed-date assets is not a good solution to this problem as these assets will not be statistically stationary, indeed they will drift to a known value.) The first idea that comes to mind to fix this problem is to use the spot valuation function as of the target date instead of the anchor date. While this would fix the problem for our 1-year cashflow, it causes other problems. For example, consider an FRA that resets between the anchor and target dates and pays after the target date. The value of this instrument as of the target date will depend on the risk-factor vector between the anchor and target date and this dependence will not be captured by the spot valuation function at the target date. For another example, consider a 6-month cashflow. In most systems, advancing the valuation date by a year and computing the value of this cashflow would simply return zero. It therefore appears that in order to properly handle these cases, the entire trajectory of the risk factors and the portfolio valuation function needs to be taken into account, i.e. we need to work with future valuation functions are described in Appendix 1.

Path dependency In some instances, the future valuation function will only depend on the marketstate vector at the target time T. When we can write the future valuation function as a function of mT alone, we say that the future valuation function is path-independent. For example, consider a zero-coupon bond maturing after time T. The value of the bond at time T will depend solely on the term structure as of time T and is thus path independent. It should be recognized that many instruments that are not commonly thought of as being path dependent are path dependent in the context of future valuation. For example, consider a standard reset-up-front, pay-in-arrears swap that resets at time t r and pays at time t p , with t 0 \t r \T\t p . To perform future valuation at time T, we need to know the term-structure at time T, to discount the cashflow, as well as the term-structure as of time t r , to fix the reset. Thus, we see that the swap is path dependent. Similar reasoning shows that an option expiring at time t e and paying at time t p , with t 0 \t e \T\t p , is, possibly strongly, path dependent.

Portfolio evolution Portfolios evolve over time. There is a natural evolution of the portfolio due to events such as coupon and principal payments and the expiration and settlement of forward and option contracts. To accurately characterize the economic value of a portfolio at the target date, it is necessary to track cashflows that are received between the anchor and target dates and to make some reasonable assumptions as to how these received cashflows are reinvested. Probably the most expedient approach to dealing with the effects of received cashflows is to incorporate a reinvestment assumption into the VaR methodology.

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There are two obvious choices; a cashflow is reinvested in a cash account at the riskfree short rate or the coupon is reinvested in a zero-coupon bond maturing at time T, with the former choice being more natural since it is independent of the time horizon. If we make the latter choice, we need to know the term structure at time t p ; if we make the former choice, we need to know the short rate over the interval [t p , T ].

Implementation considerations Path-dependence and received cashflows in the context of future valuation can create difficulties in the implementation of VaR systems. The reason is that most instrument implementations simply do not support future valuation in the sense described above, distinguishing between anchor and target dates. Rather, all that is supported is a spot valuation function, with dependence on past market data, e.g. resets, embedded in the valuation function through a separate process. Thus the usual practice is to generate samples of the instantaneous market data vector as of time T, mT , and evaluate the instantaneous valuation function at time t 0 or T, i.e. vt 0 (m T ) or vT (mT ). It is easy to think of cases where this practice gives seriously erroneous results. An obvious problem with using vt 0 (m T ) as a proxy for a future valuation function is that it will erroneously show market risk for a fixed cashflow payable at time T. A better choice is probably vT (mT ), but this will return a PV of zero for a cashflow payable between t 0 and T. In addition, one needs to be careful about resets. For example, suppose there is a reset occurring at time t r with t 0 \t r \T. Since the spot valuation function only has market data as of time T available to it, it cannot determine the correct value of the reset, as it depends on past market data when viewed from time T. (In normal operation, the value of this reset would have been previously set by a separate process.) How the valuation function treats this missing reset is, of course, implementation dependent. For example, it may throw an error message, which would be the desired behavior in a trading environment or during a revaluation process, but is not very helpful in a simulation. Even worse, it might just fail silently, setting the missing reset to 0 or some other arbitrary value. To solve this problem completely, instrument implementations would have to support future valuation function with distinct anchor and target dates. Such a function would potentially need to access market-data values for times between the anchor and target date. As of today, valuation functions supporting these semantics are not generally available. Even if they were, a rigorous extension of even the parametric VaR methodology that would properly account for the intermediate evolutions of variables would be quite involved. We can, however, recommend a reasonably simple approximation that will at least capture the gross effects. We will just sketch the idea at a conceptual level. First of all, we construct DEAF proxies for all instruments based on the spot valuation function as of the anchor date. We then modify the mapping procedure as follows: 1 Exposures are numerically equal to the forward value of the asset at the target date rather than the present value at the anchor date. Forward values of assets for delivery prior to the target date are computed by assuming that the asset is reinvested until the target date at the forward price as of the anchor date. 2 Asset flows between the anchor and target dates do not result in discount-factor risk. Asset flows after the target date are mapped to discount factors with maturities equal to the difference between the delivery and target date.

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Glossary of terms Absolute Return: The change in value of an instrument or portfolio over the time horizon. Analytic VaR: Any VaR methodology in which the distribution of portfolio value is approximated by an analytic expression. Variance/covariance VaR is a special case. Anchor Date: Roughly speaking, ‘today’. More formally, the date up until which market conditions are known. DEAFs: Delta-equivalent asset flows. Primary asset flows that serve as proxies for derivative instruments in a VaR calculation. DTS: Discounting term structure. Data structure used for discounting of future asset flows relative to their value today. Exposure: The present value of the equivalent position in a fundamental asset. Fundamental Asset: A fundamental market factor in the form of the market price for a traded asset. Future Valuation Function: A function that gives the value of a financial instrument at the target date in terms of the evolution of the risk factors between the anchor and target date. FX: Foreign exchange. Historical VaR: A value-at-risk methodology in which the statistical model for the risk factors is directly tied to the historical time series of changes in these variables. Maturity: The interval of time between the anchor date and the delivery of a given cashflow, option expiration, or other instrument lifecycle event. Monte Carlo VaR: A value-at-risk methodology in which the distribution of portfolio values is estimated by drawing samples from the probabilistic model for the risk factors and constructing a histogram of the resulting portfolio values. Native Currency: The usual currency in which the price of a given asset is quoted. PAV: Primary asset value. A variable that denotes the value of an asset flow for spot or future delivery by a counterparty of a given credit quality. PV: Present value. The value of a given asset as of the anchor date. Relative Return: The change in value of an asset over the time horizon divided by its value at the anchor date. Risk Factors: A set of market variables that determine the value of a financial portfolio. In most VaR methodologies, the starting point is a probabilistic model for the evolution of these factors. Spot Valuation Function: A function that gives the value of a financial instrument at a given date in terms of the evolution of the risk factor vector for that date. Target Date Date in the future on which we are assessing possible changes in portfolio value. Time Horizon: The interval of time between the anchor and target dates. VaR: Value at risk. A given percentile point in the profit and loss distribution between the anchor and target date. Variance/Covariance VaR: A value-at-risk methodology in which the risk factors are modeled as jointly normal and the portfolio value is modeled as a linear combination of the risk factors and hence is normally distributed. VTS: Volatility term structure. Data structure used for assigning spot and future asset flows to exposure vectors. ZCDF: Zero-coupon discount factor. The ratio of the value of a given asset for future delivery with a given maturity by a counterparty of a given credit quality to the spot value of the asset.

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Acknowledgments This chapter largely reflects experience gained in building the Opus Value at Risk system at Renaissance Software and many of the ideas described therein are due to the leader of that project, Jim Lewis. The author would like to thank Oleg Zakharov and the editors of this book for their helpful comments on preliminary drafts of this chapter.

Notes 1

RiskMetrics is a registered trademark of J. P. Morgan. We use the term spot rate to mean for exchange as of today. Quoted spot rates typically reflect a settlement lag, e.g. for exchange two days from today. There is a small adjustment between the two to account for differences in the short-term interest rates in the two currencies. 3 Our initial impulse was to use the term forward valuation function, but this has an established and different meaning. The forward value of a portfolio, i.e. the value of the portfolio for forward delivery in terms of forward currency, is a deterministic quantity that is determined by an arbitrage relationship. In contrast, the future value is a stochastic quantity viewed from time t 0 . 4 The RiskMetrics Technical Document generally uses the notation r to indicate log returns. However, the words in Morgan and Reuters (1996, §1.2.2.1) seem to indicate that the variables are the prices themselves, not log prices. 2

References Hull, J. C. (1997) Options, Futures, and other Derivatives, Prentice-Hall, Englewood Cliffs, NJ, third edition. Morgan, J. P. and Reuters (1996) RiskMetricsT M-Technical Document, Morgan Guaranty Trust Company, New York, fourth edition.

7

Additional risks in fixed-income markets TERI L. GESKE

Introduction Over the past ten years, risk management and valuation techniques in fixed-income markets have evolved from the use of static, somewhat naı¨ve concepts such as Macaulay’s duration and nominal spreads to option-adjusted values such as effective duration, effective convexity, partial or ‘key rate’ durations and option-adjusted spreads (OAS). This reflects both the increased familiarity with these more sophisticated measures and the now widespread availability of the analytical tools required to compute them, including option models and Monte Carlo analyses for securities with path-dependent options (such as mortgage-backed securities with embedded prepayment options). However, although these option-adjusted measures are more robust, they focus exclusively on a security’s or portfolio’s interest rate sensitivity. While an adverse change in interest rates is the dominant risk factor in this market, there are other sources of risk which can have a material impact on the value of fixed-income securities. Those securities that offer a premium above risk-free Treasury rates do so as compensation either for some type of credit risk (i.e. that the issuer will be downgraded or actually default), or ‘model risk’ (i.e. the risk that valuations may vary because future cash flows change in ways that models cannot predict). We have seen that gains from a favorable interest rate move can be more than offset by a change in credit spreads and revised prepayment estimates can significantly alter previous estimates of a mortgage portfolio’s interest rate sensitivity. The presence of these additional risks highlights the need for measures that explain and quantify a bond’s or portfolio’s sensitivity to changes in these variables. This chapter discusses two such measures: spread duration and prepayment uncertainty. We describe how these measures may be computed, provide some historical perspective on changes in these risk factors, and compare spread duration and prepayment uncertainty to interest rate risk measures for different security types. A risk manager can use these measures to evaluate the firm’s exposure to changes in credit spreads and uncertainty associated with prepayments in the mortgage-backed securities market. Since spread duration and prepayment uncertainty may be calculated both for individual securities and at the portfolio level, they may be used to establish limits with respect to both individual positions and the firm’s overall

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exposure to these important sources of risk. Both measures are summarized below: Ω Spread Duration – A measure of a bond’s (or portfolio’s) credit spread risk, i.e. its sensitivity to a change in the premium over risk-free Treasury rates demanded by investors in a particular segment of the market, where the premium is expressed in terms of an option-adjusted spread (OAS). The impact of a change in spreads is an important source of risk for all dollar-denominated fixed-income securities other than US Treasuries, and will become increasingly important in European debt markets if the corporate bond market grows as anticipated as a result of EMU. To calculate spread duration, a bond’s OAS (as implied by its current price) is increased and decreased by a specified amount; two new prices are computed based on these new OASs, holding the current term structure of interest rates and volatilities constant. The bond’s spread duration is the average percentage change in its price, relative to its current price, given the higher and lower OASs (scaled to a 100 bp shift in OAS). Spread duration allows risk managers to quantify and differentiate a portfolio’s sensitivity to changes in the risk premia demanded across market segments such as investment grade and high yield corporates, commercial mortgage-backed and asset-backed securities and so on. Ω Prepayment Uncertainty – A measure of the sensitivity of a security’s price to a change in the forecasted rate of future prepayments. This concept is primarily applicable to mortgage-backed securities,1 where homeowner prepayments due to refinancing incentives and other conditions are difficult to predict. To calculate this measure, alternative sets of future cash flows for a security are generated by adjusting the current prepayment forecast upward and downward by some percentage, e.g. 10% (for mortgage-backed securities, prepayment rates may be expressed using the ‘PSA’ convention, or as SMMs, single monthly mortality rates, or in terms of CPR, conditional/constant prepayment rates). Holding all other things constant (including the initial term structure of interest rates, volatility inputs and the security’s OAS), two new prices are computed using the slower and faster versions of the base case prepayment forecasts. The average percentage change in price resulting from the alternative prepayment forecasts versus the current price is the measure of prepayment uncertainty; the more variable a security’s cash flows under the alternative prepay speeds versus the current forecast, the greater its prepayment uncertainty and therefore its ‘model’ risk. An alternative approach to deriving a prepayment uncertainty measure is to evaluate the sensitivity of effective duration to a change in prepayment forecasts. This approach provides additional information to the risk manager and may be used in place of or to complement the ‘price sensitivity’ form of prepayment uncertainty. Either method helps to focus awareness on the fact that while mortgage-backed security valuations capture the impact of prepayment variations under different interest rate scenarios (typically via some type of Monte Carlo simulation), these valuations are subject to error because of the uncertainty of any prepayment forecast. We now discuss these risk measures in detail, beginning with spread duration.

Spread duration As summarized above, spread duration describes the sensitivity of a bond’s price to

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a change in its option-adjusted spread (OAS). For those who may be unfamiliar with the concept of option-adjusted spreads, we give a brief definition here. In a nutshell, OAS is the constant spread (in basis points) which, when layered onto the Treasury spot curve, equates the present value of a fixed-income security’s expected future cash flows adjusted to reflect the exercise of any embedded options (calls, prepayments, interest rate caps and so on) to its market price (see Figure 7.1).

Figure 7.1 Treasury curve and OAS.

To solve for a security’s OAS, we invoke the appropriate option model (e.g. a binomial or trinomial tree or finite difference algorithm for callable/puttable corporate bonds, or some type of Monte Carlo-simulation for mortgage-backed and other path-dependent securities) to generate expected future cash flows under interest rate uncertainty and iteratively search for the constant spread which, when layered onto the Treasury spot rates, causes the present value of those option-adjusted cash flows, discounted at the Treasury spot rates plus the OAS, to equal the market price of the security.

OAS versus nominal spread For bonds with embedded options (e.g. call options, prepayments, embedded rate caps, and so on), the difference between the option-adjusted spread and nominal spread (the difference between the bond’s yield-to-maturity or yield-to-call and the yield on a specific Treasury) can be substantial. Compared to nominal spread, OAS is a superior measure of a security’s risk premium for a number of reasons: Ω OAS analysis incorporates the potential variation in the present value of a bond’s expected future cash flows due to option exercise or changes in prepayment speeds. Nominal spread is based on a single cash flow forecast and therefore cannot accommodate the impact of interest rate uncertainty on expected future cash flows. Ω OAS is measured relative to the entire spot curve, whereas nominal spread is measured relative to a single point on the Treasury curve. Even for option-free securities, this is a misleading indication of expected return relative to a portfolio of risk-free Treasuries offering the same cash flows, particularly in a steep yield curve environment. Ω Nominal spread is a comparison to a single average life-matched Treasury, but if a security’s average life is uncertain its nominal spread can change dramatically (especially if the yield curve is steeply sloped) if a small change in the Treasury curve causes the bond to ‘cross over’ and trade to its final maturity date instead

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of a call date, or vice versa. OAS is computed relative to the entire set of Treasury spot rates and uses expected cash flows which may fluctuate as interest rates change, thus taking into account the fact that a security’s average life can change when interest rates shift and a call option is exercised or prepayments speed up or slow down. Ω Nominal spread assumes all cash flows are discounted at a single yield, which ultimately implies that cash flows from different risk-free securities which are paid in the same period (e.g. Year 1 or Year 2) will be discounted at different rates, simply because the securities have different final maturities, and so on. Although OAS is clearly superior to nominal spread in determining relative value, there are a number of problems associated with using OAS. For example, OAS calculations can vary from one model to another due to differences in volatility parameters, prepayment forecasts, etc. Nonetheless, OAS is now a commonly accepted valuation tool, particularly when comparing the relative value of fixedincome securities across different markets.

Spread risk – a ‘real-world’ lesson In the Fall of 1998, fixed-income securities markets experienced unprecedented volatility in response to the liquidity crisis (real or perceived) and ‘flight to quality’ that resulted from the turmoil in Russian and Brazilian debt markets and from problems associated with the ‘meltdown’ of the Long Term Capital Management hedge fund. As Treasury prices rallied, sending yields to historic lows, spreads on corporate bonds, mortgage-backed securities and asset-backed securities all widened in the course of a few days by more than the sum of spread changes that would normally occur over a number of months or even years. Many ‘post-mortem’ analyses described the magnitude of the change as a ‘5 standard deviation move’, and one market participant noted that ‘spreads widened more than anyone’s risk models predicted and meeting margin calls sucked up liquidity’ (Bond Week, 1998). Spreads on commercial mortgage-backed securities widened to the point where liquidity disappeared completely and no price quotes could be obtained. While there are undoubtedly many lessons to be learned from this experience, certainly one is that while a firm’s interest rate risk may be adequately hedged, spread risk can overwhelm interest rate risk when markets are in turmoil.

Spread Duration/Spread Risk Since investors demand a risk premium to hold securities other than risk-free (i.e. free of credit risk, liquidity risk, prepayment model risk, etc.) debt, and that risk premium is not constant over time, spread duration is an important measure to include in the risk management process. Spreads can change in response to beliefs about the general health of the domestic economy, to forecasts about particular sectors (e.g. if interest rates rise, spreads in the finance sector may increase due to concerns about the profitability of the banking industry), to political events (particularly in emerging markets) that affect liquidity, and so on. Often, investors are just as concerned with the magnitude and direction of changes in spreads as with changes in interest rates, and spread duration allows the risk manager to quantify the impact of changes in option-adjusted sector spreads across a variety of fixed-income investment alternatives.

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Computing spread duration To calculate spread duration, we increase and decrease a security’s OAS by some amount and, holding Treasury (spot) rates and volatilities at current levels, compute two new prices based on these new spreads: POASñ100 bps ñPOASò100 bps î100 2îPBase case OAS Spread duration is the average percentage change in the security’s price given the lower and higher OASs. It allows us to quickly translate a basis point change in spreads to a percentage change in price, and by extension, a dollar value change in a position. For example, the impact of a 20 bp shift in OAS on the price of a bond with a spread duration of 4.37 is estimated by (0.20î4.37)ó0.874%. Therefore, a $50 million position in this security would decline by $43.7K ($50 millionî0.874) if spreads widened by 20 bps.

Spread changes – historical data Since the correlation between changes in credit spreads and changes in interest rates is unstable (the correlation even changes sign over time), it is important to measure a portfolio’s or an institution’s exposure to spread risk independent of an assessment of interest rate risk. For example, a portfolio of Treasuries with an effective duration of 5.0 has no spread risk, but does have interest rate risk. A portfolio of corporate bonds with an effective duration of 3.0 has less interest rate risk than the Treasury portfolio, but if adverse moves in interest rates and spreads occur simultaneously, the corporate portfolio may be a greater source of risk than the Treasury portfolio. Spread risk affects corporate bonds, mortgage-backed securities, asset-backed securities, municipal bonds and so on, and a change in spreads in one segment of the market may not carry over to other areas, as the fundamentals and technicals that affect each of these markets are typically unrelated. Nonetheless, we have seen that in times of extreme uncertainty, correlations across markets can converge rapidly to ò1.0, eliminating the benefits that might otherwise be gained by diversifying spread risk across different market sectors. What magnitude of spread changes can one reasonably expect over a given period? Table 7.1 shows the average and standard deviations of option-adjusted spreads for various sectors over the six-year period, August 1992 to July 1998. In parentheses, we show the standard deviations computed for a slightly different six-year period, November 1992 to October 1998 (note: statistics were computed from weekly observations). The reason the two standard deviations are so different is that the values in parentheses include October 1998 data and therefore reflect the spread volatility experienced during the market crisis discussed above. Of course, six years is a long time and statistics can change significantly depending upon the observations used to compute them, so we also show the average and standard deviation of optionadjusted spreads measured over a one-year period, August 1997 to July 1998, with the standard deviation computed over the one year period November 1997 to October 1998 shown in parentheses. When evaluating the importance of spread risk, it is important to stress-test a portfolio under scenarios that reflect possible market conditions. Although the October 1998 experience may certainly be viewed as a rare event, if we use the oneyear data set excluding October 1998 to forecast future spread changes (based on

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The Professional’s Handbook of Financial Risk Management Table 7.1 Average and standard deviations of option-adjusted spreads Option-Adjusted Spread (OAS) six years of data Sector/quality Industrial – AA Industrial – A Industrial – BAA Utility – AA Utility – A Utility – BAA Finance – AA Finance – A Finance – BAA Mortgage pass-throughs (30-yr FNMA)

8/92–7/98 Average 47 67 111 48 72 95 56 74 99 73 Average:

(11/92–10/98) Standard dev. 6.2 8.4 21.2 5.4 14.8 15.1 6.5 11.1 16.1

(8.8) (11.8) (22.7) (8.1) (14.4) (15.7) (11.6) (16.1) (19.7)

16.4 (17.1) 12.1 (14.6)

Option-Adjusted Spread (OAS) one year of data 8/97–7/98 Average 49 68 93 47 63 87 59 72 96 54 Average:

(11/97–10/98) Standard dev. 4.4 5.5 5.8 2.9 5.1 5.4 4.7 5.1 9.1

(13.8) (18.2) (26.9) (15.0) (15.2) (17.8) (21.3) (27.2) (28.9)

11.2 (25.2) 5.9 (20.9)

Note: OASs for corporate sectors are based on securities with an effective duration of approximately 5.0.

standard deviation), we could potentially underestimate a portfolio’s spread risk by more than threefold (the average standard deviation based on one year of data including October 1998 is 3.5 times the average standard deviation based on one year of data excluding October 1998). When stress-testing a portfolio, it would be a good idea to combine spread changes with interest rate shocks – for example, what would happen if interest rates rise by X bps while corporate spreads widen by Y bps and mortgage spreads widen by Z bps? If we have computed the portfolio’s effective duration and the spread duration of the corporate and mortgage components of the portfolio, we can easily estimate the impact of this scenario: [(Effective durationOverall îX ) ò(Spread durationCorporates îY )ò(Spread durationMortgages îZ )].

Spread risk versus interest rate risk How, if at all, does spread duration relate to the more familiar effective duration value that describes a bond’s or portfolio’s sensitivity to changes in interest rates? In this section, we attempt to provide some intuition for how spread risk compares to interest rate risk for different types of securities. In attempting to understand spread duration, it is necessary to think about how a change in spreads affects both the present value of a security’s future cash flows and the amount and timing of the cash flows themselves. In this respect, an interesting contrast between corporate bonds and mortgage-backed securities may be observed when analyzing spread duration. Corporate bonds A change in the OAS of a callable (or puttable) corporate bond directly affects the cash flows an investor expects to receive, since the corporate issuer (who is long the call option) will decide whether or not to call the bond on the basis of its price in the secondary market. If a security’s OAS narrows sufficiently, its market price will rise

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above its call price, causing the issuer to exercise the call option. Likewise, the investor who holds a puttable bond will choose to exercise the put option if secondary market spreads widen sufficiently to cause the bond’s price to drop below the put price (typically par). Since changing the bond’s OAS by X basis points has the same impact on its price as shifting the underlying Treasury yields by an equal number of basis points, the spread duration for a fixed rate corporate bond is actually equal to its effective duration.2 Therefore, either spread duration or effective duration can be used to estimate the impact of a change in OAS on a corporate bond’s price. The same applies to a portfolio of corporate bonds, so if a risk management system captures the effective (option-adjusted) duration of a corporate bond inventory, there is no need to separately compute the spread duration of these holdings. (Note that Macaulay’s, a.k.a. ‘Modified’, duration is not an acceptable proxy for the spread risk of corporate bonds with embedded options, for the same reasons it fails to adequately describe the interest rate sensitivity of these securities.) Floating rate securities Although we can see that for fixed rate corporate bonds, spread duration and effective duration are the same, for floating rate notes (FRNs), this is not the case. The effective duration of an (uncapped) FRN is roughly equal to the amount of time to its next reset date. For example, an FRN with a monthly reset would have an effective duration of approximately 0.08, indicating the security has very little interest rate sensitivity. However, since a change in secondary spreads does not cause the FRN’s coupon rate to change; a FRN can have substantial spread risk. This is due to the impact of a change in secondary spreads on the value of the remaining coupon payments – if spreads widen, the FRN’s coupon will be below the level now demanded by investors, so the present value of the remaining coupon payments will decline. The greater the time to maturity, the longer the series of below-market coupon payments paid to the investor and the greater the decline in the value of the FRN. Therefore, the spread duration of an FRN is related to its time to maturity; e.g. an FRN maturing in two years has a lower spread duration than an FRN maturing in ten years. Mortgage passthroughs The spread duration of a mortgage-backed security is less predictable than for a corporate bond and is not necessarily related to its effective duration. We observed that for corporate bonds, a change in secondary market spreads affects the cash flows to the bondholder because of the effect on the exercise of a call or put option. Can we make the same claim for mortgage-backed securities? In other words, can we predict whether or not a change in secondary market mortgage spreads will affect a homeowner’s prepayment behavior, thereby altering the expected future cash flows to the holder of a mortgage-backed security? What implications does this have for the spread risk involved in holding these securities? Let us consider two separate possibilities, i.e. that homeowners’ prepayment decisions are not affected by changes in secondary spreads for MBS, and conversely, that spread changes do affect homeowners’ prepayments. If we assume that a homeowner’s incentive to refinance is not affected by changes in spreads, we would expect the spread duration of a mortgage passthrough to resemble its Macaulay’s duration, with good reason. Recall that Macaulay’s duration tells us the percentage

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change in a bond’s price given a change in yield, assuming no change in cash flows. Spread duration is calculated by discounting a security’s projected cash flows using a new OAS, which is roughly analogous to changing its yield. Therefore, if we assume that a change in spreads has no effect on a mortgage-backed security’s expected cash flows, its spread duration should be close to its Macaulay’s duration.3 However, it may be more appropriate to assume that a change in OAS affects not only the discount rate used to compute the present value of expected future cash flows from a mortgage pool, but also the refinancing incentive faced by homeowners, thereby changing the amount and timing of the cash flows themselves. As discussed in the next section on prepayment uncertainty, the refinancing incentive is a key factor in prepayment modeling that can have a significant affect on the valuation and assessment of risk of mortgage-backed securities. If we assume that changes in spreads do affect refinancings, the spread duration of a mortgage passthrough would be unrelated to its Macaulay’s duration, and unrelated to its effective (optionadjusted) duration as well. Adjustable rate mortgage pools (ARMs) are similar to FRNs in that changes in spreads, unlike interest rate shifts, do not affect the calculation of the ARM’s coupon rate and thus would not impact the likelihood of encountering any embedded reset or lifetime caps. Therefore, an ARM’s spread duration may bear little resemblance to its effective duration, which reflects the interest rate risk of the security that is largely due to the embedded rate caps. CMOs and other structured securities The spread duration of a CMO depends upon the deal structure and the tranche’s payment seniority within the deal. If we assume that a widening (narrowing) of spreads causes prepayments to decline (increase), a CMO with extension (contraction) risk could have substantial spread risk. Also, it is important to remember that as interest rates change, changes in CMO spreads may be different than changes collateral spreads. For example, when interest rates fall, spreads on well-protected PACs may tighten as spreads on ‘cuspy’ collateral widen, if investors trade out of the more prepayment-sensitive passthroughs into structured securities with less contraction risk. Therefore, when stress-testing a portfolio of mortgage-backed securities, it is important to include simulation scenarios that combine changes in interest rates with changes in spreads that differentiate by collateral type (premium versus discount) and by CMO tranche type (e.g. stable PACs and VADMs versus inverse floaters, IOs and so on). PSA-linked index-amortizing notes (IANs) have enjoyed some degree of popularity among some portfolio managers as a way to obtain MBS-like yields without actually increasing exposure to mortgage-backed securities. Since the principal amortization rate on these securities is linked to the prepayment speed on a reference pool of mortgage collateral, one might think that the spread duration of an IAN would be similar to the spread duration of the collateral pool. However, it is possible that spreads on these structured notes could widen for reasons that do not affect the market for mortgage-backed securities (such as increased regulatory scrutiny of the structured note market). Therefore, when stress-testing a portfolio that includes both mortgage-backed securities and IANs, it would be appropriate to simulate different changes in spreads across these asset types. To summarize, spread risk for corporate bonds is analogous to interest rate risk, as a change in OAS produces the same change in price as a change in interest rates.

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Spread duration for mortgage-backed securities reflects the fact that a change in OAS affects the present value of expected future cash flows, but whether or not the cash flows themselves are affected by the change in the OAS is a function of assumptions made by the prepayment model used in the analysis. The spread duration of a diversified portfolio measures the overall sensitivity to a change in OASs across all security types, giving the portfolio manager important information about a portfolio’s risk profile which no other risk measure provides.

Prepayment uncertainty Now we turn to the another important source of risk in fixed-income markets, prepayment uncertainty. Prepayment modeling is one of the most critical variables in the mortgage-backed securities (MBS) market, as a prepayment forecast determines a security’s value and its perceived ‘riskiness’ (where ‘risky’ is defined as having a large degree of interest rate sensitivity, described by a large effective duration and/or negative convexity). For those who may be unfamiliar with CMOs, certain types of these securities (such as principal-only tranches, and inverse floaters) can have effective durations that are two or three times greater than the duration of the underlying mortgage collateral, while interest-only (IO) tranches typically have negative durations, and many CMOs have substantial negative convexity. Changes in prepayment expectations can have a considerable impact on the value of mortgagebacked and asset-backed securities and therefore represents an important source of risk. Let’s briefly review how mortgage prepayment modeling affects the valuation of mortgage-backed securities. To determine the expected future cash flows of a security, a prepayment model must predict the impact of a change in interest rates on a homeowner’s incentive to prepay (refinance) a mortgage; as rates decline, prepayments typically increase, and vice versa. Prepayment models take into account the age or ‘seasoning’ of the collateral, as a homeowner whose mortgage is relatively new is less likely to refinance in the near-term than a homeowner who has not recently refinanced. Prepayment models also typically incorporate ‘burnout’, a term that reflects the fact that mortgage pools will contain a certain percentage of homeowners who, despite a number of opportunities over the years, simply cannot or will not refinance their mortgages. Many models also reflect the fact that prepayments tend to peak in the summer months (a phenomenon referred to as ‘seasonality’), as homeowners will often postpone moving until the school year is over to ease their children’s transition to a new neighborhood. Prepayment models attempt to predict the impact of these and other factors on the level of prepayments received from a given pool of mortgages over the life of the collateral. Earlier, we alluded to the fact that mortgage valuation is a path-dependent problem. This is because the path that interest rates follow will determine the extent to which a given collateral pool is ‘burned out’ when a new refinancing opportunity arises. For example, consider a mortgage pool consisting of fairly new 7.00% mortgages, and two interest rate paths generated by a Monte Carlo simulation. For simplicity, we make the following assumptions: Ω Treasury rates are at 5.25% across the term structure Ω Mortgage lending rates are set at 150 bps over Treasuries Ω Homeowners require at least a 75 bp incentive to refinance their mortgages.

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Given this scenario, homeowners with a 7.00% mortgage do not currently have sufficient incentive to trigger a wave of refinancings. Now, imagine that along the first interest rate path Treasury rates rise to 6.00% over the first two years, then decline to 4.75% at the end of year 3; therefore, mortgage lending rates at the end of year 3 are at 6.25%, presenting homeowners with sufficient incentive to refinance at a lower rate for the first time in three years. Along this path, we would expect a significant amount of prepayments at the end of year 3. On the second path, imagine that rates decline to 4.75% by the end of the first year and remain there. This gives homeowners an opportunity to refinance their 7.00% mortgages for two full years before a similar incentive exists on the first path. Consequently, by the end of year 3 we would expect that most homeowners who wish to refinance have already done so, and the cash flows forecasted for the end of year 3 would differ markedly compared to the first path, even though interest rates are the same on both paths at that point in time. Therefore, we cannot forecast the prepayments to be received at a given point in time simply by observing the current level of interest rate; we must know the path that rates followed prior to that point. In valuing mortgage-backed securities, this is addressed by using some type of Monte Carlo simulation to generate a sufficient number of different interest rate paths which provide the basis for a prepayment model to predict cash flows from the collateral pool under a variety of possible paths, based upon the history of interest rates experienced along each path.

Differences in prepayment models Prepayment speed forecasts in the mortgage-backed securities market often differ considerably across various reliable dealers. Table 7.2 shows the median prepayment estimates provided to the Bond Market Association by ten dealer firms for different types of mortgage collateral, along with the high and low estimates that contributed to the median. Note that in many cases, the highest dealer prepayment forecast is more than twice as fast as the lowest estimate for the same collateral type. To illustrate the degree to which differences in prepayment model forecasts can affect one’s estimate of a portfolio’s characteristics, we created a portfolio consisting Table 7.2 Conventional 30-year ﬁxed-rate mortgages as of 15 October 1998 Collateral type

Dealer prepay (PSA%) forecast

High versus low differences

Coupon

Issue year

Median

Low

High

Absolute

6.0 6.0 6.0 6.5 6.5 6.5 7.0 7.0 7.0 7.5 7.5

1998 1996 1993 1998 1996 1993 1998 1996 1993 1997 1993

170 176 173 226 234 215 314 334 300 472 391

152 150 137 175 176 151 213 235 219 344 311

286 256 243 365 320 326 726 585 483 907 721

134 156 106 190 144 175 513 350 264 563 410

PSA PSA PSA PSA PSA PSA PSA PSA PSA PSA PSA

Percent 188% 171% 177% 209% 182% 216% 341% 249% 221% 264% 232%

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of the different mortgage collateral shown in Table 7.2, with equal par amounts of eleven collateral pools with various coupons and maturities, using the ‘Low’ (slowest) PSA% and the ‘High’ (fastest) PSA% for each collateral type. Using the ‘Low’ speed, the portfolio had an average life of 6.85 years, with a duration of 4.73; using the ‘High’ speeds, the same portfolio had an average life of 3.67 years and a duration of 2.69. Clearly, the uncertainty in prepayment modeling can have a large impact on one’s assessment of a portfolio’s risk profile. There are a number of reasons why no two prepayment models will produce the same forecast, even with the same information about the interest rate environment and the characteristics of the mortgage collateral of interest. For example, different firms’ prepayment models may be calibrated to different historical data sets – some use five or even ten years of data, others may use data from only the past few years; some models attach greater weight to more recent data, others attach equal weight to all time periods; the variables used to explain and forecast prepayment behavior differ across models, and so on. Therefore, differences in prepayment modeling across well-respected providers is to be expected.4 In addition to differences in the way models are calibrated and specified, there is some likelihood that the historical data used to fit the model no longer reflects current prepayment behavior. When new prepayment data indicates that current homeowner behavior is not adequately described by existing prepayment models, prepayment forecasts will change as dealers and other market participants revise their models in light of the new empirical evidence For example, in recent years mortgage lenders have become more aggressive in offering low-cost or no-cost refinancing. As a result, a smaller decline in interest rates is now sufficient to entice homeowners to refinance their mortgages compared to five years ago (the required ‘refinance incentive’ has changed). Further developments in the marketplace (e.g. the ability to easily compare lending rates and refinance a mortgage over the Internet) will undoubtedly affect future prepayment patterns in ways that the historical data used to fit today’s prepayment models does not reflect. As mentioned previously, in the Fall of 1998 a combination of events wreaked havoc in fixed-income markets. Traditional liquidity sources dried up in the MBS market, which forced a number of private mortgage lenders to file for bankruptcy over the course of a few days. At the same time, Treasury prices rose markedly as investors sought the safe haven of US Treasuries in the wake of the uncertainties in other markets. As a rule, when Treasury yields decline mortgage prepayments are expected to increase, because mortgage lenders are expected to reduce borrowing rates in response to the lower interest rate environment. This time, however, mortgage lenders actually raised their rates, because the significant widening of spreads in the secondary market meant that loans originated at more typical (narrower) spreads over Treasury rates were no longer worth as much in the secondary market, and many lenders rely on loan sales to the secondary market as their primary source of funds. (Note that this episode is directly relevant to the earlier discussion of spread duration for mortgage-backed securities.) These conditions caused considerable uncertainty in prepayment forecasting. Long-standing assumptions about the impact of a change in Treasury rates on refinancing activity did not hold up but it was uncertain as to whether or not this would be a short-lived phenomenon. Therefore, it was unclear whether prepayment models should be revised to reflect the new environment or whether this was a short-term aberration that did not warrant a permanent change to key modeling

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parameters. During this time, the reported durations of well-known benchmark mortgage indices, such as the Lehman Mortgage Index and Salomon Mortgage Index, swung wildly (one benchmark index’s duration more than doubled over the course of a week), indicating extreme uncertainty in risk assessments among leading MBS dealers. In other words, there was little agreement as to prepayment expectations for, and therefore the value of, mortgage-backed securities. Therefore we must accept the fact that a prepayment model can only provide a forecast, or an ‘educated guess’ about actual future prepayments. We also know that the market consensus about expected future prepayments can change quickly, affecting the valuation and risk measures (such as duration and convexity) that are being used to manage a portfolio of these securities. Therefore, the effect of revised prepayment expectations on the valuation of mortgage-backed securities constitutes an additional source of risk for firms that trade and/or invest in these assets. This risk, which we may call prepayment uncertainty risk, may be thought of as a ’model risk’ since it derives from the inherent uncertainty of all prepayment models. For an investment manager who is charged with managing a portfolio’s exposure to mortgages relative to a benchmark, or for a risk manager who must evaluate a firm’s interest rate risk including its exposure to mortgage-backed securities, this episode clearly illustrates the importance of understanding the sensitivity of a valuation or risk model’s output to a change in a key modeling assumption. We do this by computing a ‘prepayment uncertainty’ measure that tests the ‘stability’ of a model’s output given a change in prepayment forecasts. Deﬁning a measure of prepayment uncertainty While standard definitions for effective duration and convexity have gained universal acceptance as measures of interest rate risk,5 no standard set of prepayment uncertainty measures yet exists. Some proposed measures have been called ‘prepayment durations’ or ‘prepayment sensitivities’ (Sparks and Sung, 1995; Patruno, 1994). Here, we describe three measures that are readily understood and capture the major dimensions of prepayment uncertainty. These measures are labeled overall prepayment uncertainty, refinancing (‘refi’) partial prepayment uncertainty, and relocation (‘relo’) partial payment uncertainty. To derive an overall prepayment uncertainty measure, the ‘base case’ prepayment speeds predicted by a model are decreased by 10%, then increased by 10%, and two new prices are derived under the slower and faster versions of the model (holding the term structure of interest rates, volatilities and security’s option-adjusted spread constant): 6 PSMMñ10% ñPSMMò10% î100 2îPBase case SMM where Póprice and SMMósingle monthly mortality rate (prepayment speed expressed as a series of monthly rates). Computed this way, securities backed by discount collateral tend to show a negative prepayment uncertainty. This makes intuitive sense, as a slowdown in prepayment speeds means the investor must wait longer to be repaid at par. Conversely, securities backed by premium collateral tend to show a positive prepayment uncertainty, because faster prepayments decrease the amount of future income expected from the high-coupon mortgage pool compared to the base case forecast.

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Note that for CMOs, a tranche may be priced at a premium to par even though the underlying collateral is at a discount, and vice versa. Therefore, one should not assume that the prepayment uncertainty of a CMO is positive or negative simply by noting whether the security is priced below or above par.

Reﬁnance and relocation uncertainty One of the most important variables in a prepayment model is the minimum level or ‘threshold’ incentive it assumes a homeowner requires to go to the trouble of refinancing a mortgage. The amount of the required incentive has certainly declined over the past decade; in the early days of prepayment modeling, it was not unusual to assume that new mortgage rates had to be at least 150 bps lower than a homeowner’s mortgage rate before refinancings would occur. Today, a prepayment model may assume that only a 75 bp incentive or less is necessary to trigger a wave of refinancings. Therefore, we may wish to examine the amount of risk associated with a misestimate in the minimum incentive the model assumes homeowners will require before refinancing their mortgages. To do so, we separate the total prepayment uncertainty measure into two components: refinancing uncertainty and relocation uncertainty. The ‘refi’ measure describes the sensitivity of a valuation to changes in the above-mentioned refinancing incentive, and the ‘relo’ measure shows the sensitivity to a change in the level of prepayments that are independent of the level of interest rates (i.e. due to demographic factors such as a change in job status or location, birth of children, divorce, retirement, and so on). Table 7.3 shows the overall and partial (‘refi’ and ‘relo’ prepayment uncertainties) for selected 30-year passthroughs. Table 7.3 Prepayment uncertainty – 30-year mortgage collateral as of August 1998 Prepayment uncertainty (%)

Collateral seasoning*

Price

Total

Reﬁ

Relo

Effective duration

6.50% 7.50% 8.50%

New New New

99.43 102.70 103.69

ñ0.042 0.169 0.300

0.036 0.155 0.245

ñ0.080 0.014 0.055

3.70 1.93 1.42

6.50% 7.50% 8.50%

Moderate Moderate Moderate

99.60 102.51 104.38

ñ0.024 0.191 0.341

0.036 0.161 0.257

ñ0.060 0.031 0.083

3.37 1.92 1.87

6.50% 7.50% 8.50%

Seasoned Seasoned Seasoned

99.66 102.58 104.48

ñ0.014 0.206 0.363

0.038 0.163 0.258

ñ0.050 0.042 0.105

3.31 1.94 1.83

Coupon

*Note: ‘Seasoning’ refers to the amount of time since the mortgages were originated; ‘new’ refers to loans originated within the past 24 months, ‘moderate’ applies to loans between 25 and 60 months old, fully ‘seasoned’ loans are more than 60 months old.

At first glance, these prepayment uncertainty values appear to be rather small. For example, we can see that a 10% increase or decrease in expected prepayments would produce a 0.191% change in the value of a moderately seasoned 7.50% mortgage pool. However, it is important to note that a 10% change in prepayment expectations is a rather modest ‘stress test’ to impose on a model. Recall the earlier discussion of

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differences in prepayment forecasts among Wall Street mortgage-backed securities dealers, where the high versus low estimates for various collateral types differed by more than 200%. Therefore, it is reasonable to multiply the prepayment uncertainty percentages derived from a 10% change in a model by a factor of 2 or 3, or even more. It is interesting that there appears to be a negative correlation between total prepayment uncertainty and effective duration; in other words, as prepayment uncertainty increases, interest rate sensitivity decreases. Why would this be so? Consider the ‘New 6.50%’ collateral with a slightly negative total prepay uncertainty measure (ñ0.042). This collateral is currently priced at a slight discount to par, so a slowdown in prepayments would cause the price to decline as the investor would receive a somewhat below-market coupon longer than originally expected. Both the ‘refi’ and ‘relo’ components of this collateral’s total uncertainty measure are relatively small, partly because these are new mortgages and we do not expect many homeowners who have just recently taken out a new mortgage to relocate or even to refinance in the near future, and partly because the collateral is priced slightly below but close to par. Since the collateral is priced below par, even a noticeable increase in the rate of response to a refinancing incentive would not have much impact on homeowners in this mortgage pool so the ‘refi’ component is negligible. Also, since the price of the collateral is so close to par it means the coupon rate on the security is roughly equal to the currently demanded market rate of interest. An increase or decrease in prepayments without any change in interest rates simply means the investor will earn an at-market interest rate for a shorter or longer period of time; the investor is be indifferent as to whether the principal is prepaid sooner or later under these circumstances as there are reinvestment opportunities at the same interest rate that is currently being earned. In contrast, the effective duration is relatively large at 3.70 precisely because the collateral is new and is priced close to par. Since the collateral is new, the remaining cash flows extend further into the future than for older (seasoned) collateral pools and a change in interest rates would have a large impact on the present value of those cash flows. Also, since the price is so close to par a small decline in interest rates could cause a substantial increase in prepayments as homeowners would have a new-found incentive to refinance (in other words, the prepayment option is close to at-the-money). This highlights a subtle but important difference between the impact of a change in refinance incentive due to a change in interest rates, which effective duration reflects, and the impact of a prepayment model misestimate of refinancing activity absent any change in interest rates. We should also note that since prepayment uncertainty is positive for some types of collateral and negative for others, it is possible to construct a portfolio with a prepayment uncertainty of close to zero by diversifying across collateral types.

Prepayment uncertainty – CMOs For certain CMO tranche types, such as IO (interest-only), PO (principal only), inverse floaters and various ‘support’ tranches, and mortgage strips, the prepayment uncertainty measures can attain much greater magnitude, both positive and negative, than for passthroughs. By the same token, well-protected PACs will exhibit a lesser degree of prepayment uncertainty than the underlying pass-through collateral. At

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times, the prepayment uncertainty values for CMOs may seem counterintuitive; in other words, tranches that one would expect to be highly vulnerable to a small change in prepayment forecasts have fairly low prepayment uncertainties, and vice versa. This surprising result is a good reminder of the complexity of these securities. Consider the examples in Table 7.4. Table 7.4 Prepayment uncertainties for CMOs Tranche Type Total prepay. uncertainty ‘Reﬁ’ uncertainty ‘Relo’ uncertainty Collateral prepay. uncertainty

IO

PO

PAC #1

PAC #2

7.638 5.601 2.037 0.190

ñ2.731 ñ2.202 ñ0.529 (re-remic)

0.315 0.220 0.095 0.115

0.049 0.039 0.010 0.177

Here we see an IO tranche with an overall prepayment uncertainty value of 7.638; in other words, the tranche’s value would increase (decrease) by more than 7.5% if prepayments were expected to be 10% slower (faster) than originally forecasted. This is not surprising, given the volatile nature of IO tranches. If prepayment forecasts are revised to be faster than originally expected, it means that the (notional) principal balance upon which the IO’s cash flows are based is expected to pay down more quickly, thus reducing the total interest payments to the IO holder. In contrast, the prepayment uncertainty of the collateral pool underlying the IO tranche is a modest 0.19 – a 10% change in expected prepayment speeds would produce only a small change in the value of the collateral. One would expect PO tranches to exhibit fairly large prepayment uncertainty measures as well, as POs are priced at a substantial discount to par (they are zero coupon instruments) and a change in prepayment forecasts means the tranche holder expects to recoup that discount either sooner or later than originally estimated. The total prepayment uncertainty for this particular PO is –2.731; note that the underlying collateral of this PO is a ‘re-remic’ – in other words, the collateral is a combination of CMO tranches from other deals, which may be backed by various types of collateral. In a re-remic, the underlying collateral may be a combination of highly seasoned, premium mortgages of various ‘vintages’ so it is virtually impossible to estimate the tranche’s sensitivity to prepayment model risk simply by noting that it is a PO. The exercise of computing a prepayment uncertainty measure for CMOs reminds us that these are complicated securities whose sensitivities to changing market conditions bears monitoring.

Measuring prepayment uncertainty – a different approach An alternative way of looking at prepayment uncertainty is to consider the effect of a change in prepayment speed estimates on the effective duration of a mortgage or portfolio of mortgage-backed securities. Since many firms hedge their mortgage positions by shorting Treasuries with similar durations, it is important to note that that the duration of a mortgage portfolio is uncertain and can be something of a moving target. These tables show the impact of a ô10% change in prepayment speeds on the average life, effective duration and convexity of different types of mortgage collateral. We can see that a small change in prepayment expectations could significantly

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Table 7.5 Average life, effective duration and convexity – base case and prepay speeds ô10% Base case Coupon*

Avg life

Dur.

PSA% ò10%

Conv PSA%

PSA% ñ10%

Avg life

Dur

Conv PSA%

Avg life

Dur

Conv PSA%

30 year collateral 6.50 3.8 3.9 6.50 3.0 3.6 6.50 2.7 3.3 7.00 3.0 2.8 7.00 2.3 2.7 7.00 2.2 2.5 7.50 3.3 3.0 7.50 2.5 2.1 7.50 2.3 2.1 8.00 2.3 1.9 8.00 2.4 2.0

ñ1.2 ñ1.0 ñ0.9 ñ1.1 ñ0.8 ñ0.7 ñ0.6 ñ0.2 ñ0.3 ñ0.1 ñ0.1

470 467 511 631 572 609 577 536 594 618 562

3.6 2.7 2.4 2.8 2.1 1.9 3.0 2.3 2.0 2.2 2.2

3.7 3.4 3.1 2.6 2.4 2.3 3.0 2.0 1.9 1.7 1.9

ñ1.2 ñ1.0 ñ1.0 ñ1.1 ñ0.8 ñ0.7 ñ0.7 ñ0.3 ñ0.3 ñ0.1 ñ0.1

517 514 562 694 629 669 635 590 663 680 618

4.2 3.3 3.0 3.3 2.7 2.4 3.6 2.9 2.6 2.7 2.8

4.1 3.8 3.6 3.1 3.0 2.8 3.4 2.7 2.5 2.2 2.4

ñ1.1 ñ1.0 ñ0.9 ñ1.0 ñ0.8 ñ0.7 ñ0.7 ñ0.4 ñ0.4 ñ0.2 ñ0.2

423 420 460 568 515 549 519 482 535 556 506

15 year collateral 6.50 3.3 2.3 6.50 2.6 2.0 7.00 2.0 1.4

ñ1.1 ñ0.9 ñ0.7

493 457 599

3.1 2.4 1.8

2.1 1.8 1.2

ñ1.1 ñ0.9 ñ0.6

542 503 659

3.5 2.8 2.3

2.42 2.18 1.66

ñ1.0 ñ0.8 ñ0.7

444 411 537

Change in average life, effective duration and convexity versus base case prepay estimates Avg absolute chg ô10% Coupon*

Avg life

Avg percent chg ô10%

Eff dur

Conv

Avg life

Eff dur

Conv

30 year collateral 6.50 0.29 6.50 0.33 6.50 0.29 7.00 0.25 7.00 0.29 7.00 0.25 7.50 0.29 7.50 0.29 7.50 0.29 8.00 0.25 8.00 0.29

0.22 0.23 0.21 0.22 0.28 0.25 0.23 0.33 0.32 0.29 0.29

0.02 0.02 0.01 0.01 ñ0.02 ñ0.02 ñ0.01 ñ0.07 ñ0.06 ñ0.08 ñ0.07

7.62 11.11 10.92 8.33 12.52 11.52 8.97 11.67 12.96 10.73 12.05

5.54 6.30 6.34 7.80 10.34 9.96 7.40 15.70 14.79 15.24 14.18

ñ2.03 ñ2.07 ñ1.48 ñ0.45 2.53 2.90 1.02 29.55 18.18 61.54 59.09

15 year collateral 6.50 0.21 6.50 0.21 7.00 0.21

0.16 0.18 0.21

0.02 0.01 ñ0.02

6.26 8.07 10.42

6.86 8.79 14.79

ñ1.32 ñ1.11 2.11

*Note: The same coupon rate may appear multiple times, representing New, Moderately Seasoned and Fully seasoned collateral.

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change the computed interest rate sensitivity of a portfolio of mortgage-backed securities. For example, a 10% change in our prepayment forecast for 30-year, 7.0% collateral changes our effective duration (i.e. our estimated interest rate risk) by an average of 9.37% (across new, moderately seasoned and fully seasoned pools with a coupon rate, net WAC, of 7.0%). Since a small change in prepayment speeds can cause us to revise our estimated duration by close to 10% (or more), this means that an assessment of an MBS portfolio’s interest rate risk is clearly uncertain. With these two approaches to measuring prepayment uncertainty, i.e. the change in price or change in effective duration given a change in prepayment forecasts, a risk manager can monitor the portfolio’s sensitivity to prepayment model risk in terms of both market value and/or the portfolio’s interest rate sensitivity. For example, the ‘change in market value’ form of prepayment uncertainty might be used to adjust the results of a VAR calculation, while the ‘change in effective duration’ version could be used to analyze a hedging strategy to understand how a hedge against interest rate risk for a position in MBS would have to be adjusted if prepayment expectations shifted. The prepayment uncertainty measures presented here can also assist with trading decisions on a single-security basis, as differences in prepayment uncertainty may explain why two securities with seemingly very similar characteristics trade at different OASs.

Summary Risk management for fixed-income securities has traditionally focused on interest rate risk, relying on effective duration and other measures to quantify a security’s or portfolio’s sensitivity to changes in interest rates. Spread duration and prepayment uncertainty are measures that extend that risk management and investment analysis beyond interest rate risk to examine other sources of risk which impact fixed-income markets. At the individual security level these concepts can assist with trading and investment decisions, helping to explain why two securities with seemingly similar characteristics have different OASs and offer different risk/return profiles. At the portfolio level, these measures allow a manager to quantify and manage exposure to these sources of risk, trading off one type of exposure for another, depending upon expectations and risk tolerances. Examining the effect of interest rate moves combined with spread changes and shifts in prepayment modeling parameters can provide a greater understanding of a firm’s potential exposure to the various risks in fixed-income markets.

Notes 1

The term ‘mortgage-backed security’ refers to both fixed and adjustable rate mortgage passthroughs as well as CMOs. 2 Assuming effective duration is calculated using the same basis point shift used to calculate spread duration. 3 The two durations still would not be exactly equal, as spread duration is derived from a Monte Carlo simulation that involves an average of the expected prepayments along a number of possible interest rate paths, whereas Macaulay’s duration is computed using only a single set of cash flows generated by a specified lifetime PSA% speed.

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4

For a discussion of how to assess the accuracy of a prepayment model, see Phoa and Nercessian. Evaluating a Fixed Rate Payment Model – White Paper available from Capital Management Sciences. 5 Although convexity may be expressed as either a ‘duration drift’ term or a ‘contribution to return’ measure (the former is approximately twice the latter), its definition is generally accepted by fixed-income practitioners. 6 Our methodology assumes that a Monte Carlo process is used to compute OASs for mortgagebacked securities.

References Bond Week (1998) XVIII, No. 42, 19 October. Patruno, G. N. (1994) ‘Mortgage prepayments: a new model for a new era’, Journal of Fixed Income, March, 7–11. Phoa, W. and Nercessian, T. Evaluating a Fixed Rate Prepayment Model, White Paper available from Capital Management Sciences, Los Angeles, CA. Sparks, A. and Sung, F. F. (1995) ‘Prepayment convexity and duration’, Journal of Fixed Income, December, 42–56.

8

Stress testing PHILIP BEST

Does VaR measure risk? If you think this is a rhetorical question then consider another: What is the purpose of risk management? Perhaps the most important answer to this question is to prevent an institution suffering unacceptable loss. ‘Unacceptable’ needs to be defined and quantified, the quantification must wait until later in this chapter. A simple definition, however, can be introduced straight away: An unacceptable loss is one which either causes an institution to fail or materially damages its competitive position. Armed with a key objective and definition we can now return to the question of whether VaR measures risk. The answer is, at best, inconclusive. Clearly if we limit the VaR of a trading operation then we will be constraining the size of positions that can be run. Unfortunately this is not enough. Limiting VaR does not mean that we have prevented an unacceptable loss. We have not even identified the scenarios, which might cause such a loss, nor have we quantified the exceptional loss. VaR normally represents potential losses that may occur fairly regularly – on average, one day in twenty for VaR with a 95% confidence level. The major benefit of VaR is the ability to apply it consistently across almost any trading activity. It is enormously useful to have a comparative measure of risk that can be applied consistently across different trading units. It allows the board to manage the risk and return of different businesses across the bank and to allocate capital accordingly. VaR, however, does not help a bank prevent unacceptable losses. Using the Titanic as an analogy, the captain does not care about the flotsam and jetsam that the ship will bump into on a fairly regular basis, but does care about avoiding icebergs. If VaR tells you about the size of the flotsam and jetsam, then it falls to stress testing to warn the chief executive of the damage that would be caused by hitting an iceberg. As all markets are vulnerable to extreme price moves (the fat tails in financial asset return distributions) stress testing is required in all markets. However, it is perhaps in the emerging markets where stress testing really comes into its own. Consideration of an old and then a more recent crisis will illustrate the importance of stress testing. Figure 8.1 shows the Mexican peso versus US dollar exchange rate during the crisis of 1995. The figure shows the classic characteristics of a sudden crisis, i.e. no prior warning from the behavior of the exchange rate. In addition there is very low volatility prior to the crisis, as a result VaR would indicate that positions in this currency represented very low risk.1 Emerging markets often show very low volatility

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Figure 8.1 Mexican peso versus USD.

during normal market conditions, the lack of volatility results, in part, from the low trading volumes in these markets – rather than the lack of risk. It is not uncommon to see the exchange rate unchanged from one trading day to the next. Figure 8.2 shows the VaR ‘envelope’ superimposed on daily exchange rate changes (percent). To give VaR the best chance of coping with the radical shift in behavior the exponentially weighted moving average (EWMA) volatility model has been used (with a decay factor of 0.94 – giving an effective observation period of approximately 30 days). As can be seen, a cluster of VaR exceptions that make a mockery of the VaR measured before the crisis heralds the start of crisis. The VaR envelope widens rapidly in response to the extreme exchange rate changes. But it is too late – being after the event! If management had been relying on VaR as a measure of the riskiness of positions in the Mexican peso they would have been sadly misled. The start of the crisis sees nine exchange rate changes of greater than 20 standard deviations,2 including one change of 122 standard deviations!

Figure 8.2 VaR versus price change: EWMA – decay factor: 0.94.

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Figure 8.3 USD/IDR exchange rate (January 1997 to August 1998).

Figure 8.3 shows the Indonesian rupiah during the 1997 tumult in the Asian economies. Note the very stable exchange rate until the shift into the classic exponential curve of a market in a developing crisis. Figure 8.4 shows the overlay of VaR on daily exchange rate changes. There are several points to note from Figure 8.4. First, note that the VaR envelope prior to the crisis indicated a very low volatility and, by implication, therefore a low risk. VaR reflects the recent history of exchange rate changes and does not take account of changes in the economic environment until such changes show up in the asset’s price behavior. Second, although the total number of exceptions is within reasonable statistical bounds (6.2% VaR excesses over the two years3 ), VaR does not say anything about how large the excesses will be. In the two years of history examined for the Indonesian rupiah there were 12 daily changes in the exchange rate of greater than twice the 95% confidence VaR – and one change of 19 standard deviations. Consideration of one-day price changes, however, is not enough. One-day shocks are probably less important than the changes that happen over a number of days.

Figure 8.4 IDR/USD–VaR versus rate change: EWMA – decay factor: 0.94.

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Examining Figures 8.3 and 8.4 shows that the largest one-day change in the Indonesian rupiah exchange rate was 18%, bad enough you might think, but this is only one third the 57% drop during January 1998. Against this, the 95% confidence VaR at the beginning of January was ranging between 9% and 11.5%. Most people would agree that stress testing is required to manage ‘outliers’ 4 it is perhaps slightly less widely understood that what you really need to manage are strong directional movements over a more extended period of time.

Stress testing – central to risk control By now it should be clear that VaR is inadequate as a measure of risk by itself. Risk management must provide a way of identifying and quantifying the effects of extreme price changes on a bank’s portfolio. A more appropriate risk measurement methodology for dealing with the effect of extreme price changes is a class of methods known as stress testing. The essential idea behind stress testing is to take a large price change or, more normally, a combination of price changes and apply them to a portfolio and quantify the potential profit or loss that would result. There are a number of ways of arriving at the price changes to be used, this chapter describes and discusses the main methods of generating price changes and undertaking stress testing: Ω Scenario analysis: Creation and use of potential future economic scenarios to measure their profit and loss impact on a portfolio Ω Historical simulation: The application of actual past events to the present portfolio. The past events used can be either a price shock that occurred on a single day, or over a more extended period of time. Ω Stressing VaR: The parameters, which drive VaR, are ‘shocked’, i.e. changed and the resultant change in the VaR number produced. Stressing VaR will involve changing volatilities and correlations, in various combinations. Ω Systematic stress testing: The creation of a comprehensive series of scenarios that stress all major risk factors within a portfolio, singly and in combination. As with the first two methods, the desired end result is the potential profit and loss impact on the portfolio. The difference with this method is the comprehensive nature of the stress tests used. The idea is to identify all major scenarios that could cause a significant loss, rather than to test the impact of a small number scenarios, as in the first two methods above. One of the primary objectives of risk management is to protect against bankruptcy. Risk management cannot guarantee bankruptcy will never happen (otherwise all banks would have triple A credit ratings) but it must identify the market events that would cause a severe financial embarrassment. Note that ‘event’ should be defined as an extreme price move that occurs over a period of time ranging from one day to 60 days. Once an event is identified the bank’s management can then compare the loss implied by the event against the available capital and the promised return from the business unit. The probability of an extreme event occurring and the subsequent assessment of whether the risk is acceptable in prevailing market conditions has until now been partly subjective and partly based on a simple inspection of historic return series. Now, however, a branch of statistics known as extreme value theory (EVT) holds out the possibility of deriving the probability of extreme events consistently across

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different asset classes. EVT has long been used in the insurance industry but is now being applied to the banking industry. An introduction to EVT can be found below. Given the low probabilities of extreme shocks the judgement as to whether the loss event identified is an acceptable risk will always be subjective. This is an important point, particularly as risk management has become based increasingly on statistical estimation. Good risk management is still and always will be based first and foremost on good risk managers, assisted by statistical analysis. Stress testing must be part of the bank’s daily risk management process, rather than an occasional investigation. To ensure full integration into the bank’s risk management process, stress test limits must be defined from the bank’s appetite for extreme loss. The use of stress testing and its integration into the bank’s risk management framework is discussed in the final part of this chapter.

Extreme value theory – an introduction Value at risk generally assumes that returns are normally or log-normally distributed and largely ignores the fat tails of financial return series. The assumption of normality works well enough when markets are themselves behaving normally. As already pointed out above, however, risk managers care far more about extreme events than about the 1.645 or 2.33 standard deviation price changes (95% or 99% confidence) given by standard VaR. If measuring VaR with 99% confidence it is clear that, on average, a portfolio value change will be experienced one day in every hundred that will exceed VaR. By how much, is the key question. Clearly, the bank must have enough capital available to cover extreme events – how much does it need? Extreme value theory (EVT) is a branch of statistics that deals with the analysis and interpretation of extreme events – i.e. fat tails. EVT has been used in engineering to help assess whether a particular construction will be able to withstand extremes (e.g. a hurricane hitting a bridge) and has also been used in the insurance industry to investigate the risk of extreme claims, i.e. their size and frequency. The idea of using EVT in finance and specifically risk management is a recent development which holds out the promise of a better understanding of extreme market events and how to ensure a bank can survive them.

EVT risk measures There are two key measures of risk that EVT helps quantify: Ω The magnitude of an ‘X’ year return. Assume that senior management in a bank had defined its extreme appetite for risk as the loss that could be suffered from an event that occurs only once in twenty years – i.e. a twenty-year return. EVT allows the size of the twenty-year return to be estimated, based on an analysis of past extreme returns. We can express the quantity of the X year return, RX , where: P(r[RX )ó1ñF(RX ) Or in words; the probability that a return will exceed RX can be drawn from the distribution function, F. Unfortunately F is not known and must be estimated by fitting a fat-tailed distribution function to the extreme values of the series. Typical

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distribution functions used in EVT are discussed below. For a twenty-year return P(r[RX ) is the probability that an event, r, occurring that is greater than RX will only happen, on average, once in twenty years. Ω The excess loss given VaR. This is an estimate of the size of loss that may be suffered given that the return exceeds VaR. As with VaR this measure comes with a confidence interval – which can be very wide, depending on the distribution of the extreme values. The excess loss given VaR is sometimes called ‘Beyond VaR’, B-VaR, and can be expressed as: BñVaRóESrñVaR Dr[VaRT In words; Beyond VaR is the expected loss (mean loss) over and above VaR given (i.e. conditional on the fact) that VaR has been exceeded. Again a distribution function of the excess losses is required.

EVT distribution functions EVT uses a particular class of distributions to model fat tails:

F(X)óexp ñ 1òm

xñk t

ñ1/m

ò

where m, k and t are parameters which define the distribution, k is the location parameter (analogous to the mean), t is the scale parameter and m, the most important, is the shape parameter. The shape parameter defines the specific distribution to be used. mó0 is called the Gumbel distribution, m\0 is known as the Weibull and finally and most importantly for finance, m[0 is referred to as the Frechet distribution. Most applications of EVT to finance use the Fre´ chet distribution. From Figure 8.5 the fat-tailed behavior of the Fre´ chet distribution is clear. Also notice that the distribution has unbounded support to the right. For a more formal exposition of the theory of EVT see the appendix at the end of this chapter.

Figure 8.5 Fre´chet distribution.

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The use and limitations of EVT At present EVT is really only practically applicable to single assets. There is no easy to implement multivariate application available at the time of writing. Given the amount of academic effort going into this subject and some early indications of progress it is likely that a tractable multivariate solution will evolve in the near future. It is, of course, possible to model the whole portfolio as a single composite ‘asset’ but this approach would mean refitting the distribution every time the portfolio changed, i.e. daily. For single assets and indices EVT is a powerful tool. For significant exposures to single-asset classes the results of EVT analysis are well worth the effort. Alexander McNeil (1998) gives an example of the potential of EVT. McNeil cites the hypothetical case of a risk analyst fitting a Fre´ chet distribution to annual maxima of the S&P 500 index since 1960. The analyst uses the distribution to determine the 50-year return level. His analysis indicates that the confidence interval for the 50-year return lies between 4.9% and 24%. Wishing to give a conservative estimate the analyst reports the maximum potential loss to his boss as a 24% drop. His boss is sceptical. Of course the date of this hypothetical analysis is the day before the 1987 crash – on which date the S&P 500 dropped 20.4%. A powerful demonstration of how EVT can be used on single assets.

Scenario analysis When banks first started stress testing it was often referred to as Scenario Analysis. This seeks to investigate the effect, i.e. the change in value of a portfolio, of a particular event in the financial markets. Scenarios were typically taken from past, or potential future, economic or natural phenomena, such as a war in the Middle East. This may have a dramatic impact on many financial markets: Ω Oil price up 50%, which may cause Ω Drop in the US dollar of 20%, which in turn leads to Ω A rise in US interest rates of 1% These primary market changes would have significant knock-on effects to most of the world’s financial markets. Other political phenomena that could be investigated include the unexpected death of a head of state, a sudden collapse of a government or a crisis in the Euro exchange rate. In all cases it is the unexpected or sudden nature of the news that causes an extreme price move. Natural disasters can also cause extreme price moves, for example the Japanese earthquake in 1995. Financial markets very quickly take account of news and rumour. A failed harvest is unlikely to cause a sudden price shock, as there is likely to be plenty of prior warning, unless the final figures are much worse than the markets were expecting. However, a wellreported harvest failure could cause the financial markets to substantially revalue the commodity, thereby causing a sustained price increase. A strong directional trend in a market can have an equally devastating effect on portfolio value and should be included in scenario analyses.

Stress testing with historical simulation Another way of scenario testing is to recreate actual past events and investigate their impact on today’s portfolio. The historical simulation method of calculating VaR

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lends itself particularly well to this type of stress testing as historical simulation uses actual asset price histories for the calculation of VaR. Scenario testing with historical simulation simply involves identifying past days on which the price changes would have created a large change in the value of today’s portfolio. Note that a large change in the portfolio value is sought, rather than large price changes in individual assets. Taking a simple portfolio as an example: $3 million Sterling, $2 million gold and $1 million Rand, Figure 8.6 below shows 100 days of value changes for this portfolio.

Figure 8.6 Portfolio value change.

It is easy to see the day on which the worst loss would have occurred. The size of the loss is in itself an interesting result: $135000, compared to the VaR for the portfolio of $43000. Historical simulation allows easy identification of exactly what price changes caused this extreme loss (see Table 8.1). Table 8.1 Asset price change (%) Asset

% change

Sterling Gold Rand

ñ2.5 ñ5.6 ñ0.2

Total (weighted)

ñ2.3

This is useful information, a bank would be able to discuss these results in the context of its business strategy, or intended market positioning. It may also suggest that further analysis of large price changes in gold are indicated to see whether they have a higher correlation with large price changes in sterling and rand. Identifying the number of extreme price moves, for any given asset, is straightforward. Historical simulation enables you to go a step further, and identify which assets typically move together in times of market stress. Table 8.2 shows the price changes that caused the biggest ten losses in five years of price history for the example portfolio above. It can be seen from Table 8.2 that the two currencies in the portfolio seem to move together during market shocks. This is suggesting that in times of market stress the currencies have a higher correlation than they do usually. If this was shown to be the case with a larger number of significant portfolio value changes then it is extremely important information and should be used when constructing stress tests for a portfolio and the corresponding risk limits for the portfolio. In fact, for the example portfolio, when all changes in portfolio value of more than 1% were examined it was found that approximately 60% of them arose as a result of large price moves (greater than 0.5%) in both of the currencies. This is particularly

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Rand

Sterling

Change in portfolio value

ñ1.41 ñ0.17 ñ3.30 0.06 ñ0.29 ñ0.33 0.88 0.03 ñ0.60 ñ0.60

ñ13.31 ñ5.62 ñ1.77 ñ3.72 ñ0.71 ñ3.12 ñ0.79 ñ2.62 ñ2.14 1.44

ñ0.05 ñ2.53 ñ0.67 ñ1.77 ñ2.54 ñ1.66 ñ3.23 ñ1.98 ñ1.72 ñ2.68

ñ162 956 ñ135 453 ñ103 728 ñ89 260 ñ89 167 ñ87 448 ñ87 222 ñ85 157 ñ85 009 ñ77 970

interesting as the correlation between rand and sterling over the 5-year period examined is very close to zero. In times of market stress the correlation between sterling and rand increases significantly, to above 0.5.

Assessing the effect of a bear market Another form of scenario testing that can be performed with historical simulation is to measure the effect on a portfolio of an adverse run of price moves, no one price move of which would cause any concern by itself. The benefit of using historical simulation is that it enables a specific real life scenario to be tested against the current portfolio. Figure 8.7 shows how a particular period of time can be selected and used to perform a scenario test. The example portfolio value would have lost $517 000 over the two-week period shown, far greater than the largest daily move during the five years of history examined and 12 times greater than the calculated 95% VaR.

Figure 8.7 Portfolio value change.

A bank must be able to survive an extended period of losses as well as extreme market moves over one day. The potential loss over a more extended period is known as ‘maximum drawdown’ in the hedge-fund industry. Clearly it is easier to manage a period of losses than it is to manage a sudden one-day move, as there will be more opportunities to change the structure of the portfolio, or even to liquidate the portfolio. Although in theory it is possible to neutralize most positions in a relatively short period of time this is not always the case. Liquidity can become a real issue in times

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of market stress. In 1994 there was a sudden downturn in the bond markets. The first reversal was followed by a period of two weeks in which liquidity was much reduced. During that period, it was extremely difficult to liquidate Eurobond positions. These could be hedged with government bonds but that still left banks exposed to a widening of the spreads between government and Eurobonds. This example illustrates why examining the impact of a prolonged period of market stress is a worthwhile part of any stress-testing regime. One of the characteristics of financial markets is that economic shocks are nearly always accompanied by a liquidity crunch. There remains the interesting question of how to choose the extended period over which to examine a downturn in the market. To take an extreme; it would not make sense to examine a prolonged bear market over a period of several months or years. In such prolonged bear markets liquidity returns and positions can be traded out of in a relatively normal manner. Thus the question of the appropriate length of an extended period is strongly related to liquidity and the ability to liquidate a position – which in turn is dependent on the size of the position. Market crises, in which liquidity is severely reduced, generally do not extend for very long. In mature and deep markets the maximum period of severely restricted liquidity is unlikely to last beyond a month, though in emerging markets reduced liquidity may continue for some time. The Far Eastern and Russian crises did see liquidity severely reduced for periods of up to two months, after which bargain hunters returned and generated new liquidity – though at much lower prices. As a rule of thumb, it would not make sense to use extended periods of greater than one month in the developed markets and two months in the emerging markets. These guidelines assume the position to be liquidated is much greater than the normally traded market size. In summary, historical simulation is a very useful tool for investigating the impact of past events on today’s portfolio. Therein also lies its limitation. If a bank’s stress testing regime relied solely on historical simulation it would be assuming that past events will recur in the same way as before. This is extremely unlikely to be the case. In practice, as the dynamics of the world’s financial markets change, the impact of a shock in one part of the world or on one asset class will be accompanied by a new combination of other asset/country shocks – unlike anything seen before. The other limitation is, of course, that historical simulation can only give rise to a relatively small number of market shock scenarios. This is simply not a sufficiently rigorous way of undertaking stress testing. It is necessary to stress test shocks to all combinations of significant risk factors to which the bank is exposed.

Stressing VaR – covariance and Monte Carlo simulation methods The adoption of VaR as a new standard for measuring risk has given rise to a new class of scenario tests, which can be undertaken with any of the three main VaR calculation methods: covariance, historical simulation and Monte Carlo simulation (see Best, 1998). The use of historical simulation for scenario testing was discussed in the preceding section. With the covariance and Monte Carlo simulation methods the basic VaR inputs can be stressed to produce a new hypothetical VaR.

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Scenario testing using either covariance or Monte Carlo simulation is essentially the same, as volatilities and correlations are the key inputs for both the VaR methods. Before describing the stress testing of VaR it is worth considering what the results of such a stress test will mean. At the beginning of this chapter stress testing was defined as the quantification of potential significant portfolio losses as a result of changes in the prices of assets making up the portfolio. It was also suggested that stress tests should be used to ascertain whether the bank’s portfolio represents a level of risk that is within the bank’s appetite for risk. Scenario and stress testing have been implicitly defined so far as investigating the portfolio impact of a large change in market prices. Stress testing VaR is not an appropriate way to undertake such an investigation, as it is simpler to apply price changes directly to a portfolio. The stressing of volatility and correlation is asking how the bank’s operating, or dayto-day, level of risk would change if volatilities or correlations changed. This is a fundamentally different question than is answered by stress testing proper. Nonetheless it is a valid to ask whether the bank would be happy with the level of risk implied by different volatilities and correlations. Given that changes in volatilities and correlations are not instantaneous they do not pose the same threat to a trading institution as a market shock or adjustment.

Stressing volatility When stressing volatility in a VaR calculation it is important to be clear as to what is being changed with respect to the real world. Often, when volatility is stressed in a VaR calculation it is intended to imitate the effect of a market shock. If this is the intention, then it is better to apply the price move implied by the stressed volatility directly to the portfolio and measure its effect. Given that volatilities are calculated as some form of weighted average price change over a period of time it is clear that stressing volatility is an inappropriate way to simulate the effect of an extreme price movement. Therefore, we should conclude that the change in VaR given by stressing volatilities is answering the question ‘what would my day-to-day level of risk be if volatilities changes to X ?’

Stressing correlations Similar arguments apply to correlations as for volatilities. Stressing correlations is equivalent to undertaking a stress test directly on a portfolio. Consider a portfolio of Eurobonds hedged with government bonds. Under normal market conditions these two assets are highly correlated, with correlations typically lying between 0.8 and 0.95. A VaR stress test might involve stressing the correlation between these two asset classes, by setting the correlations to zero. A more direct way of undertaking this stress test is to change the price of one of the assets whilst holding the price of the second asset constant. Again, the question that must be asked is; what is intended by stressing correlations? Applying scenarios to VaR input parameters may give some useful insights and an interesting perspective on where the sources of risk are in a portfolio and equally, where the sources of diversification are in a portfolio. Nonetheless, stressing a VaR calculation, by altering volatilities and correlations, is not the most effective or efficient way of performing stress tests. Stressing volatilities and correlations in a VaR calculation will establish what the underlying risk in a portfolio would become if volatilities and correlations were to

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change to the levels input. Bank management can then be asked whether they would be happy with regular losses of the level given by the new VaR.

The problem with scenario analysis The three methods described above are all types of scenario analysis, i.e. they involve applying a particular scenario to a portfolio and quantifying its impact. The main problem with scenario testing, however it is performed, is that it only reveals a very small part of the whole picture of potential market disturbances. As shown below, in the discussion on systematic testing, there are an extremely large number of possible scenarios. Scenario analysis will only identify a tiny number of the scenarios that would cause significant losses. A second problem with scenario analysis is that the next crisis will be different. Market stress scenarios rarely if ever repeat themselves in the same way. For any individual asset over a period of time there will be several significant market shocks, which in terms of a one-day price move will look fairly similar to each other. What is unlikely to be the same is the way in which a price shock for one asset combines with price shocks for other assets. A quick examination of Table 8.2 above shows this to be true. ‘All right, so next time will be different. We will use our economists to predict the next economic shock for us.’ Wrong! Although this may be an interesting exercise it is unlikely to identify how price shocks will combine during the next shock. This is simply because the world’s financial system is extremely complex and trying to predict what will happen next is a bit like trying to predict the weather. The only model complex enough to guarantee a forecast is the weather system itself. An examination of fund management performance shows that human beings are not very good at predicting market trends, let alone sudden moves. Very few fund managers beat the stock indices on a consistent basis and if they do, then only by a small percentage. What is needed is a more thorough way of examining all price shock combinations.

Are simulation techniques appropriate? One way of generating a large number of market outcomes is to use simulation techniques, such as the Monte Carlo technique. Simulation techniques produce a random set of price outcomes based on the market characteristics assumed. Two points should be made here. One of the key market characteristics usually assumed is that price changes are normally distributed. The second point to note is that modelling extreme moves across a portfolio is not a practical proposition at present. Standard Monte Carlo simulation models will only produce as many extreme price moves as dictated by a normal distribution. For stress testing we are interested in price moves of greater than three standard deviations, as well as market moves covered by a normal distribution. Therefore, although simulation may appear to provide a good method of producing stress test events, in practice it is unlikely to be an efficient approach.

Systematic testing From the previous section, it should be clear that a more comprehensive method of stress testing is required. The methods so far discussed provide useful ways to

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investigate the impact of specific past or potential future scenarios. This provides valuable information but is simply not sufficient. There is no guarantee that all significant risk factors have been shocked, or that all meaningful combinations have been stressed. In fact it is necessary to impose systematically (deterministically) a large number of different combinations of asset price shocks on a portfolio to produce a series of different stress test outcomes. In this way scenarios that would cause the bank significant financial loss can be identified. Stress testing is often only thought about in the context of market risk, though it is clearly also just as applicable to credit risk. Stress tests for market and credit risk do differ as the nature and number of risk factors are very different in the two disciplines. Market risk stress testing involves a far greater number of risk factors than are present for credit risk. Having said this it is clear that credit risk exposure for traded products is driven by market risk factors. This relationship is discussed further in the section on credit risk stress testing.

Market risk stress tests As noted above, the dominant issue in stress testing for market risk is the number of risk factors involved and the very large number of different ways in which they can combine. Systematic stress testing for market risk should include the following elements: Ω Ω Ω Ω Ω

Non-linear price functions (gamma risk) Asymmetries Correlation breakdowns Stressing different combinations of asset classes together and separately appropriate size shocks

This section looks at ways of constructing stress tests that satisfy the elements listed above, subsequent sections look at how to determine the stress tests required and the size of the shocks to be used. Table 8.3 shows a matrix of different stress tests for an interest rate portfolio that includes options. The table is an example of a matrix of systematic stress tests. Table 8.3 Stress test matrix for an interest rate portfolio containing options – proﬁt and loss impact on the portfolio (£000s) Vol. multipliers î0.6 î0.8 î0.9 Null î1.1 î1.2 î1.4

Parallel interest rates shifts (%) ñ2

ñ1

ñ0.5

ñ0.1

Null

0.1

0.5

1

2

4

1145 1148 1150 1153 1157 1162 1174

435 447 456 466 478 490 522

34 78 100 122 144 167 222

ñ119 ñ60 ñ33 ñ8 18 46 113

ñ102 ñ48 ñ24 0 25 52 118

ñ102 ñ52 ñ29 ñ6 18 45 114

ñ188 ñ145 ñ125 ñ103 ñ77 ñ46 37

ñ220 ñ200 ñ189 ñ173 ñ151 ñ119 ñ25

37 ñ10 ñ12 2 32 80 223

ñ130 ñ165 ñ164 ñ151 ñ126 ñ90 21

The columns represent different parallel shifts in the yield curve and the rows represent different multiples of volatility. It should be noted that it is not normally sufficient to stress only parallel shifts to the yield curve. Where interest rate trading

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forms a substantial part of an operation it would be normal to also devise stress test matrices that shock the short and long end of the yield and volatility curves. The stress test matrix in Table 8.3 is dealing with gamma risk (i.e. a non-linear price function) by stressing the portfolio with a series of parallel shifts in the yield curve. The non-linear nature of option pricing means that systematic stress testing must include a number of different shifts at various intervals, rather than the single shift that would suffice for a portfolio of linear instruments (for example, equities). Also note that the matrix deals with the potential for an asymmetric loss profile by using both upward and downward shifts of both interest rates and implied volatility. The first thing to note is that the worst-case loss is not coincident with the largest price move applied. In fact had only extreme moves been used then the worst-case loss would have been missed altogether. In this example the worst-case loss occurs with small moves in rates. This is easier to see graphically, as in Figure 8.8.

Figure 8.8 Portfolio proﬁt and loss impact of combined stresses to interest rates and implied volatilities.

Stress test matrices are often presented graphically as well as numerically as the graphical representation facilitates an instant comprehension of the ‘shape’ of the portfolio – giving a much better feel for risks being run than can be obtained from a table of numbers. Table 8.3 shows a set of stress tests on a complete portfolio where price changes are applied to all products in the portfolio at the same time. This is only one of a number of sets of stress tests that should be used. In Table 8.3 there are a total of 69 individual scenarios. In theory, to get the total number of stress test combinations, the number of different price shocks must be applied to each asset in turn and to all combinations of assets. If the price shocks shown in Table 8.3 were applied to a portfolio of 10 assets (or risk factors), there would be a possible 70 587 6 stress tests. With a typical bank’s portfolio the number of possible stress tests would render

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comprehensive stress testing impractical. In practice a little thought can reduce the number of stress tests required. Table 8.3 is an example of a stress test matrix within a single asset class: interest rates. As a bank’s portfolio will normally contain significant exposures to several asset classes (interest rates, equities, currencies and commodities), it makes sense to devise stress tests that cover more than one asset class. Table 8.4 shows a stress test matrix that investigates different combinations of shocks to a bank’s equity and interest rate portfolios. Table 8.4 Cross-asset class stress test for equities and interest rates – proﬁt and loss impact on the portfolio (£000s) Index (% change) ñ50% ñ30% ñ10% 0 10% 30% 50%

Parallel interest rate shift (basis points) ñ200

ñ100

ñ50

0

50

100

200

50 250 450 550 650 850 1050

ñ250 ñ50 150 250 350 550 750

ñ370 ñ170 30 130 230 430 630

ñ500 ñ300 ñ100 0 100 300 500

ñ625 ñ425 ñ225 ñ125 ñ25 175 375

ñ700 ñ500 ñ300 ñ200 ñ100 100 300

ñ890 ñ690 ñ490 ñ390 ñ290 ñ90 110

As in the previous example, the columns represent different parallel shifts in the yield curve; the rows represent different shocks to an equity index. It should be noted that this stress test could be applied to a single market, i.e. one yield curve and the corresponding index, to a group of markets, or to the whole portfolio, with all yield curves and indices being shocked together. Again, it can be helpful to view the results graphically. Figure 8.9 instantly proves its worth, as it shows that the portfolio is behaving in a linear fashion, i.e. that there is no significant optionality present in the portfolio. In normal circumstances equity and interest rate markets are negatively correlated, i.e. if interest rates rise then equity markets often fall. One of the important things a stress test matrix allows a risk manager to do is to investigate the impact of changing the correlation assumptions that prevail in normal markets. The ‘normal’ market assumption of an inverse correlation between equities and interest rates is in effect given in the bottom right-hand quarter of Table 8.4. In a severe market shock one might expect equity and interest rate markets to crash together i.e. to be highly correlated. This scenario can be investigated in the upper right-hand quarter of the stress test matrix.

Credit risk stress testing There is, of course, no reason why stress testing should be constrained to market risk factors. In fact, it makes sense to stress all risk factors to which the bank is exposed and credit risk is, in many cases, the largest risk factor. It would seem natural having identified a potentially damaging market risk scenario to want to know what impact that same scenario would have on the bank’s credit exposure. Table 8.5 shows a series of market scenarios applied to trading credit risk exposures.

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Figure 8.9 Portfolio proﬁt and loss impact of combined stresses to interest rates and an equity index.

Table 8.5 A combined market and credit risk stress test (£000s) Market risk scenario Credit scenarios Portfolio portfolio value Portfolio unrealized proﬁt and loss Counterparty A exposure Counterparty B exposure Collateral value Loss given default; collateral agreement works Loss given default; collateral agreement failure Total portfolio loss – default of counterparty B

Now 10 000 0 5 000 5 000 5 000 0 ñ5 000 ñ5 000

A 9 000 ñ1 000 1 740

B 8 000 ñ2 000 ñ720

C 6 000 ñ4 000 ñ4 320

D 4 000 ñ6 000 ñ7 040

7 260 8 720 10 320 11 040 3 750 2 950 2 400 1 375 ñ3 510 ñ5 770 ñ7 920 ñ9 665 ñ7 260 ñ8 720 ñ10 320 ñ11 040 ñ8 260 ñ10 720 ñ14 320 ñ17 040

In general, a bank’s loan portfolio is considered to be relatively immune to market scenarios. This is not strictly true, as the value of a loan will change dependent on the level of the relevant yield curve; also, many commercial loans contain optionality (which is often ignored for valuation purposes). Recently there has been a lot of discussion about the application of traded product valuation techniques to loan books and the desirability of treating the loan and trading portfolio on the same basis for credit risk. This makes eminent sense and will become standard practice over the next few years. However, it is clear that the value of traded products, such as swaps, are far more sensitive to changes in market prices than loans. Table 8.5 shows a portfolio containing two counterparties (customers) the value of their exposure is equal at the present time (£5 million). Four different market risk scenarios have been applied to the portfolio to investigate the potential changes in the value of the counterparty exposure. The four scenarios applied to the portfolio

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can be taken to be a series of increasing parallel yield curve shifts. It is not really important, however, it may be the case that the counterparty portfolios contain mainly interest rate instruments, such as interest rate swaps, and that the counterparty portfolios are the opposite way round from each other (one is receiving fixed rates whilst the other is paying fixed rates). The thought process behind this series of stress could be that the systematic stress testing carried out on the market risk portfolio has enabled the managers to identify market risk scenarios that concern them. Perhaps they result from stressing exposure to a particular country. Risk managers then remember that there are two counterparties in that country that they are also concerned about from a purely credit perspective, i.e. they believe there is a reasonable probability of downgrade or default. They decide to run the same market risk scenarios against the counterparties and to investigate the resultant exposure and potential losses given default. Of course, multiple credit risk stress tests could be run with different counterparties going into default, either singly or in groups. Table 8.5 shows that exposure to counterparty A becomes increasingly negative as the scenarios progress. Taking scenario D as an example, the exposure to counterparty A would be ñ£7.04 million. In the case of default, the profit and loss for the total portfolio (unrealized plus realized profit and loss) would not change. Prior to default, the negative exposure would be part of the unrealized profit and loss on the portfolio. After default, the loss would become realized profit and loss.7 As the net effect on the portfolio value is zero, counterparties with negative exposure are normally treated as a zero credit risk. More worrying is the rapid increase in the positive value of exposure to counterparty B, this warrants further analysis. Table 8.5 gives further analysis of the credit risk stress test for counterparty B in the grey shaded area. The line after the exposure shows the value of collateral placed by counterparty B with the bank. At the current time, the exposure to counterparty B is fully covered by the value of collateral placed with the bank. It can be seen that this situation is very different, dependent on the size of the market shock experienced. In the case of scenario D the value of the collateral has dropped to £1.375 million – against an exposure of £11.04 million, i.e. the collateral would only cover 12.5% of the exposure! This may seem unrealistic but is actually based on market shocks that took place in the emerging markets in 1997 and 1998. The next part of the analysis is to assume default and calculate the loss that would be experienced. This is shown in the next two lines in Table 8.5. The first line assumes the collateral agreement is enforceable. The worst-case loss scenario is that the collateral agreement is found to be unenforceable. Again this may sound unrealistic but more than one banker has been heard to remark after the Asian crises that ‘collateral is a fair-weather friend’. The degree of legal certainty surrounding collateral agreements – particularly in emerging markets is not all that would be wished for. Note that the credit risk stress test does not involve the probability of default or the expected recovery rate. Both of these statistics are common in credit risk models but do not help in the quantification of loss in case of an actual default. The recovery rate says what you may expect to get back on average (i.e. over a large number of defaults with a similar creditor rating) and does not include the time delay. In case of default, the total loss is written off, less any enforceable collateral held, no account is taken of the expected recovery rate. After all, it may be some years before the counterparty’s assets are liquidated and distributed to creditors. The final piece of analysis to undertake is to examine the impact the default has

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on the total portfolio value. For a real portfolio this would need to be done by stripping out all the trades for the counterparties that are assumed to be in default and recalculating the portfolio values under each of the market scenarios under investigation. In the very simple portfolio described in Table 8.5, we can see what the impact would be. It has already been noted that the impact of counterparty A defaulting would be neutral for the portfolio value, as an unrealized loss would just be changed into a realized loss. In the case of counterparty B, it is not quite so obvious. Once a credit loss has occurred, the portfolio value is reduced by the amount of the loss (becoming a realised loss). The final line in Table 8.5 shows the total portfolio loss as a combination of the market scenario and the default of counterparty B. This is actually the result of a combined market and credit risk stress test.

Which stress test combinations do I need? The approach to determining which stress tests to perform is best described by considering a simple portfolio. Consider a portfolio of two assets, a 5-year Eurobond hedged with a 10-year government bond. The portfolio is hedged to be delta neutral, or in bond terminology, has a modified duration of zero. In other words the value of the portfolio will not change if the par yield curve moves up (or down) in parallel, by small amounts. If the stress test matrix shown in Table 8.3 were performed on the delta neutral bond portfolio the results would be close to zero (there would be some small losses shown due to the different convexity8 of the bonds). The value of this simple portfolio will behave in a linear manner and there is therefore no need to have multiple parallel shifts. However, even with the large number of shifts applied in Table 8.3, not all the risks present in the portfolio have been picked up. Being delta neutral does not mean the portfolio is risk free. In particular this portfolio is subject to spread risk; i.e. the risk that the prices of Euro and government bonds do not move in line with each other. It is speculated that this is the risk that sunk LTCM; it was purportedly betting on spreads narrowing and remaining highly correlated. The knock-on effect of the Asian crisis was that spreads widened dramatically in the US market. The other risk that the portfolio is subject to is curve risk; i.e. the risk that yield curve moves are not parallel. Stress tests must be designed that capture these risks. Curve risk can be investigated by applying curve tilts to the yield curve, rather than the parallel moves used in Table 8.3. Spread risk can be investigated by applying price shocks to one side of a pair of hedged asset classes. In this example a price shock could be applied to the Eurobonds only. This example serves to illustrate the approach for identifying the stress tests that need to be performed. A bank’s portfolio must be examined to identify the different types of risk that the portfolio is subject to. Stress tests should be designed that test the portfolio against price shocks for all the significant risks identified. There are typically fewer significant risks than there are assets in a portfolio and stress tests can be tailored to the products present in the portfolio. A portfolio without options does not need as many separate yield curve shocks as shown in Table 8.3, as the price function of such a portfolio will behave approximately linearly. From this example it can be seen that the actual number of stress tests that need to be performed, whilst still significant, is much smaller than the theoretical number of combinations. The stress tests illustrated by Table 8.3 in the previous section did not specify

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whether it is for a single market or group of markets. It is well known that the world’s interest rates are quite highly correlated and that a shock to one of the major markets, such as America, is likely to cause shocks in many of the world’s other markets. When designing a framework of systematic stress test such relationships must be taken into account. A framework of systematic stress test should include: Ω Stressing individual markets to which there is significant exposure Ω Stressing regional groups, or economic blocks; such as the Far East (it may also make sense to define a block whose economic fortunes are closely linked to Japan, The Euro block (with and without non-Euro countries) and a block of countries whose economies are linked to that of the USA9 Ω Stressing the whole portfolio together – i.e. the bank’s total exposure across all markets and locations stressing exposure in each trading location (regardless of the magnitude of the trading activity – Singapore was a minor operation for Barings). Stress tests in individual trading locations should mirror those done centrally but should be extended to separately stress any risk factors that are specific to the location.

Which price shocks should be used? The other basic question that needs to be answered is what magnitude of price shocks to use. A basic approach will entail undertaking research for each risk factor to be stressed to identify the largest ever move and also the largest move in the last ten years. Judgement must then be used to choose price shocks from the results of the research. The size of the price shocks used may be adjusted over time as asset return behavior changes. At the time of writing the world was in a period of high volatility. In such an environment it may make sense to increase the magnitude of price shocks. The price shocks used will not need to change often but should be reviewed once a year or as dictated by market behavior and changes in portfolio composition. A more sophisticated approach, for individual assets, would involve the use of extreme value theory (EVT). There are two approaches to designing stress tests with EVT: Ω Find the magnitude of price change that will be exceeded only, on average, once during a specified period of time. The period of time is a subjective decision and must be determined by the risk manager, typical periods to be considered may be 10, 20 or 50 years. If 20 years were chosen, the price change identified that would not be exceeded on average more than once in 20 years is called the ‘20-year return level’. Ω The second approach is merely the inverse of the first. Given that a bank will have identified its risk appetite (see below) in terms of the maximum loss it is prepared to suffer, then EVT can be used to determine the likelihood of such a loss. If the probability were considered to be too great (i.e. would occur more often than the bank can tolerate) then the risk appetite must be revisited.

Determining risk appetite and stress test limits The primary objective of stress testing is to identify the scenarios that would cause the bank a significant loss. The bank can then make a judgement as to whether it is happy with the level of risk represented by the current portfolios in the present market.

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Stress testing and capital In order to determine whether a potential loss identified by stress testing is acceptable, it must be related to the bank’s available capital. The available capital in this case will not be the ‘risk capital’, or shareholder-capital-at-risk allocated to the trading area (i.e. the capital used in risk-adjusted performance measures such as RAROC) but the bank’s actual shareholder capital. Shareholder-capital-at-risk is often based on VaR (as well as measures of risk for credit and operational risk). VaR will represent the amount of capital needed to cover losses due to market risk on a day-to-day basis. If, however, an extreme period of market stress is encountered then the bank may lose more than the allocated shareholder-capital-at-risk. This will also be the case if a downturn is experienced over a period of time. Clearly institutions must be able to survive such events, hence the need for the actual capital to be much larger than the shareholder-capital-atrisk typically calculated for RAROC. As with shareholder-capital-at-risk, the question of allocation arises. It is not possible to use stress testing for the allocation of actual shareholder capital until it is practical to measure the probability of extreme events consistently across markets and portfolios. Therefore it continues to make sense for shareholder-capital-at-risk to be used for capital allocation purposes.

Determining risk appetite A bank must limit the amount it is prepared to lose under extreme market circumstances; this can be done using stress test limits. As with all limit setting, the process should start with the identification of the bank’s risk appetite. If we start with the premise that a bank’s risk appetite is expressed as a monetary amount, let us say $10 million, then a natural question follows. Are you prepared to lose $10 million every day, once per month, or how often? The regularity with which a loss of a given magnitude can be tolerated is the key qualification of risk appetite. Figure 8.10 shows how a bank’s risk appetite could be defined. Several different losses are identified, along with the frequency with which each loss can be tolerated. The amount a bank is prepared to lose on a regular basis is defined as the 95%

Figure 8.10 Deﬁning risk appetite.

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confidence VaR (with a one-day holding period). The daily VaR limit should be set after consideration of the bank’s, or trading unit’s profit target. There would be little point in having a daily VaR limit that is larger than the annual profit target, otherwise there would be a reasonable probability that the budgeted annual profit would be lost during the year. The second figure is the most important in terms of controlling risk. An institution’s management will find it difficult to discuss the third figure – the extreme tolerance number. This is because it is unlikely that any of the senior management team have ever experienced a loss of that magnitude. To facilitate a meaningful discussion the extreme loss figure to be used to control risk must be of a magnitude that is believed possible, even if improbable. The second figure is the amount the bank is prepared to lose on an infrequent basis, perhaps once every two or three years. This amount should be set after consideration of the available or allocated capital. This magnitude of loss would arise from an extreme event in the financial markets and will therefore not be predicted by VaR. Either historic price changes or extreme value theory could be used to predict the magnitude. The bank’s actual capital must more than cover this figure. Stress tests should be used to identify scenarios that would give rise to losses of this magnitude or more. Once the scenarios have been identified, management’s subjective judgement must be used, in conjunction with statistical analysis, to judge the likelihood of such an event in prevailing market conditions. Those with vested interests should not be allowed to dominate this process. The judgement will be subjective, as the probabilities of extreme events are not meaningful over the short horizon associated with trading decisions. In other words the occurrence of extreme price shocks is so rare that a consideration of the probability of an extreme event would lead managers to ignore such events. EVT could be used in conjunction with management’s subjective judgement of the likelihood of such an event, given present or predicted economic circumstance. The loss labelled ‘extreme tolerance’ does not have an associated frequency, as it is the maximum the bank is prepared to lose – ever. This amount, depending on the degree of leverage, is likely to be between 10% and 20% of a bank’s equity capital and perhaps equates to a 1 in 50-year return level. Losses greater than this would severely impact the bank’s ability to operate effectively. Again stress tests should be used to identify scenarios and position sizes that would give rise to a loss of this magnitude. When possible (i.e. for single assets or indices) EVT should then be used to estimate the probability of such an event.

Stress test limits A bank must limit the amount it is prepared to lose due to extreme market moves, this is best achieved by stress test limits. As VaR only controls day-to-day risk, stress test limits are required in addition to VaR limits. Stress test limits are entirely separate from VaR limits and can be used in a variety of ways (see below). However they are used, it is essential to ensure that stress test limits are consistent with the bank’s VaR risk management limits, i.e. the stress test limits should not be out of proportion with the VaR limits. Stress test limits should be set at a magnitude that is consistent with the ‘occasional loss’ figure from Figure 8.10, above. However, significantly larger price shocks should also be tested to ensure that the ‘extreme tolerance number is not breached’.

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Stress test limits are especially useful for certain classes of products, particularly options. Traditional limits for options were based around the greeks; delta, gamma, vega, rho and theta. A single matrix of stress tests can replace the first three greeks. The advantage of stress tests over the greeks is that stress tests quantify the loss on a portfolio in a given market scenario. The greeks, particularly, gamma, can provide misleading figures. When options are at-the-money and close to expiry gamma can become almost infinitely large. This has nothing to do with potential losses and everything to do with the option pricing function (Black–Scholes). Figure 8.11 gives an example of stress limits for an interest rate option portfolio. This example of stress test limits could be used for the matrix of stress tests given above in Table 8.3.

Figure 8.11 Stress test limits for an interest option portfolio.

Figure 8.11 shows three stress test limits, which increase in magnitude with the size of the shift in interest rates and the change in volatility. Stress tests limits like this make it easy for trading management to see that losses due to specified ranges of market shifts are limited to a given figure. Using the greeks, the loss caused by specific market shifts is not specified (except for the tiny shifts used by the greeks). Stress test limits can be used as are standard VaR, or other, risk limits, i.e. when a stress test identifies that a portfolio could give rise to a loss greater than specified by the stress test limit, then exposure cannot be increased and must be decreased. This approach establishes stress test limits as ‘hard’ limits and therefore, along with standard risk limits, as absolute constraints on positions and exposures that can be created. Another approach is to set stress test limits but use them as ‘trigger points’ for discussion. Such limits need to be well within the bank’s absolute tolerance of loss. When a stress test indicates that the bank’s portfolio could give rise to a specified loss, the circumstances that would cause such a loss are distributed to senior management, along with details of the position or portfolio. An informed discussion can then take place as to whether the bank is happy to run with such a risk. Although EVT and statistics can help, the judgement will be largely subjective and will be based on the experience of the management making the decision.

Conclusion Due to the extreme price shocks experienced regularly in the world’s financial markets VaR is not an adequate measure of risk. Stress testing must be used to complement VaR. The primary objective of stress testing is to identify the scenarios

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that would cause a significant loss and to put a limit on risk exposures that would cause such losses. Stress testing must be undertaken in a systematic way. Ad-hoc scenario tests may produce interesting results but are unlikely to identify the worst-case loss a bank could suffer. Care must be taken to identify the stress tests required by examining the types of risk in the bank’s portfolio. Stress tests should be run daily as a bank’s portfolio can change significantly over a 24-hour period. A bank’s risk appetite should be set with reference to VaR and to the worst-case loss a bank is prepared to countenance under extreme market conditions. This is best done with reference to the frequency with which a certain loss can be tolerated. Stress test limits can then be established to ensure that the bank does not create positions that could give rise, in severe market circumstances, to a loss greater than the bank’s absolute tolerance of loss. Stress testing should be an integral part of a bank’s risk management framework and stress test limits should be used along side other risk limits, such as VaR limits.

Appendix: The theory of extreme value theory – an introduction © Con Keating In 1900 Bachelier introduced the normal distribution to financial analysis (see also Cootner, 1964); today most students of the subject would be able to offer a critique of the shortcomings of this most basic (but useful) model. Most would point immediately to the ‘fat tails’ evident in the distributions of many financial time series. Benoit Mandelbrot (1997), now better known for his work on fractals, and his doctoral student Eugene Fama published extensive studies of the empirical properties of the distributions of a wide range of financial series in the 1960s and 1970s which convincingly demonstrate this non-normality. Over the past twenty years, both academia and the finance profession have developed a variety of new techniques, such as the ARCH family, to simulate the observed oddities of actual series. The majority fall short of delivering an entirely satisfactory result. At first sight the presence of skewness or kurtosis in the distributions suggests that of a central limit theorem failing but, of course, the central limit theorem should only be expected to apply strongly to the central region, the kernel of the distribution. Now this presents problems for the risk manager who naturally is concerned with the more unusual (or extreme) behavior of markets, i.e. the probability and magnitudes of the events forming the tails of the distributions. There is also a common misunderstanding that the central limit theorem implies that any mixture of distributions or samplings from a distribution will result in a normal distribution, but a further condition exists, which often passes ignored, that these samplings should be independent. Extreme value theory (EVT) is precisely concerned with the analysis of tail behaviour. It has its roots in the work of Fisher and Tippett first published in 1928 and a long tradition of application in the fields of hydrology and insurance. EVT considers the asymptotic (limiting) behavior of series and, subject to the assumptions listed below, states lim P

nê

1 (Xn ñbn )Ox ó lim F n (an xòbn )óH(x) nê an

(A1)

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which implies that: F é MDA(Hm (x)) for some m

(A2)

which is read as: F is a realization in the maximum domain of attraction of H. To illustrate the concept of a maximum domain of attraction, consider a fairground game: tossing ping-pong balls into a collection of funnels. Once inside a funnel, the ball would descend to its tip – a point attractor. The domain of attraction is the region within a particular funnel and the maximum domain of attraction is any trajectory for a ping-pong ball which results in its coming to rest in a particular funnel. This concept of a stable, limiting, equilibrium organization to which dynamic systems are attracted is actually widespread in economics and financial analysis. The assumptions are that bn and an [0 (location and scaling parameters) exist such that the financial time series, X, demonstrates regular limiting behavior and that the distribution is not degenerate. These are mathematical technicalities necessary to ensure that we do not descend inadvertently into paradox and logical nonsenses. m is referred to as a shape parameter. There is (in the derivation of equation (A1)) an inherent assumption that the realisations of X, x0 , x1 , . . . , xn are independently and identically distributed. If this assumption were relaxed, the result, for serially dependent data, would be slower convergence to the asymptotic limit. The distribution Hm (x) is defined as the generalized extreme value distribution (GEV) and has the functional form: Hm(x)ó

exp(ñ(1òmx) ñ1/m )

exp(ñe ñx )

for mÖ0, mó0 respectively

(A3)

The distributions where the value of the tail index, m, is greater than zero, equal to zero or less than zero are known, correspondingly, as Fre´ chet, Gumbel and Weibull distributions. The Fre´ chet class includes Student’s T, Pareto and many other distributions occasionally used in financial analysis; all these distributions have heavy tails. The normal distribution is a particular instance of the Gumbel class where m is zero. This constitutes the theory underlying the application of EVT techniques but it should be noted that this exposition was limited to univariate data.10 Extensions of EVT to multivariate data are considerably more intricate involving measure theory, the theory of regular variations and more advanced probability theory. Though much of the multivariate theory does not yet exist, some methods based upon the use of copulas (bivariate distributions whose marginal distributions are uniform on the unit interval) seem promising. Before addressing questions of practical implementation, a major question needs to be considered. At what point (which quantile) should it be considered that the asymptotic arguments or the maximum domain of attraction applies? Many studies have used the 95th percentile as the point beyond which the tail is estimated. It is far from clear that the arguments do apply in this still broad range and as yet there are no simulation studies of the significance of the implicit approximation of this choice. The first decision when attempting an implementation is whether to use simply the ordered extreme values of the entire sample set, or to use maxima or minima in defined time periods (blocks) of, say, one month or one year. The decision trade-off is the number of data points available for the estimation and fitting of the curve

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parameters versus the nearness to the i.i.d. assumption. Block maxima or minima should be expected to approximate an i.i.d. series more closely than the peaks over a threshold of the whole series but at the cost of losing many data-points and enlarging parameter estimation uncertainty. This point is evident from examination of the following block maxima and peaks over threshold diagrams. Implementation based upon the whole data series, usually known as peaks over threshold (POT), uses the value of the realization (returns, in most financial applications) beyond some (arbitrarily chosen) level or threshold. Anyone involved in the insurance industry will recognize this as the liability profile of an unlimited excess of loss policy. Figures 8A.1 and 8A.2 illustrate these two approaches:

Figure 8A.1 25-Day block minima.

Figure 8A.2 Peaks over thresholds.

Figure 8A.1 shows the minimum values in each 25-day period and may be compared with the whole series data-set below. It should be noted that both series are highly autocorrelated and therefore convergence to the asymptotic limit should be expected to be slow. Figure 8A.2 shows the entire data-set and the peaks under an arbitrary value (1.5). In this instance, this value has clearly been chosen too close to the mean of the distribution – approximately one standard deviation. In a recent paper, Danielsson and De Vries (1997) develop a bootstrap method for the automatic choice of this cutoff point but as yet, there is inadequate knowledge of the performance of small sample estimators. Descriptive statistics of the two (EVT) series are given in Table 8A.1.

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604 ñ2.38 ñ0.00203 ñ0.87826 0.511257 ñ1.25063 ñ0.885 ñ0.42 ñ0.8293 ñ0.11603

Minima 108 ñ3.88 3.7675 ñ0.15054 1.954465 ñ1.6482 ñ0.47813 1.509511 ñ1.12775 0.052863

Notice that the data-set for estimation in the case of block minima has declined to just 108 observations and further that neither series possesses ‘fat tails’ (positive kurtosis). Figure 8A.3 presents these series.

Figure 8A.3 Ordered 25-day block minima and peaks over (1.5) threshold.

The process of implementing EVT is first to decide which approach, then the level of the tail boundary and only then to fit a parametric model of the GEV class to the processed data. This parametric model is used to generate values for particular VaR quantiles. It is standard practice to fit generalized Pareto distributions (GPD) to POT data: GPDm,b (x)ó

1ñ(1òmx/b) ñ1/m

for mÖ0

1ñexp(ñx/b)

for mó0

(A4)

where b[0 is a scaling parameter. There is actually a wide range of methods, which may be used to estimate the parameter values. For the peaks over threshold approach it can be shown that the tail estimator is:

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ˆ (x)ó1ñ nu 1òmˆ xñu F N bˆ

ñ1/mˆ

(A5)

where n is the number of observations in the tail, u is the value of the threshold observation and the ‘hatted’ parameters b, m are the estimated values. These latter are usually derived using maximum likelihood (MLE) from the log likelihood function, which can be shown to be: n

Ln (m, b)óñn ln(b)ò ; ln 1òm ió1

xñu b

ñ1/m

ñln 1òm

xñu b

(A6)

omitting the u and x subscripts. The numerical MLE solution should not prove problematic provided m[ñ 21 which should prove the case for most financial data. A quotation from R. L. Smith is appropriate: ‘The big advantage of maximum likelihood procedures is that they can be generalized, with very little change in the basic methodology, to much more complicated models in which trends or other effects may be present.’ Estimation of the parameters may also be achieved by either linear (see, for example, Kearns and Pagan, 1997) or non-linear regression after suitable algebraic manipulation of the distribution function. It should be immediately obvious that there is one potential significant danger for the risk manager in using EVT; that the estimates of the parameters introduce error non-linearly into the estimate of the VaR quantile. However, by using profile likelihood, it should be possible to produce confidence intervals for these estimates, even if the confidence interval is often unbounded. Perhaps the final point to make is that it becomes trivial to estimate the mean expected loss beyond VaR in this framework; that is, we can estimate the expected loss given a violation of the VaR limit – an event which can cause changes in management behavior and cost jobs. This brief appendix has attempted to give a broad overview of the subject. Of necessity, it has omitted some of the classical approaches such as Pickand’s and Hill’s estimators. An interested reader would find the introductory texts10 listed far more comprehensive. There has been much hyperbole surrounding extreme value theory and its application to financial time series. The reality is that more structure (ARCH, for example) needs to be introduced into the data-generating processes before it can be said that the method offers significant advantages over conventional methods. Applications, however, do seem most likely in the context of stress tests of portfolios.

Acknowledgements Certain sections of this chapter were drawn from Implementing Value at Risk, by Philip Best, John Wiley, 1998. John Wiley’s permission to reproduce these sections is kindly acknowledged. The author would also like to thank Con Keating for his invaluable assistance in reviewing this chapter and for writing the appendix on Extreme Value Theory. This chapter also benefited from the comments of Gurpreet Dehal and Patricia Ladkin.

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Notes 1

Note that observing other market parameters, such as the volatility of short-term interest rates, might have warned the risk manager that a currency devaluation was possible. Observed by a central risk management function in a different country, however, the chances of spotting the danger are much reduced. 2 That is, twenty times the return volatility prior to the crisis. 3 Z score of binomial distribution of exceptions: 1.072, i.e. the VaR model would not be rejected by a Type I error test. 4 Extreme price changes that have an almost infinitesimally small probability in a normal distribution but which we know occur with far greater regularity in financial markets. 5 For a more comprehensive coverage of EVT see Embrechs et al. (1997). 6 This is the number of ways of selecting n assets from a set of 10, all multiplied by the number of scenarios – 69 for this example. 7 Counterparty A’s liquidators would expect the bank to perform on the contracts, thus their value at the time of default would have to be written off. Once written off, of course, there is no potential for future beneficial market moves to improve the situation. 8 Bond price curvature – the slight non-linearity of bond prices for a given change in yield. 9 Note that this is not the same as the group of countries who have chosen to ‘peg’ their currencies to the US dollar. 10 For a more formal and complete introduction to EVT see Embrechs et al. (1997), Reiss and Thomas (1997) and Beirlant et al. (1996). Readers interested in either the rapidly developing multivariate theory or available software should contact the author.

References Bachelier, L. (1900) ‘Theorie de la speculation’, Annales Scientifique de l’Ecole Normale Superieur, 21–86, 111–17. Beirlant, J., Teugels, J. and Vynckier, P. (1996) Practical Analysis of Extreme Values, Leuven University Press. Best, P. (1998) Implementing Value at Risk, John Wiley. Cootner, E. (ed.) (1964) The Random Character of Stock Market Prices, MIT Press. Danielsson, J. and de Vries, C. (1997) Beyond the Sample: Extreme Quantile and Probability Estimation, Tinbergen Institute. Embrechs, P., Kluppelberg, C. and Mikosch, T. (1997) Modelling Extremal Events for Insurance and Finance, Springer-Verlag. Fisher, R. and Tippett, L. (1928) ‘Limiting forms of the frequency distribution of the largest and smallest member of a sample’, Proceedings of the Cambridge Philosophical Society, 24, 180–90. Kearns, P. and Pagan, A. (1997) ‘Estimating the tail density index for financial time series’, Review of Economics and Statistics, 79, 171–5. Mandelbrot, B. (1997) Fractals and Scaling in Finance: Discontinuity, Concentration, Risk, Springer-Verlag. McNeil, A. (1998) Risk, January, 96–100. Reiss, R. and Thomas, M. (1997) Statistical Analysis of Extreme Values, Birhausen.

9

Backtesting MARK DEANS The aim of backtesting is to test the effectiveness of market risk measurement by comparing market risk figures with the volatility of actual trading results. Banks must carry out backtesting if they are to meet the requirements laid down by the Basel Committee on Banking Supervision in the Amendment to the Capital Accord to incorporate market risks (1996a). If the results of the backtesting exercise are unsatisfactory, the local regulator may impose higher capital requirements on a bank. Further, when performed at a business line or trading desk level, backtesting is a useful tool to evaluate risk measurement methods.

Introduction Backtesting is a requirement for banks that want to use internal models to calculate their regulatory capital requirements for market risk. The process consists of comparing daily profit and loss (P&L) figures with corresponding market risk figures over a period of time. Depending on the confidence interval used for the market risk measurement, a certain proportion of the P&L figures are expected to show a loss greater than the market risk amount. The result of the backtest is the number of losses greater than their corresponding market risk figures: the ‘number of exceptions’. According to this number, the regulators will decide on the multiplier used for determining the regulatory capital requirement. Regulations require that backtesting is done at the whole bank level. Regulators may also require testing to be broken down by trading desk (Figure 9.1). When there is an exception, this breakdown allows the source of the loss to be analysed in more detail. For instance, the loss might come from one trading desk, or from the sum of losses across a number of different business areas. In addition to the regulatory requirements, backtesting is a useful tool for evaluating market risk measurement and aggregation methods within a bank. At the whole bank level, the comparison between risk and P&L gives only a broad overall picture of the effectiveness of the chosen risk measurement methods. Satisfactory backtesting results at the aggregate level could hide poor risk measurement methods at a lower level. For instance, risks may be overestimated for equity trading, but underestimated for fixed income trading. Coincidentally, the total risk measured could be approximately correct. Alternatively, risks could be underestimated for each broad risk category (interest rate, equity, FX, and commodity risk), but this fact could be hidden by a very conservative simple sum aggregation method. Backtesting at the portfolio level, rather than just for the whole bank, allows

Figure 9.1

Hierarchy of trading divisions and desks at a typical investment bank.

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individual market risk measurement models to be tested in practice. The lower the level at which backtesting is applied, the more information becomes available about the risk measurement methods used. This allows areas to be identified where market risk is not measured accurately enough, or where risks are being taken that are not detected by the risk measurement system. Backtesting is usually carried out within the risk management department of a bank where risk data is relatively easily obtained. However, P&L figures, often calculated by a business unit control or accounting department, are equally important for backtesting. The requirements of these departments when calculating P&L are different from those of the risk management department. The accounting principle of prudence means that it is important not to overstate the value of the portfolio, so where there is uncertainty about the value of positions, a conservative valuation will be taken. When backtesting, the volatility of the P&L is most important, so capturing daily changes in value of the portfolio is more important than having a conservative or prudent valuation. This difference in aims means that P&L as usually calculated for accounting purposes is often not ideal for backtesting. It may include unwanted contributions from provisions or intraday trading. Also, the bank’s breakdown of P&L by business line may not be the same as the breakdown used for risk management. To achieve effective backtesting, the risk and P&L data must be brought together in a single system. This system should be able to identify exceptions, and produce suitable reports. The data must be processed in a timely manner, as some regulators (e.g. the FSA) require an exception to be reported to them not more than one business day after it occurs. In the last few years, investment banks have been providing an increasing amount of information about their risk management activities in their annual reports. The final part of this chapter reviews the backtesting information given in the annual reports of some major banks.

Comparing risk measurements and P&L Holding period For regulatory purposes, the maximum loss over a 10-business-day period at the 99% confidence level must be calculated. This measurement assumes a static portfolio over the holding period. In a realistic trading environment, however, portfolios usually change significantly over 10 days, so a comparison of 10-day P&L with market risk would be of questionable value. A confidence level of 99% and a holding period of 10 days means that one exception would be expected in 1000 business days (about 4 years). If exceptions are so infrequent, a very long run of data has to be observed to obtain a statistically significant conclusion about the risk measurement model. Because of this, regulators require a holding period of one day to be used for backtesting. This gives an expected 2.5 events per year where actual loss exceeds the market risk figure. Figure 9.2 shows simulated backtesting results. Even with this number of expected events, the simple number of exceptions in one year has only limited power to distinguish between an accurate risk measurement model and an inaccurate one. As noted above, risk figures are often calculated for a holding period of 10 days. For backtesting, risks should ideally be recalculated using a 1-day holding period.

Backtesting graph.

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Figure 9.2

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For the most accurate possible calculation, this would use extreme moves of risk factors and correlations based on 1-day historical moves rather than 10-day moves. Then the risk figures would be recalculated. The simplest possible approach is simply to scale risk figures by the square root of 10. The effectiveness of a simple scaling approach depends on whether the values of the portfolios in question depend almost linearly on the underlying risk factors. For instance, portfolios of bonds or equities depend almost linearly on interest rates or equity prices respectively. If the portfolio has a significant non-linear component (significant gamma risk), the scaling would be inaccurate. For example, the value of a portfolio of equity index options would typically not depend linearly on the value of the underlying equity index. Also, if the underlying risk factors are strongly mean reverting (e.g. spreads between prices of two grades of crude oil, or natural gas prices), 10-day moves and 1-day moves would not be related by the square root of time. In practice, the simple scaling approach is often used. At the whole bank level, this is likely to be reasonably accurate, as typically the majority of the risk of a whole bank is not in options portfolios. Clearly, this would not be so for specialist businesses such as derivative product subsidiaries, or banks with extensive derivative portfolios.

Comparison process Risk reports are based on end-of-day positions. This means that the risk figures give the loss at the chosen confidence interval over the holding period for the portfolio that is held at the end of that business day. With a 1-day holding period, the risk figure should be compared with the P&L from the following business day. The P&L, if unwanted components are removed, gives the change in value from market movements of the portfolio the risk was measured for. Therefore, the risk figures and P&L figures used for comparison must be skewed by 1 business day for meaningful backtesting.

Proﬁt and loss calculation for backtesting When market risk is calculated, it gives the loss in value of a portfolio over a given holding period with a given confidence level. This calculation assumes that the composition of the portfolio does not change during the holding period. In practice, in a trading portfolio, new trades will be carried out. Fees will be paid and received, securities bought and sold at spreads below or above the mid-price, and provisions may be made against possible losses. This means that P&L figures may include several different contributions other than those related to market risk measurement. To compare P&L with market risk in a meaningful way, there are two possibilities. Actual P&L can be broken down so that (as near as possible) only contributions from holding a position from one day to the next remain. This is known as cleaning the P&L. Alternatively, the trading positions from one day can be revalued using prices from the following day. This produces synthetic or hypothetical P&L. Regulators recognize both these methods. If the P&L cleaning is effective, the clean figure should be almost the same as the synthetic figure. The components of typical P&L figures, and how to clean them, or calculate synthetic P&L are now discussed.

Dirty or raw P&L As noted above, P&L calculated daily by the business unit control or accounting department usually includes a number of separate contributions.

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Fees and commissions When a trade is carried out, a fee may be payable to a broker, or a spread may be paid relative to the mid-market price of the security or contract in question. Typically, in a market making operation, fees will be received, and spreads will result in a profit. For a proprietary trading desk, in contrast, fees would usually be paid, and spreads would be a cost. In some cases, fees and commissions are explicitly stated on trade tickets. This makes it possible to separate them from other sources of profit or loss. Spreads, however, are more difficult to deal with. If an instrument is bought at a spread over the mid-price, this is not generally obvious. The price paid and the time of the trade are recorded, but the current mid-price at the time of the trade is not usually available. The P&L from the spread would become part of intraday P&L, which would not impact clean P&L. To calculate the spread P&L separately, the midprice would have to be recorded with the trade, or it would have to be calculated afterwards from tick-by-tick security price data. Either option may be too onerous to be practical. Fluctuations in fee income relate to changes in the volume of trading, rather than to changes in market prices. Market risk measures give no information about risk from changes in fee income, therefore fees and commissions should be excluded from P&L figures used for backtesting. Provisions When a provision is taken, an amount is set aside to cover a possible future loss. For banking book positions that are not marked to market (e.g. loans), provisioning is a key part of the portfolio valuation process. Trading positions are marked to market, though, so it might seem that provisioning is not necessary. There are several situations, however, where provisions are made against possible losses. Ω The portfolio may be marked to market at mid-prices and rates. If the portfolio had to be sold, the bank would only receive the bid prices. A provision of the mid–bid spread may be taken to allow for this. Ω For illiquid instruments, market spreads may widen if an attempt is made to sell a large position. Liquidity provisions may be taken to cover this possibility. Ω High yield bonds pay a substantial spread over risk-free interest rates, reflecting the possibility that the issuer may default. A portfolio of a small number of such bonds will typically show steady profits from this spread with occasional large losses from defaults. Provisions may be taken to cover losses from such defaults. When an explicit provision is taken to cover one of these situations, it appears as a loss. For backtesting, such provisions should be removed from the P&L figures. Sometimes, provisions may be taken by marking the instrument to the bid price or rate, or to an even more conservative price or rate. The price of the instrument may not be marked to market daily. Price testing controls verify that the instrument is priced conservatively, and therefore, there may be no requirement to price the instrument except to make sure it is not overvalued. From an accounting point of view, there is no problem with this approach. However, for backtesting, it is difficult to separate out provisions taken in this way, and recover the mid-market value of the portfolio. Such implicit provisions smooth out fluctuations in portfolio value, and lead to sudden jumps in value when provisions are reevaluated. These jumps may lead to backtesting exceptions despite an accurate risk measurement method. This is illustrated in Figure 9.9 (on p. 281).

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Funding When a trading desk buys a security, it requires funding. Often, funding is provided by the bank’s treasury desk. In this case, it is usually not possible to match up funding positions to trading positions or even identify which funding positions belong to each trading desk. Sometimes, funding costs are not calculated daily, but a monthly average cost of funding is given. In this case, daily P&L is biased upwards if the trading desk overall requires funds, and this is corrected by a charge for funding at the month end. For backtesting, daily funding costs should be included with daily P&L figures. The monthly funding charge could be distributed retrospectively. However, this would not give an accurate picture of when funding was actually required. Also, it would lead to a delay in reporting backtesting exceptions that would be unacceptable to some regulators. Intraday trading Some trading areas (e.g. FX trading) make a high proportion of their profits and losses by trading during the day. Daily risk reports only report the risk from end of day positions being held to the following trading day. For these types of trading, daily risk reporting does not give an accurate picture of the risks of the business. Backtesting is based on daily risk figures and a 1-day holding period. It should use P&L with contributions from intra-day trading removed. The Appendix gives a detailed definition of intra- and interday P&L with some examples. It may be difficult to separate intraday P&L from the general P&L figures reported. For trading desks where intraday P&L is most important, however, it may be possible to calculate synthetic P&L relatively easily. Synthetic P&L is based on revaluing positions from the end of the previous day with the prices at the end of the current day (see below for a full discussion). Desks where intraday P&L is most important are FX trading and market-making desks. For these desks, there are often positions in a limited number of instruments that can be revalued relatively easily. In these cases, calculating synthetic P&L may be a more practical alternative than trying to calculate intraday P&L based on all trades during the day, and then subtracting it from the reported total P&L figure. Realized and unrealized P&L P&L is usually also separated into realized and unrealized P&L. In its current form, backtesting only compares changes in value of the portfolio with value at risk. For this comparison, the distinction between realized and unrealized P&L is not important. If backtesting were extended to compare cash-flow fluctuations with a cash-flow at risk measure, this distinction would be relevant.

Clean P&L Clean P&L for backtesting purposes is calculated by removing unwanted components from the dirty P&L and adding any missing elements. This is done to the greatest possible extent given the information available. Ideally, the clean P&L should not include: Ω Ω Ω Ω

Fees and commissions Profits or losses from bid–mid–offer spreads Provisions Income from intraday trading

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The clean P&L should include: Ω Interday P&L Ω Daily funding costs

Synthetic or hypothetical P&L Instead of cleaning the existing P&L figures, P&L can be calculated separately for backtesting purposes. Synthetic P&L is the P&L that would occur if the portfolio was held constant during a trading day. It is calculated by taking the positions from the close of one trading day (exactly the positions for which risk was calculated), and revaluing these using prices and rates at the close of the following trading day. Funding positions should be included. This gives a synthetic P&L figure that is directly comparable to the risk measurement. This could be written: Synthetic P&LóP0 (t1)ñP0 (t0 ) where P0 (t 0) is the value of the portfolio held at time 0 valued with the market prices as of time 0 P0 (t 1) is the value of the portfolio held at time 0 valued with the market prices as of time 1 The main problem with calculating synthetic P&L is valuing the portfolio with prices from the following day. Some instruments in the portfolio may have been sold, so to calculate synthetic P&L, market prices must be obtained not just for the instruments in the portfolio but for any that were in the portfolio at the end of the previous trading day. This can mean extra work for traders and business unit control or accounting staff. The definition of synthetic P&L is the same as that of interday P&L given in the Appendix.

Further P&L analysis for option books P&L analysis (or P&L attribution) breaks down P&L into components arising from different sources. The above breakdown removes unwanted components of P&L so that a clean P&L figure can be calculated for backtesting. Studying these other components can reveal useful information about the trading operation. For instance, on a market-making desk, does most of the income come from fees and commissions and spreads as expected, or is it from positions held from one day to the next? A change in the balance of P&L from different sources could be used to trigger a further investigation into the risks of a trading desk. The further breakdown of interday P&L is now considered. In many cases, the P&L analysis would be into the same factors as are used for measuring risk. For instance, P&L from a corporate bond portfolio could be broken down into contributions from treasury interest rates, movements in the general level of spreads, and the movements of specific spreads of individual bonds in the portfolio. An equity portfolio could have P&L broken down into one component from moves in the equity index, and another from movement of individual stock prices relative to the index. This type of breakdown allows components of P&L to be compared to general market risk and specific risk separately. More detailed backtesting can then be done to demonstrate the adequacy of specific risk measurement methods.

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P&L for options can be attributed to delta, gamma, vega, rho, theta, and residual terms. The option price will change from one day to the next, and according to the change in the price of the underlying and the volatility input to the model, this change can be broken down. The breakdown for a single option can be written as follows: *có

Lc 1 L2 c Lc Lc Lc *Sò (*S)2 ò *pò *rò *tòResidual LS 2 LS 2 Lp Lr Lt

This formula can also be applied to a portfolio of options on one underlying. For a more general option portfolio, the greeks relative to each underlying would be required. If most of the variation of the price of the portfolio is explained by the greeks, then a risk measurement approach based on sensitivities is likely to be effective. If the residual term is large, however, a full repricing approach would be more appropriate. The breakdown of P&L allows more detailed backtesting to validate risk measurement methods by risk factor, rather than just at an aggregate level. When it is possible to see what types of exposure lead to profits and losses, problems can be identified. For instance, an equity options desk may make profits on equity movements, but losses on interest rate movements.

Regulatory requirements The Basel Committee on Banking Supervision sets out its requirements for backtesting in the document Supervisory framework for the use of ‘backtesting’ in conjunction with the internal models approach to market risk capital requirements (1996b). The key points of the requirements can be summarized as follows: Ω Risk figures for backtesting are based on a 1-day holding period and a 99% confidence interval. Ω A 1-year observation period is used for counting the number of exceptions. Ω The number of exceptions is formally tested quarterly. The committee also urges banks to develop the ability to use synthetic P&L as well as dirty P&L for backtesting. The result of the backtesting exercise is a number of exceptions. This number is used to adjust the multiplier used for calculating the bank’s capital requirement for market risk. The multiplier is the factor by which the market risk measurement is multiplied to arrive at a capital requirement figure. The multiplier can have a minimum value of 3, but under unsatisfactory backtesting results can have a value up to 4. Note that the value of the multiplier set by a bank’s local regulator may also be increased for other reasons. Table 9.1 (Table 2 from Basel Committee on Banking Supervision (1996b)) provides guidelines for setting the multiplier. The numbers of exceptions are grouped into zones. A result in the green zone is taken to indicate that the backtesting result shows no problems in the risk measurement method. A result in the yellow zone is taken to show possible problems. The bank is asked to provide explanations for each exception, the multiplier will probably be increased, and risk measurement methods kept under review. A result in the red zone is taken to mean that there are severe problems with the bank’s risk measurement model or system. Under some circumstances, the local regulator may decide

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Number of exceptions

Increase in multiplier

Cumulative probability %

Green Green Green Green Green Yellow Yellow Yellow Yellow Yellow Red

0 1 2 3 4 5 6 7 8 9 10 or more

0.00 0.00 0.00 0.00 0.00 0.40 0.50 0.65 0.75 0.85 1.00

8.11 28.58 54.32 75.81 89.22 95.88 98.63 99.60 99.89 99.97 99.99

The cumulative probability column shows the probability of recording at least the number of exceptions shown if the risk measurement method is accurate, and assuming normally distributed P&L ﬁgures.

that there is an acceptable reason for an exception (e.g. a sudden increase in market volatilities). Some exceptions may then be disregarded, as they do not indicate problems with risk measurement. Local regulations are based on the international regulations given in Basel Committee on Banking Supervision (1996b) but may be more strict in some areas.

FSA regulations The Financial Services Authority (FSA) is the UK banking regulator. Its requirements for backtesting are given in section 10 of the document Use of Internal Models to Measure Market Risks (1998). The key points of these regulations that clarify or go beyond the requirements of the Basel Committee are now discussed. Ω When a bank is first seeking model recognition (i.e. approval to use its internal market risk measurement model to set its market risk capital requirement), it must supply 3 months of backtesting data. Ω When an exception occurs, the bank must notify its supervisor orally by close of business two working days after the loss is incurred. Ω The bank must supply a written explanation of exceptions monthly. Ω A result in the red zone may lead to an increase in the multiplication factor greater than 1, and may lead to withdrawal of model recognition. The FSA also explains in detail how exceptions may be allowed to be deemed ‘unrecorded’ when they do not result from deficiencies in the risk measurement model. The main cases when this may be allowed are: Ω Final P&L figures show that the exception did not actually occur. Ω A sudden increase in market volatility led to exceptions that nearly all models would fail to predict. Ω The exception resulted from a risk that is not captured within the model, but for which regulatory capital is already held.

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Other capabilities that the bank ‘should’ rather than ‘must’ have are the ability to analyse P&L (e.g. by option greeks), and to split down backtesting to the trading book level. The bank should also be able to do backtesting based on hypothetical P&L (although not necessarily on a daily basis), and should use clean P&L for its daily backtesting.

EBK regulations The Eidgeno¨ ssische Bankenkommission, the Swiss regulator, gives its requirements in the document Richtlinien zur Eigenmittelunterlegung von Marktrisiken (Regulations for Determining Market Risk Capital) (1997). The requirements are generally closely in line with the Basel Committee requirements. Reporting of exceptions is on a quarterly basis unless the number of exceptions is greater than 4, in which case, the regulator must be informed immediately. The bank is free to choose whether dirty, clean, or synthetic P&L are used for backtesting. The chosen P&L, however, must be free from components that systematically distort the backtesting results.

Backtesting to support speciﬁc risk measurement In September 1997, the Basel Committee on Banking Supervision released a modification (1997a,b) to the Amendment to the Capital Accord to include market risks (1996) to allow banks to use their internal models to measure specific risk for capital requirements calculation. This document specified additional backtesting requirements to validate specific risk models. The main points were: Ω Backtesting must be done at the portfolio level on portfolios containing significant specific risk. Ω Exceptions must be analysed. If the number of exceptions falls in the red zone for any portfolio, immediate action must be taken to correct the model. The bank must demonstrate that it is setting aside sufficient capital to cover extra risk not captured by the model. FSA and EBK regulations on the backtesting requirements to support specific risk measurement follow the Basel Committee paper very closely.

Beneﬁts of backtesting beyond regulatory compliance Displaying backtesting data Stating a number of exceptions over a given period gives limited insight into the reliability of risk and P&L figures. How big were the exceptions? Were they closely spaced in time, or separated by several weeks or months? A useful way of displaying backtesting data is the backtesting graph (see Figure 9.2). The two lines represent the 1-day 99% risk figure, while the columns show the P&L for each day. The P&L is shifted in time relative to the risk so that the risk figure for a particular day is compared with the P&L for the following trading day. Such a graph shows not only how many exceptions there were, but also their timing and magnitude. In addition, missing data, or unchanging data can be easily identified. Many banks show a histogram of P&L in their annual report. This does not directly compare P&L fluctuations with risk, but gives a good overall picture of how P&L was

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distributed over the year. Figure 9.3 shows a P&L histogram that corresponds to the backtesting graph in Figure 9.2.

Analysis of backtesting graphs Backtesting graphs prepared at a trading desk level as well as a whole bank level can make certain problems very clear. A series of examples shows how backtesting graphs can help check risk and P&L figures in practice, and reveal problems that may not be easily seen by looking at separate risk and P&L reports. Most of the examples below have been generated synthetically using normally distributed P&L, and varying risk figures. Figure 9.5 uses the S&P 500 index returns and risk of an index position. Risk measured is too low When there are many exceptions, the risk being measured is too low (Figures 9.4 and 9.5). Possible causes There may be risk factors that are not included when risk is measured. For instance, a government bond position hedged by a swap may be considered riskless, when there is actually swap spread risk. Some positions in foreign currency-denominated bonds may be assumed to be FX hedged. If this is not so, FX fluctuations are an extra source of risk. Especially if the problem only shows up on a recent part of the backtesting graph, the reason may be that volatilities have increased. Extreme moves used to calculate risk are estimates of the maximum moves at the 99% confidence level of the underlying market prices or rates. Regulatory requirements specify a long observation period for extreme move calculation (at least one year). This means that a sharp increase in volatility may not affect the size of extreme moves used for risk measurement much even if these are recalculated. A few weeks of high volatility may have a relatively small effect on extreme moves calculated from a two-year observation period. Figure 9.5 shows the risk and return on a position in the S&P 500 index. The risk figure is calculated using two years of historical data, and is updated quarterly. The period shown is October 1997 to October 1998. Volatility in the equity markets increased dramatically in September and October 1998. The right-hand side of the graph shows several exceptions as a result of this increase in volatility. The mapping of business units for P&L reporting may be different from that used for risk reporting. If extra positions are included in the P&L calculation that are missing from the risk calculation, this could give a risk figure that is too low to explain P&L fluctuations. This is much more likely to happen at a trading desk level than at the whole bank level. This problem is most likely to occur for trading desks that hold a mixture of trading book and banking book positions. Risk calculations may be done only for the trading book, but P&L may be calculated for both trading and banking book positions. Solutions To identify missing risk factors, the risk measurement method should be compared with the positions held by the trading desk in question. It is often helpful to discuss sources of risk with traders, as they often have a good idea of where the main risks

Figure 9.3

P&L distribution histogram.

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Backtesting graph – risk too low.

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Figure 9.4

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Figure 9.5

Backtesting graph – volatility increase.

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of their positions lie. A risk factor could be missed if instruments’ prices depend on a factor outside the four broad risk categories usually considered (e.g. prices of mortgage backed securities depend on real estate values). Also, positions may be taken that depend on spreads between two factors that the risk measurement system does not distinguish between (e.g. a long and a short bond position both fall in the same time bucket, and appear to hedge each other perfectly). When volatility increases suddenly, a short observation period could be substituted for the longer observation period usually used for calculating extreme moves. Most regulators allow this if the overall risk figure increases as a result. Mapping of business units for P&L and risk calculation should be the same. When this is a problem with banking and trading book positions held for the same trading desk, P&L should be broken down so that P&L arising from trading book positions can be isolated. Risk measured is too high There are no exceptions, and the P&L figures never even get near the risk figures (Figure 9.6). Possible causes It is often difficult to aggregate risk across risk factors and broad risk categories in a consistent way. Choosing too conservative a method for aggregation can give risk figures that are much too high. An example would be using a simple sum across delta, gamma, and vega risks, then also using a simple sum between interest rate, FX, and equity risk. In practice, losses in these markets would probably not be perfectly correlated, so the risk figure calculated in this way would be too high. A similar cause is that figures received for global aggregation may consist of sensitivities from some business units, but risk figures from others. Offsetting and diversification benefits between business units that report only total risk figures cannot be measured, so the final risk figure is too high. Solutions Aggregation across risk factors and broad risk categories can be done in a number of ways. None of these is perfect, and this article will not discuss the merits of each in detail. Possibilities include: Ω Historical simulation Ω Constructing a large correlation matrix including all risk factors Ω Assuming zero correlation between broad risk categories (regulators would require quantitative evidence justifying this assumption) Ω Assuming some worst-case correlation (between 1 and 0) that could be applied to the risks (rather than the sensitivities) no matter whether long or short positions were held in each broad risk category. To gain full offsetting and diversification benefits at a global level, sensitivities must be collected from all business units. If total risks are reported instead, there is no practical way of assessing the level of diversification or offsetting present. P&L has a positive bias There may be exceptions on the positive but not the negative side. Even without exceptions, the P&L bars are much more often positive than negative (Figure 9.7).

Figure 9.6

Backtesting graph – risk too high.

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Backtesting graph – positive P&L bias.

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Possible causes A successful trading desk makes a profit during the year, so unless the positive bias is very strong, the graph could be showing good trading results, rather than identifying a problem. The P&L figures may be missing funding costs or including a large contribution from fees and commissions. A positive bias is especially likely for a market making desk, or one that handles a lot of customer business where profits from this are not separated from P&L arising from holding nostro positions. Solutions P&L should include daily funding costs, and exclude income from fees and commissions. Risk ﬁgures are not being recalculated daily The risk lines on the backtesting graph show flat areas (Figure 9.8). Possible causes Almost flat areas indicating no change in risk over a number of days could occur for a proprietary trading desk that trades only occasionally, specialising in holding positions over a period of time. However, even in such a situation, small changes in risk would usually be expected due to change in market prices and rates related to the instruments held. Backtesting is often done centrally by a global risk management group. Sometimes risk figures from remote sites may be sent repeatedly without being updated, or if figures are not available, the previous day’s figures may be used. On a backtesting graph the unchanging risk shows up clearly as a flat area. Solutions First it is necessary to determine if the flat areas on the graph are from identical risk figures, or just ones that are approximately equal. Risk figures that are identical from one day to the next almost always indicate a data problem. The solution is for daily risk figures to be calculated for all trading desks. Instruments are not being marked to market daily P&L is close to zero most of the time, but shows occasional large values (Figure 9.9). Possible causes The portfolio may include illiquid instruments for which daily market prices are not available. Examples of securities that are often illiquid are corporate bonds, emerging market corporate bonds and equities, and municipal bonds. Dynamically changing provisions may be included in the P&L figures. This can smooth out fluctuations in P&L, and mean that a loss is only shown when the provisions have been used up. Including provisions can also lead to a loss being shown when a large position is taken on, and a corresponding provision is taken. In securitization, the assets being securitized may not be marked to market. P&L may be booked only when realized, or only when all asset backed securities have been sold. If the bank invests in external funds, these may not be marked to market daily.

Backtesting graph – risk values not updated.

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Figure 9.9

Backtesting graph – smooth P&L due to provisioning or infrequent valuation.

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However, such investments would not normally form part of the trading book. It is not usually possible to measure market risk in any meaningful way for such funds, because as an external investor, details of the funds’ positions would not be available. Solutions Illiquid instruments should be priced using a model where possible. For example, an illiquid corporate bond could be priced using a spread from the treasury yield curve for the appropriate currency. If the spread of another bond from the same issuer was available, this could be used as a proxy spread to price the illiquid bond. A similar approach could be used for emerging market corporate bonds. Emerging market equities could be assigned a price using a beta estimate, and the appropriate equity index. These last two methods would only give a very rough estimate of the values of the illiquid securities. However, such estimates would work better in a backtesting context than an infrequently updated price. Where possible, provisions should be excluded from P&L used for backtesting. Risk measurement methods were changed When risk measurement methods are changed, the backtesting graph may have a step in it where the method changes (Figure 9.10). This is not a problem in itself. For example, if a simple sum aggregation method was being used across some risk factors, this might prove too conservative. A correlation matrix may be introduced to make the risk measurement more accurate. The backtesting graph would then show a step change in risk due to the change in method. It is useful to be able to go back and recalculate the risk using the new method for old figures. Then a backtesting graph could be produced for the new method. Backtesting like this is valuable for checking and gaining regulatory approval for a new risk measurement method. Note that a step change in risk is also likely when extreme moves are updated infrequently (e.g. quarterly). This effect can be seen clearly in Figure 9.5, where the extreme move used is based on the historical volatility of the S&P 500 index.

P&L histograms Backtesting graphs clearly show the number of exceptions, but it is difficult to see the shape of the distribution of P&L outcomes. A P&L histogram (Figure 9.3) makes it easier to see if the distribution of P&L is approximately normal, skewed, fat-tailed, or if it has other particular features. Such a histogram can give additional help in diagnosing why backtesting exceptions occurred. The weakness of such histograms is that they show the distribution of P&L arising from a portfolio that changes with time. Even if the underlying risk factors were normally distributed, and the prices of securities depended linearly on those risk factors, the changing composition of the portfolio would result in a fat-tailed distribution. A histogram showing the P&L divided by the risk figure gives similar information to a backtesting graph. However, it is easier to see the shape of the distribution, rather than just the number of exceptions. Figure 9.11 shows an example. This graph can be useful in detecting a fat-tailed distribution that causes more exceptions than expected.

Systems requirements A backtesting system must store P&L and risk data, and be able to process it into a suitable form. It is useful to be able to produce backtesting graphs, and exception

Figure 9.10

Backtesting graph – risk measurement methods changed.

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P&L/risk distribution histogram.

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Figure 9.11

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statistics. The following data should be stored: Ω P&L figures broken down by: – Business unit (trading desk, trading book) – Source of P&L (fees and commissions, provisions, intraday trading, interday trading) Ω Risk figures broken down by: – Business unit The backtesting system should be able at a minimum to produce backtesting graphs, and numbers of exceptions at each level of the business unit hierarchy. The system should be able to process information in a timely way. Data must be stored so that at least 1 year’s history is available.

Review of backtesting results in annual reports Risk management has become a focus of attention in investment banking over the last few years. Most annual reports of major banks now have a section on risk management covering credit and market risk. Many of these now include graphs showing the volatility of P&L figures for the bank, and some show backtesting graphs. Table 9.2 shows a summary of what backtesting information is present in annual reports from a selection of banks. Table 9.2 Backtesting information in annual reports

Company

Date

Risk management section

Dresdner Bank Merrill Lynch Deutsche Bank J. P. Morgan Lehman Brothers ING Group ABN AMRO Holding Credit Suisse Group Sanwa Bank

1997 1997 1997 1997 1997 1997 1997 1997 1998

Yes Yes Yes Yes Yes Yes Yes Yes Yes

P&L graph

Backtesting graph

No Yesa No Yes Yesd No No Yes Noe

No No Nob Noc No No No Yes Yes

a

Merrill Lynch’s P&L graph is of weekly results. It shows 3 years’ results year by year for comparison. Deutsche Bank show a graph of daily value at risk c J. P. Morgan gives a graph of Daily Earnings at Risk (1-day holding period, 95% conﬁdence interval) for two years. The P&L histogram shows average DEaR for 1997, rebased to the mean daily proﬁt. d Lehman Brothers graph is of weekly results. e Sanwa Bank also show a scatter plot with risk on one axis and P&L on the other. A diagonal line indicates the conﬁdence interval below which a point would be an exception. b

Of the banks that compare risk to P&L, J. P. Morgan showed a number of exceptions (12 at the 95% level) that was consistent with expectations. They interpret their Daily Earnings at Risk (DEaR) figure in terms of volatility of earnings, and place the confidence interval around the mean daily earnings figure of $12.5 million. The shift of the P&L base for backtesting obviously increases the number of exceptions. It

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compensates for earnings that carry no market risk such as fees and commissions, but also overstates the number of exceptions that would be obtained from a clean P&L figure by subtracting the average profit from that figure. Comparing to the average DEaR could over or understate the number of exceptions relative to a comparison of each day’s P&L with the previous day’s risk figure. Credit Suisse Group show a backtesting graph for their investment bank, Credit Suisse First Boston. This graph plots the 1-day, 99% confidence interval risk figure against P&L (this is consistent with requirements for regulatory reporting). The graph shows no exceptions, and only one loss that even reaches close to half the 1-day, 99% risk figure. The Credit Suisse First Boston annual review for 1997 also shows a backtesting graph for Credit Suisse Financial Products, Credit Suisse First Boston’s derivative products subsidiary. This graph also shows no exceptions, and has only two losses that are around half of the 1-day, 99% risk figure. Such graphs show that the risk figure measured is overestimating the volatility of earnings. However, the graph shows daily trading revenue, not clean or hypothetical P&L prepared specially for backtesting. In a financial report, it may make more sense to show actual trading revenues than a specially prepared P&L figure that would be more difficult to explain. Sanwa Bank show a backtesting graph comparing 1-day, 99% confidence interval risk figures with actual P&L (this is consistent with requirements for regulatory reporting). Separate graphs are shown for the trading and banking accounts. The trading account graph shows only one loss greater than half the risk figure, while the banking account graph shows one exception. The trading account graph shows an overestimate of risk relative to volatility of earnings, while the banking account graph is consistent with statistical expectations The backtesting graphs presented by Credit Suisse Group and Sanwa Bank indicate a conservative approach to risk measurement. There are several good reasons for this: Ω It is more prudent to overestimate, rather than underestimate risk. This is especially so as market risk measurement systems in general do not have several years of proven performance. Ω From a regulatory point of view, overestimating risk is acceptable, whereas an underestimate is not. Ω Risk measurement methods may include a margin for extreme events and crises. Backtesting graphs for 1998 will probably show some exceptions. This review of annual reports shows that all banks reviewed have risk management sections in their annual reports. Backtesting information was only given in a few cases, but some information on volatility of P&L was given in over half the reports surveyed.

Conclusion This chapter has reviewed the backtesting process, giving practical details on how to perform backtesting. The often difficult task of obtaining useful profit and loss figures has been discussed in detail with suggestions on how to clean available P&L figures for backtesting purposes. Regulatory requirements have been reviewed, with specific discussion of the Basel Committee regulations, and the UK (FSA) and Swiss (EBK) regulations. Examples were given of how backtesting graphs can be used to pinpoint problems in P&L and risk calculation. The chapter concluded with a brief overview of backtesting information available in the annual reports of some investment banks.

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Appendix: Intra- and interday P&L For the purposes of backtesting, P&L from positions held from one day to the next must be separated from P&L due to trading during the day. This is because market risk measures only measure risk arising from the fluctuations of market prices and rates with a static portfolio. To make a meaningful comparison of P&L with risk, the P&L in question should likewise be the change in value of a static portfolio from close of trading one day to close of trading the next. This P&L will be called interday P&L. Contributions from trades during the day will be classified as intraday P&L. This appendix aims to give unambiguous definitions for inter- and intraday P&L, and show how they could be calculated for a portfolio. The basic information required for this calculation is as follows: Ω Prices of all instruments in the portfolio at the close of the previous business day. This includes the prices of all OTC instruments, and the price and number held of all securities or exchange traded contracts. Ω Prices of all instruments in the portfolio at the close of the current business day. This also includes the prices of all OTC instruments, and the price and number held of all securities or exchange traded contracts. Ω Prices of all OTC contracts entered into during the day. Price and amount of security traded for all securities trades (including exchange traded contract trades). The definitions shown are for single-security positions. They can easily be extended by summing together P&L for each security to form values for a whole portfolio. OTC contracts can be treated similarly to securities, except that they only have one intraday event. This is the difference between the value when the contract is entered into and its value at the end of that business day. Inter- and intraday P&L for a single-security position can be defined as follows: Interday P&LóN(t0 )(P(t1)ñP(t0 )) where N(t)ónumber of units of security held at time t P(t)óPrice of security at time t t 0 óClose of yesterday t 1 óClose of today This is also the definition of synthetic P&L. Intraday P&L is the total value of the day’s transactions marked to market at the end of the day. For a position in one security, this could be written: No. of trades

Intraday P&Ló(N(t1)ñN(t0 ))P(t1))ñ

;

*Ni Pi

ió1

where *Ni ónumber of units of security bought in trade i Pi óprice paid per unit of security in trade i The first term is the value of net amount of the security bought during the day valued at the end of the day. The second term can be interpreted as the cost of purchase of this net amount, plus any profit or loss made on trades during the day

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which result in no net increase or decrease in the amount of the security held. For a portfolio of securities, the intra- and interday P&L figures are just the sum of those for the individual securities. Examples 1. Hold the same position for one day Size of position Price at close of yesterday Price at close of today Intraday P&L Interday P&L

N(t0) and N(t1) P(t0) P(t1) N(t0)(P(t1)ñP(t0))

1000 units $100.00 $100.15 0 $150

With no trades during the day, there is no intraday P&L. 2. Sell off part of position Size of position at close of yesterday Price at close of yesterday Size of position at close of today Price at close of today Trade 1, sell 500 units at $100.10 Intraday P&L Interday P&L Total P&L

N(t0) P(t0) N(t1) P(t1) *N1 P1 (N(t1)ñN(t0))P(t1)ñ*N1P1 N(t0)(P(t1)ñP(t0)) Intraday P&Lòinterday P&L

1000 units $100.00 500 units $100.15 ñ500 units $100.10 ñ$25 $150 $125

The intraday P&L shows a loss of $25 from selling out the position ‘too soon’. 3. Buy more: increase position Size of position at close of yesterday Price at close of yesterday Size of position at close of today Price at close of today Trade 1, sell 500 units at $100.05 Intraday P&L Interday P&L Total P&L

N(t0) P(t0) N(t1) P(t1) *N1 P1 (N(t1)ñN(t0))P(t1)ñ*N1P1 N(t0)(P(t1)ñP(t0)) Intraday P&Lòinterday P&L

1000 units $100.00 1500 units $100.15 500 units $100.05 $50 $150 $200

Extra profit was generated by increasing the position as the price increased. 4. Buy then sell Size of position at close of yesterday Price at close of yesterday Size of position at close of today Price at close of today

N(t0) P(t0) N(t1) P(t1)

1000 units $100.00 500 units $100.15

Backtesting

Trade 1, sell 500 units at $100.05 Trade 2, sell 1000 units at $100.10 Intraday P&L Interday P&L Total P&L

289

*N1 P1 *N2 P2 (N(t1)ñN(t0))P(t1)ñ*N1P1 ñ*N2P2 N(t0)(P(t1)ñP(t0)) Intraday P&Lòinterday P&L

500 units $100.05 ñ1000 units $100.10 $0 $150 $150

The profit from buying 500 units as the price increased was cancelled by the loss from selling 1000 too soon, giving a total intraday P&L of zero.

References Basel Committee on Banking Supervision (1996a) Amendment to the Capital Accord to incorporate market risks, January. Basel Committee on Banking Supervision (1996b) Supervisory framework for the use of ‘backtesting’ in conjunction with the internal models approach to market risk capital requirements. Financial Services Authority (1998) Use of Internal Models to measure Market Risks, September. Eidgeno¨ ssische Bankenkommission (1997) Richtlinien zur Eigenmittelunterlegung von Marktrisiken. Basel Committee on Banking Supervision (1997a) Modifications to the market risk amendment: Textual changes to the Amendment to the Basel Capital Accord of January 1996, September. Basel Committee on Banking Supervision (1997b) Explanatory Note: Modification of the Basel Accord of July 1988, as amended in January 1996, September.

10

Credit risk management models RICHARD K. SKORA

Introduction Financial institutions are just beginning to realize the benefits of credit risk management models. These models are designed to help the risk manager project risk, measure profitability, and reveal new business opportunities. This chapter surveys the current state of the art in credit risk management models. It provides the reader with the tools to understand and evaluate alternative approaches to modeling. The chapter describes what a credit risk management model should do, and it analyses some of the popular models. We take a high-level approach to analysing models and do not spend time on the technical difficulties of their implementation and application.1 We conclude that the success of credit risk management models depends on sound design, intelligent implementation, and responsible application of the model. While there has been significant progress in credit risk management models, the industry must continue to advance the state of the art. So far the most successful models have been custom designed to solve the specific problems of particular institutions. As a point of reference we refer to several credit risk management models which have been promoted in the industry press. The reader should not interpret this as either an endorsement of these models or as a criticism of models that are not cited here, including this author’s models. Interested readers should pursue their own investigation and can begin with the many references cited below.

Motivation Banks are expanding their operation around the world; they are entering new markets; they are trading new asset types; and they are structuring exotic products. These changes have created new opportunities along with new risks. While banking is always evolving, the current fast rate of change is making it a challenge to respond to all the new opportunities. Changes in banking have brought both good and bad news. The bad news includes the very frequent and extreme banking debacles. In addition, there has been a divergence between international and domestic regulation as well as between regulatory capital and economic capital. More subtly, banks have wasted many valuable

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resources correcting problems and repairing outdated models and methodologies. The good news is that the banks which are responding to the changes have been rewarded with a competitive advantage. One response is the investment in risk management. While risk management is not new, not even in banking, the current rendition of risk management is new. Risk management takes a firmwide view of the institution’s risks, profits, and opportunities so that it may ensure optimal operation of the various business units. The risk manager has the advantage of knowing all the firm’s risks extending across accounting books, business units, product types, and counterparties. By aggregating the risks, the risk manager is in the unique position of ensuring that the firm may benefit from diversification. Risk management is a complicated, multifaceted profession requiring diverse experience and problem-solving skills (see Bessis, 1998). The risk manager is constantly taking on new challenges. Whereas yesterday a risk manager may have been satisfied with being able to report the risk and return characteristics of his firm’s various business units, today he or she is using that information to improve his firm’s business opportunities. Credit risk is traditionally the main risk of banks. Banks are in the business of taking credit risk in exchange for a certain return above the riskless rate. As one would expect, banks deal in the greatest number of markets and types of products. Banks above all other institutions, including corporations, insurance companies, and asset managers, face the greatest challenge in managing their credit risk. One of the credit risk managers’ tools is the credit risk management model.

Functionality of a good credit risk management model A credit risk management model tells the credit risk manager how to allocate scarce credit risk capital to various businesses so as to optimize the risk and return characteristics of the firm. It is important to understand that optimize does not mean minimize risk otherwise every firm would simply invest its capital in riskless assets. Optimize means for any given target return, minimize the risk. A credit risk management model works by comparing the risk and return characteristics between individual assets or businesses. One function is to quantify the diversification of risks. Being well-diversified means that the firm has no concentrations of risk to, say, one geographical location or one counterparty. Figure 10.1 depicts the various outputs from a credit risk management model. The output depicted by credit risk is the probability distribution of losses due to credit risk. This reports for each capital number the probability that the firm may lose that amount of capital or more. For a greater capital number, the probability is less. Of course, a complete report would also describe where and how those losses might occur so that the credit risk manager can take the necessary prudent action. The marginal statistics explain the affect of adding or subtracting one asset to the portfolio. It reports the new risks and profits. In particular, it helps the firm decide whether it likes that new asset or what price it should pay for it. The last output, optimal portfolio, goes beyond the previous two outputs in that it tells the credit risk manager the optimal mix of investments and/or business ventures. The calculation of such an output would build on the data and calculation of the previous outputs. Of course, Figure 10.1 is a wish lists of outputs. Actual models may only produce

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Figure 10.1 Various outputs of a portfolio credit risk management model.

some of the outputs for a limited number of products and asset classes. For example, present technology only allows one to calculate the optimal portfolio in special situations with severe assumptions. In reality, firms attain or try to attain the optimal portfolio through a series of iterations involving models, intuition, and experience. Nevertheless, Figure 10.1 will provide the framework for our discussion. Models, in most general terms, are used to explain and/or predict. A credit risk management model is not a predictive model. It does not tell the credit risk manger which business ventures will succeed and which will fail. Models that claim predictive powers should be used by the firm’s various business units and applied to individual assets. If these models work and the associated business unit consistently exceeds its profit targets, then the business unit would be rewarded with large bonuses and/ or increased capital. Regular success within a business unit will show up at the credit risk management level. So it is not a contradiction that the business unit may use one model while the risk management uses another. Credit risk management models, in the sense that they are defined here, are used to explain rather than predict. Credit risk management models are often criticized for their failure to predict (see Shirreff, 1998). But this is an unfair criticism. One cannot expect these models to predict credit events such as credit rating changes or even defaults. Credit risk management models can predict neither individual credit events nor collective credit events. For example, no model exists for predicting an increase in the general level of defaults. While this author is an advocate of credit risk management models and he has seen many banks realize the benefits of models, one must be cautioned that there

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are risks associated with developing models. At present many institutions are rushing to lay claim to the best and only credit risk management model. Such ambitions may actually undermine the risk management function for the following reasons. First, when improperly used, models are a distraction from the other responsibilities of risk management. In the bigger picture the model is simply a single component, though an important one, of risk management. Second, a model may undermine risk management if it leads to a complacent, mechanical reliance on the model. And more subtly it can stifle competition. The risk manager should have the incentive to innovate just like any other employee.

Review of Markowitz’s portfolio selection theory Harry Markowitz (1952, 1959) developed the first and most famous portfolio selection model which showed how to build a portfolio of assets with the optimal risk and return characteristics. Markowitz’s model starts with a collection of assets for which it is assumed one knows the expected returns and risks as well as all the pair-wise correlation of the returns. Here risk is defined as the standard deviation of return. It is a fairly strong assumption to assume that these statistics are known. The model further assumes that the asset returns are modeled as a standard multivariate normal distribution, so, in particular, each asset’s return is a standard normal distribution. Thus the assets are completely described by their expected return and their pairwise covariances of returns E[ri ] and Covariance(ri , rj )óE[ri rj ]ñE[ri ]E[rj ] respectively, where ri is the random variable of return for the ith asset. Under these assumptions Markowitz shows for a target expected return how to calculate the exact proportion to hold of each asset so as to minimize risk, or equivalently, how to minimize the standard deviation of return. Figure 10.2 depicts the theoretical workings of the Markowitz model. Two different portfolios of assets held by two different institutions have different risk and return characteristics. While one may slightly relax the assumptions in Markowitz’s theory, the assumptions are still fairly strong. Moreover, the results are sensitive to the inputs; two users of the theory who disagree on the expected returns and covariance of returns may calculate widely different portfolios. In addition, the definition of risk as the standard deviation of returns is only reasonable when returns are a multi-normal distribution. Standard deviation is a very poor measure of risk. So far there is no consensus on the right probability distribution when returns are not a multi-normal distribution. Nevertheless, Markowitz’s theory survives because it was the first portfolio theory to quantify risk and return. Moreover, it showed that mathematical modeling could vastly improve portfolio theory techniques. Other portfolio selection models are described in Elton and Gruber (1991).

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Figure 10.2 Space of possible portfolios.

Adapting portfolio selection theory to credit risk management Risk management distinguishes between market risk and credit risk. Market risk is the risk of price movement due either directly or indirectly to changes in the prices of equity, foreign currency, and US Treasury bonds. Credit risk is the risk of price movement due to credit events. A credit event is a change in credit rating or perceived credit rating, which includes default. Corporate, municipal, and certain sovereign bond contain credit risk. In fact, it is sometimes difficult to distinguish between market risk and credit risk. This has led to debate over whether the two risks should be managed together, but this question will not be debated here. Most people are in agreement that the risks are different, and risk managers and their models must account for the differences. As will be seen below, our framework for a credit risk management model contains a market risk component. There are several reasons why Markowitz’s portfolio selection model is most easily applied to equity assets. First, the model is what is called a single-period portfolio model that tells one how to optimize a portfolio over a single period, say, a single day. This means the model tells one how to select the portfolio at the beginning of the period and then one holds the portfolio without changes until the end of the period. This is not a disadvantage when the underlying market is liquid. In this case, one just reapplies the model over successive periods to determine how to manage the

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portfolio over time. Since transaction costs are relatively small in the equity markets, it is possible to frequently rebalance an equity portfolio. A second reason the model works well in the equity markets is that their returns seem to be nearly normal distributions. While much research on equity assets shows that their returns are not perfectly normal, many people still successfully apply Markowitz’s model to equity assets. Finally, the equity markets are very liquid and deep. As such there is a lot of data from which to deduce expected returns and covariances of returns. These three conditions of the equity markets do not apply to the credit markets. Credit events tend to be sudden and result in large price movements. In addition, the credit markets are sometimes illiquid and have large transaction costs. As a result many of the beautiful theories of market risk models do not apply to the credit markets. Since credit markets are illiquid and transactions costs are high, an appropriate single period can be much longer that a single day. It can be as long as a year. In fact, a reasonable holding period for various instruments will differ from a day to many years. The assumption of normality in Markowitz portfolio model helps in another way. It is obvious how to compare two normal distributions, namely, less risk is better than more risk. In the case of, say, credit risk, when distributions are not normal, it is not obvious how to compare two distributions. For example, suppose two assets have probability distribution of losses with the same mean but standard deviations of $8 and $10, respectively. In addition, suppose they have maximum potential losses of $50 and $20, respectively. Which is less risky? It is difficult to answer and depends on an economic utility function for measuring. The theory of utility functions is another field of study and we will not discuss it further. Any good portfolio theory for credit risk must allow for the differences between market and credit risk.

A framework for credit risk management models This section provides a framework in which to understand and evaluate credit risk management models. We will describe all the components of a complete (or nearly complete) credit risk model. Figure 10.3 labels the major components of a credit risk model. While at present there is no model that can do everything in Figure 10.3, this description will be a useful reference by which to evaluate all models. As will be seen below, portfolio models have a small subset of the components depicted in Figure 10.3. Sometimes by limiting itself to particular products or particular applications, a model is able to either ignore a component or greatly simplify it. Some models simply settle for an approximately correct answer. More detailed descriptions and comparisons of some of these models may be found in Gordy (1998), Koyluglu and Hickman (1998), Lopez and Saidenber (1998), Lentino and Pirzada (1998), Locke (1998), and Crouhy and Mark (1998). The general consensus seems to be that we stand to learn much more about credit risk. We have yet to even scratch the surface in bringing high-powered, mathematical techniques to bear on these complicated problems. It would be a mistake to settle for the existing state of the art and believe we cannot improve. Current discussions should promote original, customized solutions and thereby encourage active credit risk management.

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Figure 10.3 Portfolio credit risk model.

Value-at-Risk Before going into more detail about credit risk management models, it would be instructive to say a few words about Value-at-Risk. The credit risk management modeling framework shares many features with this other modeling framework called Value-at-Risk. This has resulted in some confusions and mistakes in the industry, so it is worth-while explaining the relationship between the two frameworks. Notice we were careful to write framework because Value-at-Risk (VaR) is a framework. There are many different implementations of VaR and each of these implementations may be used differently. Since about 1994 bankers and regulators have been using VaR as part of their risk management practices. Specifically, it has been applied to market risk management. The motivation was to compute a regulatory capital number for market risk. Given a portfolio of assets, Value-at-Risk is defined to be a single monetary capital number which, for a high degree of confidence, is an upper bound on the amount of gains or losses to the portfolio due to market risk. Of course, the degree of confidence must be specified and the higher that degree of confidence, the higher the capital number. Notice that if one calculates the capital number for every degree of confidence then one has actually calculated the entire probability distribution of gains or losses (see Best, 1998). Specific implementation of VaR can vary. This includes the assumptions, the model, the input parameters, and the calculation methodology. For example, one implementation may calibrate to historical data and another to econometric data. Both implementations are still VaR models, but one may be more accurate and useful than the other may. For a good debate on the utility of VaR models see Kolman, 1997. In practice, VaR is associated with certain assumptions. For example, most VaR implementations assume that market prices are normally distributed or losses are

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independent. This assumption is based more on convenience than on empirical evidence. Normal distributions are easy to work with. Value-at-Risk has a corresponding definition for credit risk. Given a portfolio of assets, Credit Value-at-Risk is defined to be a single monetary capital number which, for a high degree of confidence, is an upper bound on the amount of gains or losses to the portfolio due to credit risk. One should immediately notice that both the credit VaR model and the credit risk management model compute a probability distribution of gains or losses. For this reason many risk managers and regulators do not distinguish between the two. However, there is a difference between the two models. Though the difference may be more of one of the mind-frame of the users, it is important. The difference is that VaR models put too much emphasis on distilling one number from the aggregate risks of a portfolio. First, according to our definition, a credit risk management model also computes the marginal affect of a single asset and it computes optimal portfolios which assist in making business decisions. Second, a credit risk management model is a tool designed to assist credit risk managers in a broad range of dynamic credit risk management decisions. This difference between the models is significant. Indeed, some VaR proponents have been so driven to produce that single, correct capital number that it has been at the expense of ignoring more important risk management issues. This is why we have stated that the model, its implementation, and their applications are important. Both bankers and regulators are currently investigating the possibility of using the VaR framework for credit risk management. Lopez and Saidenberg (1998) propose a methodology for generating credit events for the purpose of testing and comparing VaR models for calculating regulatory credit capital.

Asset credit risk model The first component is the asset credit risk model that contains two main subcomponents: the credit rating model and the dynamic credit rating model. The credit rating model calculates the credit riskiness of an asset today while the dynamic credit rating model calculates how that riskiness may evolve over time. This is depicted in more detail in Figure 10.4. For example, if the asset is a corporate bond, then the credit riskiness of the asset is derived from the credit riskiness of the issuer. The credit riskiness may be in the form of a probably of default or in the form of a credit rating. The credit rating may correspond to one of the international credit rating services or the institution’s own internal rating system. An interesting point is that the credit riskiness of an asset can depend on the particular structure of the asset. For example, the credit riskiness of a bond depends on its seniority as well as its maturity. (Short- and long-term debt of the same issuer may have different credit ratings.) The credit risk does not necessarily need to be calculated. It may be inputted from various sources or modeled from fundamentals. If it is inputted it may come from any of the credit rating agencies or the institution’s own internal credit rating system. For a good discussion of banks’ internal credit rating models see Treacy and Carey (1998). If the credit rating is modeled, then there are numerous choices – after all, credit risk assessment is as old as banking itself. Two examples of credit rating models are the Zeta model, which is described in Altman, Haldeman, and Narayanan (1977), and the Lambda Index, which is described in Emery and Lyons (1991). Both models are based on the entity’s financial statements.

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Figure 10.4 Asset credit risk model.

Another well-publicized credit rating model is the EDF Calculator. The EDF model is based on Robert Merton’s (1974) observation that a firm’s assets are the sum of its equity and debt, so the firm defaults when the assets fall below the face value of the debt. It follows that debt may be thought of as a short option position on the firm’s assets, so one may apply the Black–Scholes option theory. Of course, real bankruptcy is much more complicated and the EDF Calculator accounts for some of these complications. The model’s strength is that it is calibrated to a large database of firm data including firm default data. The EDF Calculator actually produces a probability of default, which if one likes, can be mapped to discrete credit ratings. Since the EDF model is proprietary there is no public information on it. The interested reader may consult Crosbie (1997) to get a rough description of its workings. Nickell, Perraudin, and Varotto (1998) compare various credit rating models including EDF. To accurately measure the credit risk it is essential to know both the credit riskiness today as well as how that credit riskiness may evolve over time. As was stated above, the dynamic credit rating model calculates how an asset’s credit riskiness may evolve over time. How this component is implemented depends very much on the assets in the portfolio and the length of the time period for which risk is being calculated. But if the asset’s credit riskiness is not being modeled explicitly, it is at least implicitly being modeled somewhere else in the portfolio model, for

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example in a pricing model – changes in the credit riskiness of an asset are reflected in the price of that asset. Of course, changes in credit riskiness of various assets are related. So Figure 10.4 also depicts a component for the correlation of credit rating which may be driven by any number of variables including historical, econometric, or market variables. The oldest dynamic credit rating model is the Markov model for credit rating migration. The appeal of this model is its simplicity. In particular, it is easy to incorporate non-independence of two different firm’s credit rating changes. The portfolio model CreditMetrics (J.P. Morgan, 1997) uses this Markov model. The basic assumption of the Markov model is that a firm’s credit rating migrates at random up or down like a Markov process. In particular, the migration over one time period is independent of the migration over the previous period. Credit risk management models based on a Markov process are implemented by Monte Carlo simulation. Unfortunately, there has been recent research showing that the Markov process is a poor approximation to the credit rating process. The main reason is that the credit rating is influenced by the economy that moves through business cycles. Thus the probability of downgrade and, thus, default is greater during a recession. Kolman (1998) gives a non-technical explanation of this fact. Also Altman, and Kao (1991) mention the shortcomings of the Markov process and propose two alternative processes. Nickell, Perraudin, and Varotto (1998a,b) give a more thorough criticism of Markov processes by using historical data. In addition, the credit rating agencies have published insightful information on their credit rating and how they evolve over time. For example, see Brand, Rabbia and Bahar (1997) or Carty (1997). Another credit risk management model, CreditRiskò, models only two states: nondefault and default (CSFP, 1997). But this is only a heuristic simplification. Rolfes and Broeker (1998) have shown how to enhance CreditRiskò to model a finite number of credit rating states. The main advantage of the CreditRiskò model is that it was designed with the goal of allowing for an analytical implementation as opposed to Monte Carlo. The last model we mention is Portfolio View (McKinsey, 1998). This model is based on econometric models and looks for relationships between the general level of default and economic variables. Of course, predicting any economic variable, including the general level of defaults, is one of the highest goals of research economics. Risk managers should proceed with caution when they start believing they can predict risk factors. As mentioned above, it is the extreme events that most affect the risk of a portfolio of credit risky assets. Thus it would make sense that a model which more accurately measures the extreme event would be a better one. Wilmott (1998) devised such a model called CrashMetrics. This model is based on the theory that the correlation between events is different from times of calm to times of crisis, so it tries to model the correlation during times of crisis. This theory shows great promise. See Davidson (1997) for another discussion of the various credit risk models.

Credit risk pricing model The next major component of the model is the credit risk pricing model, which is depicted in detail in Figure 10.5. This portion of the model together with the market

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Figure 10.5 Credit risk pricing model.

risk model will allow the credit risk management model to calculate the relevant return statistics. The credit risk pricing model is necessary because the price of credit risk has two components. One is the credit rating that was handled by the previous component, the other is the spread over the riskless rate. The spread is the price that the market charges for a particular credit risk. This spread can change without the underlying credit risk changing and is affected by supply and demand. The credit risk pricing model can be based on econometric models or any of the popular risk-neutral pricing models which are used for pricing credit derivatives. Most risk-neutral credit pricing models are transplants of risk-neutral interest rate pricing models and do not adequately account for the differences between credit risk and interest rate risk. Nevertheless, these risk-neutral models seem to be popular. See Skora (1998a,b) for a description of the various risk-neutral credit risk pricing models. Roughly speaking, static models are sufficient for pricing derivatives which do not have an option component and dynamic models are necessary for pricing derivatives which do have an option component. As far as credit risk management models are concerned, they all need a dynamic credit risk term structure model. The reason is that the credit risk management model needs both the expected return of each asset as well as the covariance matrix of returns. So even if one had both the present price of the asset and the forward price, one would still need to calculate the probability distribution of returns. So the credit risk model calculates the credit risky term structure, that is, the yield curve for the various credit risky assets. It also calculates the corresponding term structure for the end of the time period as well as the distribution of the term structure. One way to accomplish this is by generating a sample of what the term structure may look like at the end of the period. Then by pricing the credit risky assets off these various term structures, one obtains a sample of what the price of the assets may be. Since credit spreads do not move independently of one another, the credit risk pricing model, like the asset credit risk model, also has a correlation component. Again depending on the assets in the portfolio, it may be possible to economize and combine this component with the previous one.

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Finally, the choice of inputs can be historical, econometric or market data. The choice depends on how the portfolio selection model is to be used. If one expects to invest in a portfolio and divest at the end of the time period, then one needs to calculate actual market prices. In this case the model must be calibrated to market data. At the other extreme, if one were using the portfolio model to simply calculate a portfolio’s risk or the marginal risk created by purchasing an additional asset, then the model may be calibrated to historical, econometric, or market data – the choice is the risk manager’s.

Market risk pricing model The market risk pricing model is analogous to the credit risk pricing model, except that it is limited to assets without credit risk. This component models the change in the market rates such as credit-riskless, US Treasury interest rates. To price all the credit risky assets completely and accurately it is necessary to have both a market risk pricing model and credit risk pricing model. Most models, including CreditMetrics, CreditRiskò, Portfolio Manager, and Portfolio View, have a dynamic credit rating model but lack a credit risk pricing model and market risk pricing model. While the lack of these components partially cripples some models, it does not completely disable them. As such, these models are best suited to products such as loans that are most sensitive to major credit events like credit rating migration including defaults. Two such models for loans only are discussed in Spinner (1998) and Belkin et al. (1998).

Exposure model The exposure model is depicted in Figure 10.6. This portion of the model aggregates the portfolio of assets across business lines and legal entities and any other appropriate category. In particular, netting across a counterparty would take into account the relevant jurisdiction and its netting laws. Without fully aggregating, the model cannot accurately take into account diversification or the lack of diversification. Only after the portfolio is fully aggregated and netted can it be correctly priced. At this point the market risk pricing model and credit risk pricing model can actually price all the credit risky assets. The exposure model also calculates for each asset the appropriate time period, which roughly corresponds to the amount of time it would take to liquidate the asset. Having a different time period for each asset not only increases the complexity of the model, it also raises some theoretical questions. Should the time period corresponding to an asset be the length of time it takes to liquidate only that asset? To liquidate all the assets in the portfolio? Or to liquidate all the assets in the portfolio in a time of financial crisis? The answer is difficult. Most models simply use the same time period, usually one year, for all exposures. One year is considered an appropriate amount of time for reacting to a credit loss whether that be liquidating a position or raising more capital. There is an excellent discussion of this issue in Jones and Mingo (1998). Another responsibility of the exposure model is to report the portfolio’s various concentrations.

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Figure 10.6 Exposure model and some statistics.

Risk calculation engine The last component is the risk calculation engine which actually calculates the expected returns and multivariate distributions that are then used to calculate the associated risks and the optimal portfolio. Since the distributions are not normal, this portion of the portfolio model requires some ingenuity. One method of calculation is Monte Carlo simulation. This is exemplified in many of the above-mentioned models. Another method of calculating the probability distribution is numerical. One starts by approximating the probability distribution of losses for each asset by a discrete probability distribution. This is a reasonable simplification because one is mainly interested in large, collective losses – not individual firm losses. Once the individual probability distributions have been discretized, there is a well-known computation called convolution for computing the aggregate probability distribution. This numerical method is easiest when the probability distributions are independent – which in this case they are not. There are tricks and enhancements to the convolution technique to make it work for nonindependent distributions. The risk calculation engine of CreditRiskò uses the convolution. It models the nonindependence of defaults with a factor model. It assumes that there is a finite number of factors which describe nonindependence. Such factors would come from the firm’s country, geographical location, industry, and specific characteristics.

Capital and regulation Regulators ensure that our financial system is safe while at the same time that it prospers. To ensure that safety, regulators insist that a bank holds sufficient capital

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Component

Subcomponent

Counterparty credit Fundamental data risk model Credit rating Credit rating model Correlation model Credit risk model Credit risk term structure Dynamic credit risk term structure Recovery model Correlation model Market risk model

Products

Aggregation

Output

Other

Interest rate model Foreign exchange rate model Correlation Loans Bonds Derivatives Structured products Collateral Risk reduction Liquidity Concentration Legal Products Counterparty Business Books Probability distribution Capital Marginal statistics Optimization Stress Scenario

Question Accepts fundamental counterparty, market, or economic data? Computes credit rating? Models evolution of credit rating? Changes in credit rating are non-independent? Accurately constructs credit risk term structure? Robust model of changes in credit risk term structure? Recovery is static or dynamic? Changes in credit risk term structure nonindependent? Robust model of changes in riskless interest rate term structure? Robust model of changes in exchange rates? Market risk and credit risk non-independent? Accepts loans? Accepts bonds? Accepts derivatives? Accepts credit derivatives, credit-linked notes, etc.? Accepts collateral? Models covenants, downgrade triggers, etc.? Accounts for differences in liquidity? Calculates limits? Nets according to legal jurisdiction? Aggregate across products? Aggregate across counterparty? Aggregate across business lines? Aggregate across bank books? Computes cumulative probability distribution of losses? Computes economic capital? Computes marginal statistics for one asset? Computes optimal portfolio? Performs stress tests? Performs scenario tests?

to absorb losses. This includes losses due to market, credit, and all other risks. The proper amount of capital raises interesting theoretical and practical questions. (See, for example, Matten, 1996 or Pratt, 1998.) Losses due to market or credit risk show up as losses to the bank’s assets. A bank should have sufficient capital to absorb not only losses during normal times but also losses during stressful times. In the hope of protecting our financial system and standardizing requirements around the world the 1988 Basel Capital Accord set minimum requirements for calculating bank capital. It was also the intent of regulators to make the rules simple. The Capital Accord specified that regulatory capital is 8% of risk-weighted assets. The risk weights were 100%, 50%, 20%, or 0% depending on the asset. For example, a loan to an OECD bank would have a risk weighting of 20%. Even at the time the

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regulators knew there were shortcomings in the regulation, but it had the advantage of being simple. The changes in banking since 1988 have proved the Capital Accord to be very inadequate – Jones and Mingo (1998) discuss the problems in detail. Banks use exotic products to change their regulatory capital requirements independent of their actual risk. They are arbitraging the regulation. Now there is arbitrage across banking, trading, and counterparty bank books as well as within individual books (see Irving, 1997). One of the proposals from the industry is to allow banks to use their own internal models to compute regulatory credit risk capital similar to the way they use VaR models to compute add-ons to regulatory market risk capital. Some of the pros and cons of internal models are discussed in Irving (1997). The International Swaps and Derivatives Association (1998) has proposed a model. Their main point is that regulators should embrace models as soon as possible and they should allow the models to evolve over time. Regulators are examining ways to correct the problems in existing capital regulation. It is a very positive development that the models, and their implementation, will be scrutinized before making a new decision on regulation. The biggest mistake the industry could make would be to adopt a one-size-fits all policy. Arbitrarily adopting any of these models would certainly stifle creativity. More importantly, it could undermine responsibility and authority of those most capable of carrying out credit risk management.

Conclusion The rapid proliferation of credit risk models, including credit risk management models, has resulted in sophisticated models which provide crucial information to credit risk managers (see Table 10.1). In addition, many of these models have focused attention on the inadequacy of current credit risk management practices. Firms should continue to improve these models but keep in mind that models are only one tool of credit risk management. While many banks have already successfully implemented these models, we are a long way from having a ‘universal’ credit risk management model that handles all the firm’s credit risky assets.

Author’s note This paper is an extension of Richard K. Skora, ‘Modern credit risk modeling’, presented at the meeting of the Global Association of Risk Professionals. 19 October 1998.

Note 1

Of course implementing and applying a model is a crucial step in realizing the benefits of modeling. Indeed, there is a feedback effect, the practicalities of implemention and application affect many decisions in the modeling process.

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References Altman, E., Haldeman, R. G. and Narayanan, P. (1997) ‘ZETA analysis: A new model to identify bankruptcy risk of corporations’, J. Banking and Finance, 1, 29–54. Altman, E. I. and Kao, D. L. (1991) ‘Examining and modeling corporate bond rating drift’, working paper, New York University Salomon Center (New York, NY). Belkin, B., Forest, L., Aguais, S. and Suchower, S. (1998) ‘Expect the unexpected’, CreditRisk – a Risk special report, Risk, 11, No. 11, 34–39. Bessis, J. (1998) Risk Management in Banking, Wiley, Chichester. Best, P. (1998) Implementing Value at Risk, Wiley, Chichester. Brand, L., Rabbia, J. and Bahar, R. (1997) Rating Performance 1996: Stability and Transition, Standard & Poor’s. Carty, L. V. (1997) Moody’s Rating Migration and Credit Quality Correlation, 1920– 1996, Moody’s. Crosbie, P. (1997) Modeling Default Risk, KMV Corporation. Crouhy, M. and Mark, R. (1998) ‘A comparative analysis of current credit risk models’, Credit Risk Modelling and Regulatory Implications, organized by The Bank of England and Financial Services Authority, 21–22 September. Davidson, C. (1997) ‘A credit to the system’, CreditRisk – supplement to Risk, 10, No. 7, July, 61–4. Credit Suisse Financial Products (1997) CreditRiskò. Dowd, K. (1998) Beyond Value at Risk, Wiley, Chichester. Elton, E. J. and Gruber, M. J. (1991) Modern Portfolio Theory and Investment Analysis, fourth edition, Wiley, New York. Emery, G. W. and Lyons, R. G. (1991) ‘The Lambda Index: beyond the current ration’, Business Credit, November/December, 22–3. Gordy, M. B. (1998) ‘A comparative anatomy of credit risk models’, Finance and economics discussion series, Federal Reserve Board, Washington DC. Irving, R. (1997) ‘The internal question’, Credit Risk – supplement to Risk, 10, No. 7, July, 36–8. International Swaps and Derivatives Association (1998) Credit Risk and Regulatory Capital, March. Jones, D. and Mingo, J. (1998) ‘Industry practices in credit risk modeling and internal capital allocations: implications for a models-based regulatory standard’, in Financial Services at the Crossroads: Capital Regulation in the Twenty First Century, Federal Reserve Bank of New York, February. Jorion, P. (1997) Value at Risk, McGraw-Hill, New York. Kolman, J. (1997) ‘Roundtable on the limits of VAR’, Derivatives Strategy, 3, No. 4, April, 14–22. Kolman, J. (1998) ‘The world according to Edward Altman’, Derivatives Strategy, 3, No. 12, 47–51 supports the statement that the models do not try to match reality. Koyluoglu, H. V. and Hickman, A. (1998) ‘Reconcilable differences’, Risk, 11, No. 10, October, 56–62. Lentino, J. V. and Pirzada, H. (1998) ‘Issues to consider in comparing credit risk management models’, J. Lending & Credit Risk Management, 81, No. 4, December, 16–22. Locke, J. (1998) ‘Off-the-peg, off the mark?’ CreditRisk – a Risk special report, Risk, 11, No. 11, November, 22–7. Lopez, J. A. and Saidenberg, M. R. (1998) ‘Evaluating credit risk models’, Credit Risk

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Modelling and Regulatory Implications, organized by The Bank of England and Financial Services Authority, 21–22 September. Markowitz, H. (1952) ‘Portfolio selection’, J. of Finance, March, 77–91. Markowitz, H. (1959) Portfolio Selection, Wiley, New York. Matten, C. (1996) Managing Bank Capital, Wiley, Chichester. Merton, R. C. (1974) ‘On the pricing of corporate debt: the risk structure of interest rates’, Journal of Finance, 29, 449–70. J. P. Morgan (1997) CreditMetrics. McKinsey & Company, Inc. (1998) A Credit Portfolio Risk Measurement & Management Approach. Pratt, S. P. (1998) Cost of Capital, Wiley, New York. Rhode, W. (1998a) ‘McDonough unveils Basel review’, Risk, 11, No. 9, September. Nickell, P., Perraudin, W. and Varotto, S. (1998a) ‘Stability of rating transitions’, Credit Risk Modelling and Regulatory Implications, organized by The Bank of England and Financial Services Authority, 21–22 September. Nickell, P., Perraudin, W. and Varotto, S. (1998b) ‘Ratings-versus equity-based risk modelling’, Credit Risk Modelling and Regulatory Implications, organized by The Bank of England and Financial Services Authority, 21–22 September. Rolfes, B. and Broeker, F. (1998) ‘Good migrations’, Risk, 11, No. 11, November, 72–5. Shirreff, D. (1998) ‘Models get a thrashing’, Euromoney Magazine, October. Skora, R. K. (1998a) ‘Modern credit risk modeling’, presented at the meeting of the Global Association of Risk Professionals, 19 October. Skora, R. K. (1998b) Rational modelling of credit risk and credit derivatives’, in Credit Derivatives – Applications for Risk Management, Investment and Portfolio Optimization, Risk Books, London. Spinner, K. (1998) ‘Managing bank loan risk’, Derivatives Strategy, 3, No. 1, January, 14–22. Treacy, W. and Carey, M. (1998) ‘Internal credit risk rating systems at large U.S. banks’, Credit Risk Modelling and Regulatory Implications, organized by The Bank of England and Financial Services Authority, 21–22 September. Wilmott, P. (1998) CrashMetrics, Wilmott Associates, March.

11

Risk management of credit derivatives KURT S. WILHELM

Introduction Credit risk is the largest single risk in banking. To enhance credit risk management, banks actively evaluate strategies to identify, measure, and control credit concentrations. Credit derivatives, a market that has grown from virtually zero in 1993 to an estimated $350 billion at year end 1998,1 have emerged as an increasingly popular tool. Initially, banks used credit derivatives to generate revenue; more recently, bank usage has evolved to using them as a capital and credit risk management tool. This chapter discusses the types of credit derivative products, market growth, and risks. It also highlights risk management practices that market participants should adopt to ensure that they use credit derivatives in a safe and sound manner. It concludes with a discussion of a portfolio approach to credit risk management. Credit derivatives can allow banks to manage credit risk more effectively and improve portfolio diversification. Banks can use credit derivatives to reduce undesired risk concentrations, which historically have proven to be a major source of bank financial problems. Similarly, banks can assume risk, in a diversification context, by targeting exposures having a low correlation with existing portfolio risks. Credit derivatives allow institutions to customize credit exposures, creating risk profiles unavailable in the cash markets. They also enable creditors to take risk-reducing actions without adversely impacting the underlying credit relationship. Users of credit derivatives must recognize and manage a number of associated risks. The market is new and therefore largely untested. Participants will undoubtedly discover unanticipated risks as the market evolves. Legal risks, in particular, can be much higher than in other derivative products. Similar to poorly developed lending strategies, the improper use of credit derivatives can result in an imprudent credit risk profile. Institutions should avoid material participation in the nascent credit derivatives market until they have fully explored, and developed a comfort level with, the risks involved. Originally developed for trading opportunities, these instruments recently have begun to serve as credit risk management tools. This chapter primarily deals with the credit risk management aspects of banks’ use of credit derivatives. Credit derivatives have become a common element in two emerging trends in how banks assess their large corporate credit portfolios. First, larger banks increasingly devote human and capital resources to measure and model credit portfolio risks more quantitatively, embracing the tenets of modern portfolio theory (MPT). Banks

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have pursued these efforts to increase the efficiency of their credit portfolios and look to increase returns for a given level of risk or, conversely, to reduce risks for a given level of returns. Institutions adopting more advanced credit portfolio measurement techniques expect that increased portfolio diversification and greater insight into portfolio risks will result in superior relative performance over the economic cycle. The second trend involves tactical bank efforts to reduce regulatory capital requirements on high-quality corporate credit exposures. The current Basel Committee on Bank Supervision Accord (‘Basel’) requirements of 8% for all corporate credits, regardless of underlying quality, reduce banks’ incentives to make higher quality loans. Banks have used various securitization alternatives to reconcile regulatory and economic capital requirements for large corporate exposures. Initially, these securitizations took the form of collateralized loan obligations (CLOs). More recently, however, banks have explored ways to reduce the high costs of CLOs, and have begun to consider synthetic securitization structures. The synthetic securitization structures banks employ to reduce regulatory capital requirements for higher-grade loan exposures use credit derivatives to purchase credit protection against a pool of credit exposures. As credit risk modeling efforts evolve, and banks increasingly embrace a MPT approach to credit risk management, banks increasingly may use credit derivatives to adjust portfolio risk profiles.

Size of the credit derivatives market and impediments to growth The first credit derivative transactions occurred in the early 1990s, as large derivative dealers searched for ways to transfer risk exposures on financial derivatives. Their objective was to be able to increase derivatives business with their largest counterparties. The market grew slowly at first. More recently, growth has accelerated as banks have begun to use credit derivatives to make portfolio adjustments and to reduce risk-based capital requirements. As discussed in greater detail below, there are four credit derivative products: credit default swaps (CDS), total return swaps (TRS), credit-linked notes (CLNs) and credit spread options. Default swaps, total return swaps and credit spread options are over-the-counter transactions, while credit-linked notes are cash market securities. Market participants estimate the current global market for credit derivatives will reach $740 billion by the year 2000.2 Bank supervisors in the USA began collecting credit derivative information in Call Reports as of 31 March 1997. Table 11.1 tracks the quarterly growth in credit derivatives for both insured US banks, and all institutions filing Call Reports (which includes uninsured US offices of foreign branches). The table’s data reflect substantial growth in credit derivatives. Over the two years US bank supervisors have collected the data, the compounded annual growth rate of notional credit derivatives for US insured banks, and all reporting entities (including foreign branches and agencies), were 216.2% and 137.2% respectively. Call Report data understates the size of the credit derivatives market. First, it includes only transactions for banks domiciled in the USA. It does not include the activities of banks domiciled outside the USA, or any non-commercial banks, such as investment firms. Second, the data includes activity only for off-balance sheet transactions; therefore, it completely excludes CLNs.

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Table 11.1 US credit derivatives market quarterly growth (billions) 31-3-97 30-6-97 30-9-97 31-12-97 31-3-98 30-6-98 30-9-98 31-12-98 31-3-99 Insured US banks US banks, foreign branches and agencies

$19.1

$25.6

$38.9

$54.7

$91.4

$129.2 $161.8 $144.1 $191.0

$40.7

$69.0

$72.9

$97.1

$148.3 $208.9 $217.1 $198.7 $229.1

Source: Call Reports

Activity in credit derivatives has grown rapidly over the past two years. Nevertheless, the number of institutions participating in the market remains small. Like financial derivatives, credit derivatives activity in the US banking system is concentrated in a small group of dealers and end-users. As of 31 March 1999, only 24 insured banking institutions, and 38 uninsured US offices (branches and agencies) of foreign banks reported credit derivatives contracts outstanding. Factors that account for the narrow institutional participation include: 1 2 3 4 5

Difficulty of measuring credit risk Application of risk-based capital rules Credit risk complacency and hedging costs Limited ability to hedge illiquid exposures and Legal and cultural issues.

An evaluation of these factors helps to set the stage for a discussion of credit derivative products and risk management issues, which are addressed in subsequent sections.

Difﬁculty of measuring credit risk Measuring credit risk on a portfolio basis is difficult. Banks traditionally measure credit exposures by obligor and industry. They have only recently attempted to define risk quantitatively in a portfolio context, e.g. a Value-at-Risk (VaR) framework.3 Although banks have begun to develop internally, or purchase, systems that measure VaR for credit, bank managements do not yet have confidence in the risk measures the systems produce. In particular, measured risk levels depend heavily on underlying assumptions (default correlations, amount outstanding at time of default, recovery rates upon default, etc.), and risk managers often do not have great confidence in those parameters. Since credit derivatives exist principally to allow for the effective transfer of credit risk, the difficulty in measuring credit risk and the absence of confidence in the results of risk measurement have appropriately made banks cautious about using credit derivatives. Such difficulties have also made bank supervisors cautious about the use of banks’ internal credit risk models for regulatory capital purposes. Measurement difficulties explain why banks have not, until very recently, tried to implement measures to calculate Value-at-Risk (VaR) for credit. The VaR concept, used extensively for market risk, has become so well accepted that bank supervisors allow such measures to determine capital requirements for trading portfolios.4 The models created to measure credit risk are new, and have yet to face the test of an economic downturn. Results of different credit risk models, using the same data, can

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vary widely. Until banks have greater confidence in parameter inputs used to measure the credit risk in their portfolios, they will, and should, exercise caution in using credit derivatives to manage risk on a portfolio basis. Such models can only complement, but not replace, the sound judgment of seasoned credit risk managers.

Application of risk-based capital rules Regulators have not yet settled on the most appropriate application of risk-based capital rules for credit derivatives, and banks trying to use them to reduce credit risk may find that current regulatory interpretations serve as disincentives.5 Generally, the current rules do not require capital based upon economic risk. For example, capital rules neither differentiate between high- and low-quality assets nor do they recognize diversification efforts. Transactions that pose the same economic risk may involve quite different regulatory capital requirements. While the Basel Committee has made the review of capital requirements for credit derivatives a priority, the current uncertainty of the application of capital requirements has made it difficult for banks to measure fully the costs of hedging credit risk.6

Credit risk complacency and hedging costs The absence of material domestic loan losses in recent years, the current strength of the US economy, and competitive pressures have led not only to a slippage in underwriting standards but also in some cases to complacency regarding asset quality and the need to reduce credit concentrations. Figure 11.1 illustrates the ‘lumpy’ nature of credit losses on commercial credits over the past 15 years. It plots charge-offs of commercial and industrial loans as a percentage of such loans.

Figure 11.1 Charge-offs: all commercial banks. *99Q1 has been annualized. (Data source: Bank Call Reports)

Over the past few years, banks have experienced very small losses on commercial credits. However, it is also clear that when the economy weakens, credit losses can become a major concern. The threat of large losses, which can occur because of credit concentrations, has led many larger banks to attempt to measure their credit risks on a more quantitative, ‘portfolio’, basis. Until recently, credit spreads on lower-rated, non-investment grade credits had

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contracted sharply. Creditors believe lower credit spreads indicate reduced credit risk, and therefore less need to hedge. Even when economic considerations indicate a bank should hedge a credit exposure, creditors often choose not to buy credit protection when the hedge cost exceeds the return from carrying the exposure. In addition, competitive factors and a desire to maintain customer relationships often cause banks to originate credit (funded or unfunded) at returns that are lower than the cost of hedging such exposures in the derivatives market. Many banks continue to have a book value, as opposed to an economic value, focus.

Limited ability to hedge illiquid exposures Credit derivatives can effectively hedge credit exposures when an underlying borrower has publicly traded debt (loans or bonds) outstanding that can serve as a reference asset. However, most banks have virtually all their exposures to firms that do not have public debt outstanding. Because banks lend to a large number of firms without public debt, they currently find it difficult to use credit derivatives to hedge these illiquid exposures. As a practical matter, banks are able to hedge exposures only for their largest borrowers. Therefore, the potential benefits of credit derivatives largely remain at this time beyond the reach of community banks, where credit concentrations tend to be largest.

Legal and cultural issues Unlike most financial derivatives, credit derivative transactions require extensive legal review. Banks that engage in credit derivatives face a variety of legal issues, such as: 1 Interpreting the meaning of terms not clearly defined in contracts and confirmations when unanticipated situations arise 2 The capacity of counterparties to contract and 3 Risks that reviewing courts will not uphold contractual arrangements. Although contracts have become more standardized, market participants continue to report that transactions often require extensive legal review, and that many situations require negotiation and amendments to the standardized documents. Until recently, very few default swap contracts were triggered because of the relative absence of default events. The recent increase in defaults has led to more credit events, and protection sellers generally have met their obligations without threat of litigation. Nevertheless, because the possibility for litigation remains a significant concern, legal risks and costs associated with legal transactional review remain obstacles to greater participation and market growth. Cultural issues also have constrained the use of credit derivatives. The traditional separation within banks between the credit and treasury functions has made it difficult for many banks to evaluate credit derivatives as a strategic risk management tool. Credit officers in many institutions are skeptical that the use of a portfolio model, which attempts to identify risk concentrations, can lead to more effective risk/reward decision making. Many resist credit derivatives because of a negative view of derivatives generally. Over time, bank treasury and credit functions likely will become more integrated, with each function contributing its comparative advantages to more effective risk

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management decisions. As more banks use credit portfolio models and credit derivatives, credit portfolio management may become more ‘equity-like’. As portfolio managers buy and sell credit risk in a portfolio context, to increase diversification and to make the portfolio more efficient, however, banks increasingly may originate exposure without maintaining direct borrower relationships. As portfolio management evolves toward this model, banks will face significant cultural challenges. Most banks report at least some friction between credit portfolio managers and line lenders, particularly with respect to loan pricing. Credit portfolio managers face an important challenge. They will attempt to capture the diversification and efficiency benefits offered by the use of more quantitative techniques and credit derivatives. At the same time, these risk managers will try to avoid diminution in their qualitative understanding of portfolio risks, which less direct contact with obligors may imply.

What are credit derivatives? Credit derivatives permit the transfer of credit exposure between parties, in isolation from other forms of risk. Banks can use credit derivatives both to assume or reduce (hedge) credit risk. Market participants refer to credit hedgers as protection purchasers, and to providers of credit protection (i.e. the party who assumes credit risk) as protection sellers. There are a number of reasons market participants have found credit derivatives attractive. First, credit derivatives allow banks to customize the credit exposure desired, without having a direct relationship with a particular client, or that client having a current funding need. Consider a bank that would like to acquire a twoyear exposure to a company in the steel industry. The company has corporate debt outstanding, but its maturity exceeds two years. The bank can simply sell protection for two years, creating an exposure that does not exist in the cash market. However, the flexibility to customize credit terms also bears an associated cost. The credit derivative is less liquid than an originated, directly negotiated, cash market exposure. Additionally, a protection seller may use only publicly available information in determining whether to sell protection. In contrast, banks extending credit directly to a borrower typically have some access to the entity’s nonpublic financial information. Credit derivatives allow a bank to transfer credit risk without adversely impacting the customer relationship. The ability to sell the risk, but not the asset itself, allows banks to separate the origination and portfolio decisions. Credit derivatives therefore permit banks to hedge the concentrated credit exposures that large corporate relationships, or industry concentrations created because of market niches, can often present. For example, banks may hedge existing exposures in order to provide capacity to extend additional credit without breaching internal, in-house limits. There are three principal types of credit derivative products: credit default swaps, total return swaps, and credit-linked notes. A fourth product, credit spread options, is not a significant product in the US bank market.

Credit default swaps In a credit default swap (CDS), the protection seller, the provider of credit protection, receives a payment in return for the obligation to make a payment that is contingent on the occurrence of a credit event for a reference entity. The size of the payment

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reflects the decline in value of a reference asset issued by the reference entity. A credit event is normally a payment default, bankruptcy or insolvency, failure to pay, or receivership. It can also include a restructuring or a ratings downgrade. A reference asset can be a loan, security, or any asset upon which a ‘dealer price’ can be established. A dealer price is important because it allows both participants to a transaction to observe the degree of loss in a credit instrument. In the absence of a credit event, there is no obligation for the protection seller to make any payment, and the seller collects what amounts to an option premium. Credit hedgers will receive a payment only if a credit event occurs; they do not have any protection against market value declines of the reference asset that occur without a credit event. Figure 11.2 shows the obligations of the two parties in a CDS.

Figure 11.2 Credit default swap.

In the figure the protection buyer looks to reduce risk of exposure to XYZ. For example, it may have a portfolio model that indicates that the exposure contributes excessively to overall portfolio risk. It is important to understand, in a portfolio context, that the XYZ exposure may well be a high-quality asset. A concentration in any credit risky asset, regardless of quality, can pose unacceptable portfolio risk. Hedging such exposures may represent a prudent strategy to reduce aggregate portfolio risk. The protection seller, on the other hand, may find the XYZ exposure helpful in diversifying its own portfolio risks. Though each counterparty may have the same qualitative view of the credit, their own aggregate exposure profiles may dictate contrary actions. If a credit event occurs, the protection seller must pay an amount as provided in the underlying contract. There are two methods of settlement following a credit event: (1) cash settlement; and (2) physical delivery of the reference asset at par value. The reference asset typically represents a marketable obligation that participants in a credit derivatives contract can observe to determine the loss suffered in the event of default. For example, a default swap in which a bank hedges a loan exposure to a company may designate a corporate bond from that same entity as the reference asset. Upon default, the decline in value of the corporate bond should approximate

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the loss in the value of the loan, if the protection buyer has carefully selected the reference asset. Cash-settled transactions involve a credit event payment (CEP) from the protection seller to the protection buyer, and can work in two different ways. The terms of the contract may call for a fixed dollar amount (i.e. a ‘binary’ payment). For example, the contract may specify a credit event payment of 50% upon default; this figure is negotiated and may, or may not, correspond to the expected recovery amount on the asset. More commonly, however, a calculation agent determines the CEP. If the two parties do not agree with the CEP determined by the calculation agent, then a dealer poll determines the payment. The dealer poll is an auction process in which dealers ‘bid’ on the reference asset. Contract terms may call for five independent dealers to bid, over a three-day period, 14 days after the credit event. The average price that the dealers bid will reflect the market expectation of a recovery rate on the reference asset. The protection seller then pays par value less the recovery rate. This amount represents the estimate of loss on assuming exposure to the reference asset. In both cases, binary payment or dealer poll, the obligation is cash-settled because the protection seller pays cash to settle its obligation. In the second method of settlement, a physical settlement, the protection buyer may deliver the reference asset, or other asset specified in the contract, to the protection seller at par value. Since the buyer collects the par value for the defaulted asset, if it delivers its underlying exposure, it suffers no credit loss. CDSs allow the protection seller to gain exposure to a reference obligor, but absent a credit event, do not involve a funding requirement. In this respect, CDSs resemble and are economically similar to standby letters of credit, a traditional bank credit product. Credit default swaps may contain a materiality threshold. The purpose of this is to avoid credit event payments for technical defaults that do not have a significant market impact. They specify that the protection seller make a credit event payment to the protection buyer, if a credit event has occurred and the price of the reference asset has fallen by some specified amount. Thus, a payment is conditional upon a specified level of value impairment, as well as a default event. Given a default, a payment occurs only if the value change satisfies the threshold condition. A basket default swap is a special type of CDS. In a basket default swap, the protection seller receives a fee for agreeing to make a payment upon the occurrence of the first credit event to occur among several reference assets in a basket. The protection buyer, in contrast, secures protection against only the first default among the specified reference assets. Because the protection seller pays out on one default, of any of the names (i.e. reference obligors), a basket swap represents a more leveraged transaction than other credit derivatives, with correspondingly higher fees. Basket swaps represent complicated risk positions due to the necessity to understand the correlation of the assets in the basket. Because a protection seller can lose on only one name, it would prefer the names in the basket to be as highly correlated as possible. The greater the number of names in the basket and the lower the correlation among the names, the greater the likelihood that the protection seller will have to make a payment. The credit exposure in a CDS generally goes in one direction. Upon default, the protection buyer will receive a payment from, and thus is exposed to, the protection seller. The protection buyer in a CDS will suffer a default-related credit loss only if both the reference asset and the protection seller default simultaneously. A default

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by either party alone should not result in a credit loss. If the reference entity defaults, the protection seller must make a payment. If the protection seller defaults, but the reference asset does not, the protection purchaser has no payment due. In this event, however, the protection purchaser no longer has a credit hedge, and may incur higher costs to replace the protection if it still desires a hedge. The protection seller’s only exposure to the protection buyer is for periodic payments of the protection fee. Dealers in credit derivatives, who may have a large volume of transactions with other dealers, should monitor this ‘receivables’ exposure.

Total return swaps In a total return swap (TRS), the protection buyer (‘synthetic short’) pays out cash flow received on an asset, plus any capital appreciation earned on that asset. It receives a floating rate of interest (usually LIBOR plus a spread), plus any depreciation on the asset. The protection seller (‘synthetic long’) has the opposite profile; it receives cash flows on the reference asset, plus any appreciation. It pays any depreciation to the protection buying counterparty, plus a floating interest rate. This profile establishes a TRS as a synthetic sale of the underlying asset by the protection buyer and a synthetic purchase by the protection seller. Figure 11.3 illustrates TRS cash flows.

Figure 11.3 Total return swap.

TRSs enable banks to create synthetic long or short positions in assets. A long position in a TRS is economically equivalent to the financed purchase of the asset. However, the holder of a long position in a TRS (protection seller) does not actually purchase the asset. Instead, the protection seller realizes all the economic benefits of ownership of the bond, but uses the protection buyer’s balance sheet to fund that ‘purchase’. TRSs enable banks to take short positions in corporate credit more easily than is possible in the cash markets. It is difficult to sell short a corporate bond (i.e. sell a bond and hope to repurchase, subsequently, the same security at a lower price), because the seller must deliver a specific bond to the buyer. To create a synthetic short in a corporate exposure with a TRS, an investor agrees to pay total return on an issue and receive a floating rate, usually LIBOR (plus a spread) plus any depreciation on the asset. Investors have found TRSs an effective means of creating short positions in emerging market assets. A TRS offers more complete protection to a credit hedger than does a CDS, because a TRS provides protection for market value deterioration short of an outright default.

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A credit default swap, in contrast, provides the equivalent of catastrophic insurance; it pays out only upon the occurrence of a credit event, in which case the default swap terminates. A TRS may or may not terminate upon default of the reference asset. Most importantly, unlike the one-way credit exposure of a CDS, the credit exposure in a TRS goes both ways. A protection buyer assumes credit exposure of the protection seller when the reference asset depreciates; in this case, the protection seller must make a payment to the protection buyer. A protection seller assumes credit exposure of the protection buyer, who must pay any appreciation on the asset to the protection seller. A protection buyer will suffer a loss only if the value of the reference asset has declined and simultaneously the protection seller defaults. A protection seller can suffer a credit loss if the protection buyer defaults and the value of the reference asset has increased. In practice, banks that buy protection use CDSs to hedge credit relationships, particularly unfunded commitments, typically with the objective to reduce risk-based capital requirements. Banks that sell protection seek to earn the premiums, while taking credit risks they would take in the normal course of business. Banks typically use TRSs to provide financing to investment managers and securities dealers. TRSs thus often represent a means of extending secured credit rather than a credit hedging activity. In such cases, the protection seller ‘rents’ the protection buyer’s balance sheet. The seller receives the total return of the asset that the buyer holds on its balance sheet as collateral for the loan. The spread over LIBOR paid by the seller compensates the buyer for its funding and capital costs. Credit derivative dealers also use TRSs to create structured, and leveraged, investment products. As an example, the dealer acquires $100 in high-yield loans and then passes the risk through to a special-purpose vehicle (SPV) by paying the SPV the total return on a swap. The SPV then issues $20 in investor notes. The yield, and thus the risk, of the $100 portfolio of loans is thus concentrated into $20 in securities, permitting the securities to offer very high yields.7

Credit-linked Notes A credit-linked note (CLN) is a cash market-structured note with a credit derivative, typically a CDS, embedded in the structure. The investor in the CLN sells credit protection. Should the reference asset underlying the CLN default, the investor (i.e. protection seller) will suffer a credit loss. The CLN issuer is a protection buyer. Its obligation to repay the par value of the security at maturity is contingent upon the absence of a credit event on the underlying reference asset. Figure 11.4 shows the cash flows of a CLN with an embedded CDS. A bank can use the CLN as a funded solution to hedging a company’s credit risk because issuing the note provides cash to the issuing bank. It resembles a loan participation but, as with other credit derivatives, the loan remains on the bank’s books. The investor in the CLN has sold credit protection and will suffer a loss if XYZ defaults, as the issuer bank would redeem the CLN at less than par to compensate it for its credit loss. For example, a bank may issue a CLN embedded with a fixed payout (binary) default swap that provides for a payment to investors of 75 cents on the dollar in the event a designated reference asset (XYZ) defaults on a specified obligation. The bank might issue such a CLN if it wished to hedge a credit exposure to XYZ. As with other credit derivatives, however, a bank can take a short position if

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Figure 11.4 A credit-linked note.

it has no exposure to XYZ, but issues a CLN using XYZ as the reference asset. Like the structured notes of the early 1990s, CLNs provide a cash market alternative to investors unable to purchase off-balance sheet derivatives, most often due to legal restrictions. CLNs are frequently issued through special-purpose vehicles (SPV), which use the sale proceeds from the notes to buy collateral assets, e.g. Treasury securities or money market assets. In these transactions, the hedging institution purchases default protection from the SPV. The SPV pledges the assets as collateral to secure any payments due to the credit hedger on the credit default swap, through which the sponsor of the SPV hedges its credit risk on a particular obligor. Interest on the collateral assets, plus fees on the default swap paid to the SPV by the hedger, generate cash flow for investors. When issued through an SPV, the investor assumes credit risk of both the reference entity and the collateral. When issued directly, the investor assumes two-name credit risk; it is exposed to both the reference entity and the issuer. Credit hedgers may choose to issue a CLN, as opposed to executing a default swap, in order to reduce counterparty credit risk. As the CLN investor pays cash to the issuer, the protection buying issuer eliminates credit exposure to the protection seller that would occur in a CDS. Dealers may use CLNs to hedge exposures they acquire by writing protection on default swaps. For example, a dealer may write protection on a default swap, with XYZ as the reference entity, and collect 25 basis points. The dealer may be able to hedge that exposure by issuing a CLN, perhaps paying LIBORò10 basis points, that references XYZ. The dealer therefore originates the exposure in one market and hedges it in another, arbitraging the difference between the spreads in the two markets.

Credit spread options Credit spread options allow investors to trade or hedge changes in credit quality. With a credit spread option, a protection seller takes the risk that the spread on a reference asset breaches a specified level. The protection purchaser buys the right to sell a security if the reference obligor’s credit spread exceeds a given ‘strike’ level. For example, assume a bank has placed a loan yielding LIBOR plus 15 basis points

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in its trading account. The bank may purchase an option on the borrower’s spread to hedge against trading losses should the borrower’s credit deteriorate. The bank may purchase an option, with a strike spread of 30 basis points, allowing it to sell the asset should the borrower’s current market spread rise to 30 basis points (or more) over the floating rate over the next month. If the borrower’s spread rises to 50 basis points, the bank would sell the asset to its counterparty, at a price corresponding to LIBOR plus 30 basis points. While the bank is exposed to the first 15 basis point movement in the spread, it does have market value (and thus default) protection on the credit after absorbing the first 15 basis points of spread widening. The seller of the option might be motivated by the view that a spread of LIBOR plus 30 basis points is an attractive price for originating the credit exposure. Unlike other credit derivative products, the US market for credit spread options currently is not significant; most activity in this product is in Europe. Until recently, current market spreads had been so narrow in the USA that investors appeared reluctant to sell protection against widening. Moreover, for dealers, hedging exposure on credit spread options is difficult, because rebalancing costs can be very high. Table 11.2 summarizes some of the key points discussed for the four credit derivative products. Table 11.2 The four credit derivative products Credit coverage for protection buyer

Product

Market

Principal bank uses

Credit default swaps

OTC

Default only; no payment for MTM losses unless a ‘credit event’ occurs and exceeds a materiality threshold

Protection buyers seek to: (1) reduce regulatory capital requirements on high-grade exposures; (2) make portfolio adjustments; (3) hedge credit exposure. Protection sellers seek to book income or acquire targeted exposures

Total return swaps

OTC

Protection buyer has MTM coverage

(1) Alternative to secured lending, typically to highly leveraged investors; (2) used to passthrough risk on high yield loans in structured (leveraged) investment transactions

Credit-linked notes

Cash

Typically default only

Hedge exposures: (1) owned in the banking book; or (2) acquired by a dealer selling protection on a CDS

Credit spread options

OTC

MTM coverage beyond a ‘strike’ level

Infrequently used in the USA

Risks of credit derivatives When used properly, credit derivatives can help diversify credit risk, improve earnings, and lower the risk profile of an institution. Conversely, the improper use of credit derivatives, as in poor lending practices, can result in an imprudent credit risk profile. Credit derivatives expose participants to the familiar risks in commercial banking; i.e. credit, liquidity, price, legal (compliance), foreign exchange, strategic, and reputa-

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tion risks. This section highlights these risks and discusses risk management practices that can help to manage and control the risk profile effectively.

Credit Risk The most obvious risk credit derivatives participants face is credit risk. Credit risk is the risk to earnings or capital of an obligor’s failure to meet the terms of any contract with the bank or otherwise to perform as agreed. For both purchasers and sellers of protection, credit derivatives should be fully incorporated within credit risk management processes. Bank management should integrate credit derivative activity in their credit underwriting and administration policies, and their exposure measurement, limit setting, and risk rating/classification processes. They should also consider credit derivative activity in their assessment of the adequacy of the allowance for loan and lease losses (ALLL) and their evaluation of concentrations of credit. There are a number of credit risks for both sellers and buyers of credit protection, each of which raises separate risk management issues. For banks selling credit protection (i.e. buying risk), the primary source of credit risk is the reference asset or entity. Table 11.3 highlights the credit protection seller’s exposures in the three principal types of credit derivative products seen in the USA. Table 11.3 Credit protection seller – credit risks Product

Reference asset risk

Counterparty risk

Credit default swaps (CDS)

If a ‘credit event’ occurs, the seller is required to make a payment based on the reference asset’s fall in value. The seller has contingent exposure based on the performance of the reference asset. The seller may receive physical delivery of the reference asset

Minimal exposure. Exposure represents the amount of deferred payments (fees) due from counterparty (risk protection buyer)

Total return swaps (TRS)

If the value of the reference asset falls, the seller must make a payment equal to the value change. The seller ‘synthetically’ owns and is exposed to the performance of the reference asset

If the value of the reference asset increases, the seller bank will have a payment due from the counterparty. The seller is exposed to the counterparty for the amount of the payment

Credit linked notes (CLN)

If the reference asset defaults, the seller (i.e. the bond investor) will not collect par value on the bond

If the issuer defaults, the investor may not collect par value. The seller is exposed to both the reference asset and the issuer (i.e. ‘two-name risk’). Many CLNs are issued through trusts, which buy collateral assets to pledge to investors. This changes the investor’s risk from issuer nonperformance to the credit risk of the collateral

Note: In CLNs the seller of credit protection actually buys a cash market security from the buyer of credit protection.

As noted in Table 11.3, the protection seller’s credit exposure will vary depending on the type of credit derivative used. In a CDS, the seller makes a payment only if a predefined credit event occurs. When investors sell protection through total rate-of-

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return swaps (i.e. receive total return), they are exposed to deterioration of the reference asset and to their counterparty for the amount of any increases in value of the reference asset. In CLN transactions, the investor (seller of credit protection) is exposed to default of the reference asset. Directly issued CLNs (i.e. those not issued through a trust) expose the investor to both the reference asset and the issuer. When banks buy credit protection, they also are exposed to counterparty credit risk as in other derivative products. Table 11.4 highlights the credit protection buyer’s exposures in the three principal types of credit derivative products. Table 11.4 Credit protection buyer – credit risks Product

Reference asset risk

Counterparty risk

Credit default swaps (CDS)

The buyer has hedged default risk on reference asset exposure if it has an underlying exposure. The extent of hedge protection may vary depending on the terms of the contract. Mismatched maturities result in forward credit exposures. If the terms and conditions of the reference asset differ from the underlying exposure, the buyer assumes residual or ‘basis’ risk. A CDS provides default protection only; the buyer retains market risk short of default.

If a credit event occurs, the counterparty will owe the buyer an amount normally determined by the amount of decline in the reference asset’s value.

Total return The buyer has ‘synthetically’ sold the asset. If the swaps (TRS): asset value increases, the buyer owes on the TRS pay total return but is covered by owning the asset.

The buyer has credit exposure of the counterparty if the reference asset declines in value.

Credit-linked notes (CLN)

Not applicable. The protection buyer in these transactions receives cash in exchange for the securities.

The protection buyer has obtained cash by issuing CLNs. The buyer may assume basis risk, depending upon the terms of the CLN.

As noted in Table 11.4, the protection buyer’s credit exposure also varies depending on the type of credit derivative. In a CDS, the buyer will receive a payment from the seller of protection when a default event occurs. This payment normally will equal the value decline of the CDS reference asset. In some transactions, however, the parties fix the amount in advance (binary). Absent legal issues, or a fixed payment that is less than the loss on the underlying exposure, the protection buyer incurs a credit loss only if both the underlying borrower (reference asset) and the protection seller simultaneously default. In a CDS transaction with a cash settlement feature, the hedging bank (protection buyer) receives a payment upon default, but remains exposed to the original balancesheet obligation. Such a bank can assure itself of complete protection against this residual credit risk by physically delivering the asset to the credit protection seller upon occurrence of a credit event. The physical delivery form of settlement has become more popular as the market has evolved. Absent a credit event, the protection buyer has no coverage against market value deterioration. If the term of the credit protection is less than the maturity of the exposure, the hedging bank will again become exposed to the obligation when the credit derivative matures. In that case, the bank has a forward credit risk. In a TRS, the protection buyer is exposed to its counterparty, who must make a payment when the value of the reference asset declines. Absent legal issues, a buyer

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will not incur a credit loss on the reference asset unless both the reference asset declines in value and the protection seller defaults. A bank that purchases credit protection by issuing a credit-linked note receives cash and thus has no counterparty exposure; it has simply sold bonds. It may have residual credit exposure to the underlying borrower if the recovery rate as determined by a bidding process is different than the value at which it can sell the underlying exposure. This differential is called ‘basis risk’. Managing credit risk: underwriting and administration For banks selling credit protection (buying risk) through a credit derivative, management should complete a financial analysis of both reference obligor(s) and the counterparty (in both default swaps and TRSs), establish separate credit limits for each, and assign appropriate risk ratings. The analysis of the reference obligor should include the same level of scrutiny that a traditional commercial borrower would receive. Documentation in the credit file should support the purpose of the transaction and creditworthiness of the reference obligor. Documentation should be sufficient to support the reference obligor’s risk rating. It is especially important for banks to use rigorous due diligence procedures in originating credit exposure via credit derivatives. Banks should not allow the ease with which they can originate credit exposure in the capital markets via derivatives to lead to lax underwriting standards, or to assume exposures indirectly that they would not originate directly. For banks purchasing credit protection through a credit derivative, management should review the creditworthiness of the counterparty, establish a credit limit, and assign a risk rating. The credit analysis of the counterparty should be consistent with that conducted for other borrowers or trading counterparties. Management should continue to monitor the credit quality of the underlying credits hedged. Although the credit derivative may provide default protection, in many instances (e.g. contracts involving cash settlement) the bank will retain the underlying credit(s) after settlement or maturity of the credit derivative. In the event the credit quality deteriorates, as legal owner of the asset, management must take actions necessary to improve the credit. Banks should measure credit exposures arising from credit derivative transactions and aggregate with other credit exposures to reference entities and counterparties. These transactions can create highly customized exposures and the level of risk/ protection can vary significantly between transactions. Management should document and support their exposure measurement methodology and underlying assumptions. Managing basis risk The purchase of credit protection through credit derivatives may not completely eliminate the credit risk associated with holding a loan because the reference asset may not have the same terms and conditions as the balance sheet exposure. This residual exposure is known as basis risk. For example, upon a default, the reference asset (often a publicly traded bond) might lose 25% of its value, whereas the underlying loan could lose 30% of its value. Should the value of the loan decline more than that of the reference asset, the protection buyer will receive a smaller payment on the credit default swap (derivative) than it loses on the underlying loan (cash transaction). Bonds historically have tended to lose more value, in default situations, than loans. Therefore, a bank hedging a loan exposure using a bond as a

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reference asset could benefit from the basis risk. The cost of protection, however, should reflect the possibility of benefiting from this basis risk. More generally, unless all the terms of the credit derivative match those of the underlying exposure, some basis risk will exist, creating an exposure for the protection buyer. Credit hedgers should carefully evaluate the terms and conditions of protection agreements to ensure that the contract provides the protection desired, and that the hedger has identified sources of basis risk. Managing maturity mismatches A bank purchasing credit protection is exposed to credit risk if the maturity of the credit derivative is less than the term of the exposure. In such cases, the bank would face a forward credit exposure at the maturity of the derivative, as it would no longer have protection. Hedging banks should carefully assess their contract maturities to assure that they do not inadvertently create a maturity mismatch by ignoring material features of the loan. For example, if the loan has a 15-day grace period in which the borrower can cure a payment default, a formal default can not occur until 15 days after the loan maturity. A bank that has hedged the exposure only to the maturity date of the loan could find itself without protection if it failed to consider this grace period. In addition, many credit-hedging transactions do not cover the full term of the credit exposure. Banks often do not hedge to the maturity of the underlying exposure because of cost considerations, as well as the desire to avoid short positions that would occur if the underlying obligor paid off the bank’s exposure. In such cases, the bank would continue to have an obligation to make fee payments on the default swap, but it would no longer have an underlying exposure. Evaluating counterparty risk A protection buyer can suffer a credit loss on a default swap only if the underlying obligor and the protection seller simultaneously default, an event whose probability is technically referred to as their ‘joint probability of default’. To limit risk, credit-hedging institutions should carefully evaluate the correlation between the underlying obligor and the protection seller. Hedgers should seek protection seller counterparties that have the lowest possible default correlation with the underlying exposure. Low default correlations imply that if one party defaults, only a small chance exists that the second party would also default. For example, a bank seeking to hedge against the default of a private sector borrower in an emerging market ordinarily would not buy protection from a counterparty in that same emerging market. Since the two companies may have a high default correlation, a default by one would imply a strong likelihood of default by the other. In practice, some credit hedging banks often fail to incorporate into the cost of the hedge the additional risk posed by higher default correlations. The lowest nominal fee offered by a protection seller may not represent the most effective hedge, given default correlation concerns. Banks that hedge through counterparties that are highly correlated with the underlying exposure should do so only with the full knowledge of the risks involved, and after giving full consideration to valuing the correlation costs. Evaluating credit protection Determining the amount of protection provided by a credit derivative is subjective, as the terms of the contract will allow for varying degrees of loss protection. Manage-

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ment should complete a full analysis of the reference obligor(s), the counterparty, and the terms of the underlying credit derivative contract and document its assessment of the degree of protection. Table 11.5 highlights items to consider. Table 11.5 Evaluating credit protection Factor

Issues

Reference asset

Is the reference asset an effective hedge for the underlying asset(s)? Same legal entity? Same level of seniority in bankruptcy? Same currency?

Default triggers

Do the ‘triggers’ in credit derivative match the default deﬁnition in the underlying assets (i.e. the cross-default provisions)?

Maturity mismatches

Does the maturity of the credit derivative match the maturity of the underlying asset(s)? Does the underlying asset have a grace period that would require the protection period to equal the maturity plus the grace period to achieve an effective maturity match? If a maturity mismatch exists, does the protection period extend beyond ‘critical’ payment/rollover points in the borrower’s debt structure? (As the difference between the protection period and the underlying asset maturity increases, the protection provided by the credit derivative decreases.)

Counterparty

Is there a willingness and ability to pay? Is there a concentration of credit exposure with the counterparty?

Settlement issues

Can the protection buyer deliver the underlying asset at its option? Must the buyer obtain permission of the borrower to physically deliver the asset? Are there any restrictions that preclude physical delivery of the asset? If the asset is required to be cash settled, how does the contract establish the payment amount? Dealer poll? Fixed payment? When does the protection-buying bank receive the credit derivative payment? (The longer the payment is deferred, the less valuable the protection.)

Materiality thresholds

Are thresholds low enough to effectively transfer all risk or must the price fall so far that the bank effectively has a deeply subordinated (large ﬁrst loss) position in the credit? Is the contract legally enforceable? Is the contract fully documented? Are there disputes over contract terms (e.g. deﬁnition of restructuring)? What events constitute a restructuring? How is accrued interest treated?

Legal issues

Banks selling credit protection assume reference asset credit risk and must identify the potential for loss, they should risk rate the exposure based on the financial condition and resources of the reference obligor. Banks face a number of less obvious credit risks when using credit derivatives. They include leverage, speculation, and pricing risks. Managing leverage considerations Most credit derivatives, like financial derivatives, involve leverage. If a bank selling credit protection does not fully understand the leverage aspects of some credit derivative structures, it may fail to receive an appropriate level of compensation for the risks assumed. A fixed payout (or binary) default swap can embed leverage into a credit transaction. In an extreme case, the contract may call for a 100% payment from the protection

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seller to the protection buyer in the event of default. This amount is independent of the actual amount of loss the protection buyer (lender) may suffer on its underlying exposure. Fixed payout swaps can allow the protection buyer to ‘over-hedge’, and achieve a ‘short’ position in the credit. By contracting to receive a greater creditevent payment than its expected losses on its underlying transaction, the protection buyer actually benefits from a default. Protection sellers receive higher fees for assuming fixed payment obligations that exceed expected credit losses and should always evaluate and manage those exposures prudently. For a protection seller, a basket swap also represents an especially leveraged credit transaction, since it suffers a loss if any one of the basket names defaults. The greater the number of names, the greater the chance of default. The credit quality of the transaction will ordinarily be less than that of the lowest rated name. For example, a basket of 10 names, all rated ‘A’ by a national rating agency, may not qualify for an investment grade rating, especially if the names are not highly correlated. Banks can earn larger fees for providing such protection, but increasing the number of exposures increases the risk that they will have to make a payment to a counterparty. Conceptually, protection sellers in basket swaps assume credit exposure to the weakest credit in the basket. Simultaneously, they write an option to the protection buyer, allowing that party to substitute another name in the basket should it become weaker than the originally identified weakest credit. Protection buyers in such transactions may seek to capitalize upon a protection seller’s inability to quantify the true risk of a default basket. When assuming these kinds of credit exposures, protection-selling banks should carefully consider their risk tolerance, and determine whether the leverage of the transaction represents a prudent risk/reward opportunity. Speculation Credit derivatives allow banks, for the first time, to sell credit risk short. In a short sale, a speculator benefits from a decline in the price of an asset. Banks can short credit risk by purchasing default protection in a swap, paying the total return on a TRS, or issuing a CLN, in each case without having an underlying exposure to the reference asset. Any protection payments the bank receives under these derivatives would not offset a balance sheet exposure, because none exists. Short positions inherently represent trading transactions. For example, a bank may pay 25 basis points per year to buy protection on a company to which it has no exposure. If credit spreads widen, the bank could then sell protection at the new market level; e.g. 40 basis points. The bank would earn a net trading profit of 15 basis points. The use of short positions as a credit portfolio strategy raises concerns that banks may improperly speculate on credit risk. Such speculation could cause banks to lose the focus and discipline needed to manage traditional credit risk exposures. Credit policies should specifically address the institution’s willingness to implement short credit risk positions, and also specify appropriate controls over the activity. Pricing Credit trades at different spread levels in different product sectors. The spread in the corporate bond market may differ from the loan market, and each may differ from the spread available in the credit derivatives market. Indeed, credit derivatives allow

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institutions to arbitrage the different sectors of the credit market, allowing for a more complete market. With an asset swap, an investor can synthetically transform a fixed-rate security into a floating rate security, or vice versa. For example, if a corporate bond trades at a fixed yield of 6%, an investor can pay a fixed rate on an interest rate swap (and receive LIBOR), to create a synthetic floater. If the swap fixed rate is 5.80%, the floater yields LIBOR plus 20 basis points. The spread over LIBOR on the synthetic floater is often compared to the market price for a default swap as an indicator of value. If the fee on the default swap exceeds, in this example, 20 basis points, the default swap is ‘cheap’ to the asset swap, thereby representing value. While benchmark indicators are convenient, they do not consider all the factors a bank should evaluate when selling protection. For example, when comparing credit derivative and cash market pricing levels, banks should consider the liquidity disadvantage of credit derivatives, their higher legal risks, and the lower information quality generally available when compared to a direct credit relationship. Using asset swap levels to determine appropriate compensation for selling credit protection also considers the exposure in isolation, for it ignores how the new exposure impacts aggregate portfolio risk, a far more important consideration. The protection seller should consider whether the addition of the exposure increases the diversification of the protection seller’s portfolio, or exacerbates an existing concern about concentration. Depending on the impact of the additional credit exposure on its overall portfolio risk, a protection seller may find that the benchmark pricing guide; i.e. asset swaps, fails to provide sufficient reward for the incremental risk taken. Banks face this same issue when extending traditional credit directly to a borrower. The increasing desire to measure the portfolio impacts of credit decisions has led to the development of models to quantify how incremental exposures could impact aggregate portfolio risk.

Liquidity risk Market participants measure liquidity risk in two different ways. For dealers, liquidity refers to the spread between bid and offer prices. The narrower the spread, the greater the liquidity. For end-users and dealers, liquidity risk refers to an institution’s ability to meet its cash obligations as they come due. As an emerging derivative product, credit derivatives have higher bid/offer spreads than other derivatives, and therefore lower liquidity. The wider spreads available in credit derivatives offer dealers profit opportunities which have largely been competed away in financial derivatives. These larger profit opportunities in credit derivatives explain why a number of institutions currently are, or plan to become, dealers. Nevertheless, the credit derivatives market, like many cash credit instruments, has limited depth, creating exposure to liquidity risks. Dealers need access to markets to hedge their portfolio of exposures, especially in situations in which a counterparty that provides an offset for an existing position defaults. The counterparty’s default could suddenly give rise to an unhedged exposure which, because of poor liquidity, the dealer may not be able to offset in a cost-effective manner. Like financial derivatives, credit and market risks are interconnected; credit risks becomes market risk, and vice versa. Both dealers and end-users of credit derivatives should incorporate the impact of these scenarios into regular liquidity planning and monitoring systems. Cash flow

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projections should consider all significant sources and uses of cash and collateral. A contingency funding plan should address the impact of any early termination agreements or collateral/margin arrangements.

Price risk Price risk refers to the changes in earnings due to changes in the value of portfolios of financial instruments; it is therefore a critical risk for dealers. The absence of historical data on defaults, and on correlations between default events, complicates the precise measurement of price risk and makes the contingent exposures of credit derivatives more difficult to forecast and fully hedge than a financial derivatives book. As a result, many dealers try to match, or perfectly offset, transaction exposures. Other dealers seek a competitive advantage by not running a matched book. For example, they might hedge a total return swap with a default swap, or hedge a senior exposure with a junior exposure. A dealer could also hedge exposure on one company with a contract referencing another company in the same industry (i.e. a proxy hedge). As dealers manage their exposures more on a portfolio basis, significant basis and correlation risk issues can arise, underscoring the importance of stress testing the portfolio. Investors seeking exposure to emerging markets often acquire exposures denominated in currencies different from their own reporting currency. The goal in many of these transactions is to bet against currency movements implied by interest rate differentials. When investors do not hedge the currency exposure, they clearly assume foreign exchange risk. Other investors try to eliminate the currency risk and execute forward transactions. To offset correlation risk which can arise, an investor should seek counterparties on the forward foreign exchange transaction who are not strongly correlated with the emerging market whose currency risk the investor is trying to hedge.

Legal (compliance) risks Compliance risk is the risk to earnings or capital arising from violations, or nonconformance with, laws, rules, regulations, prescribed practices, or ethical standards. The risk also arises when laws or rules governing certain bank products or activities of the bank’s clients may be ambiguous or untested. Compliance risk exposes the institution to fines, civil money penalties, payment of damages, and the voiding of contracts. Compliance risk can lead to a diminished reputation, reduced franchise value, limited business opportunities, lessened expansion potential, and an inability to enforce contracts. Since credit derivatives are new and largely untested credit risk management products, legal risks associated with them can be high. To offset such risks, it is critical for each party to agree to all terms prior to execution of the contract. Discovering that contracts have not been signed, or key terms have not been clearly defined, can jeopardize the protection that a credit risk hedger believes it has obtained. Banks acting in this capacity should consult legal counsel as necessary to ensure credit derivative contracts are appropriately drafted and documented. The Russian default on GKO debt in 1998 underscores the importance of understanding the terms of the contract and its key definitions. Most default swap contracts in which investors purchased protection on Russian debt referenced external debt obligations, e.g. Eurobond debt. When Russia defaulted on its internal GKO obliga-

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tions, many protection purchasers were surprised to discover, after reviewing their contracts, that an internal default did not constitute a ‘credit event’. As of July 1999, Russia has continued to pay its Eurobond debt. Although investors in Russia’s Eurobonds suffered significant mark-to-market losses when Russia defaulted on its internal debt, protection purchasers could not collect on the default swap contracts. Credit hedgers must assess the circumstances under which they desire protection, and then negotiate the terms of the contract accordingly. Although no standardized format currently exists for all credit derivatives, transactions are normally completed with a detailed confirmation under an ISDA Master Agreement. These documents will generally include the following transaction information: Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω

trade date maturity date business day convention reference price key definitions (credit events, etc.) conditions to payment materiality requirements notice requirements dispute resolution mechanisms reps and warranties designed to reduce legal risk credit enhancement terms or reference to an ISDA master credit annex effective date identification of counterparties reference entity reference obligation(s) payment dates payout valuation method settlement method (physical or cash) payment details

Documentation should also address, as applicable, the rights to obtain financial information on the reference asset or counterparty, restructuring or merger of the reference asset, method by which recovery values are determined (and any fallback procedures if a dealer poll fails to establish a recovery value), rights in receivership or bankruptcy, recourse to the borrower, and early termination rights. Moral hazards To date, no clear legal precedent governs the number of possible moral hazards that may arise in credit derivatives. The following examples illustrate potentially troubling issues that could pose legal risks for banks entering into credit derivative transactions. Access to material, nonpublic information Based on their knowledge of material, nonpublic information, creditors may attempt to buy credit protection and unfairly transfer their risk to credit protection sellers. Most dealers acknowledge this risk, but see it as little different from that faced in loan and corporate bond trading. These dealers generally try to protect themselves against the risk of information asymmetries by exercising greater caution about

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intermediating protection as the rating of the reference asset declines. They also may want to consider requiring protection purchasers to retain a portion of the exposure when buying protection so that the risk hedger demonstrates a financial commitment in the asset. Bank dealers also should adopt strict guidelines when intermediating risk from their bank’s own credit portfolios, for they can ill afford for market participants to suspect that the bank is taking advantage of nonpublic information when sourcing credit from its own portfolio. Implementation of firewalls between the public and nonpublic sides of the institution is an essential control. When the underlying instrument in a credit derivatives transaction is a security (as defined in the federal securities laws), credit protection sellers may have recourse against counterparties that trade on inside information and fail to disclose that information to their counterparties. Such transactions generally are prohibited as a form of securities fraud. Should the underlying instrument in a credit derivatives transaction not be a security and a credit protection seller suspects that its counterparty possessed and traded on the basis of material, nonpublic information, the seller would have to base a claim for redress on state law antifraud statutes and common law. Inadequate credit administration The existence of credit protection may provide an incentive for protection purchasers to administer the underlying borrower relationship improperly. For example, consider technical covenant violations in a loan agreement a bank may ordinarily waive. A bank with credit protection may be tempted to enforce the covenants and declare a default so that the timing of the default occurs during the period covered by the credit protection. It is unclear whether the protection seller has a cause of action against such a bank by charging that it acted improperly to benefit from the credit derivative. Another potential problem could involve the definition of a default event, which typically includes a credit restructuring. A creditor that has purchased protection on an exposure can simply restructure the terms of a transaction, and through its actions alone, declare a credit event. Most contracts require a restructuring to involve a material adverse change for the holder of the debt, but the legal definition of a material adverse change is subject to judgment and interpretation. All participants in credit derivative transactions need to understand clearly the operative definition of restructuring. In practice, credit derivative transactions currently involve reference obligors with large amounts of debt outstanding, in which numerous banks participate as creditors. As a result, any one creditor’s ability to take an action that could provide it with a benefit because of credit derivative protection is limited, because other participant creditors would have to affirm the actions. As the market expands, however, and a greater number of transactions with a single lender occur, these issues will assume increasing importance. Protection sellers may consider demanding voting rights in such cases. Optionality Credit derivative contracts often provide options to the protection purchaser with respect to which instruments it can deliver upon a default event. For example, the purchaser may deliver any instrument that ranks pari passu with the reference asset. Though two instruments may rank pari passu, they may not have the same

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value upon default. For example, longer maturities may trade at lower dollar prices. Protection purchasers can thus create greater losses for protection sellers by exploiting the value of these options, and deliver, from among all potentially deliverable assets, the one that maximizes losses for the protection seller. Protection sellers must carefully assess the potential that the terms of the contract could provide uncompensated, yet valuable, options to their counterparties. This form of legal risk results when one party to the contract and its legal counsel have greater expertise in credit derivatives than the counterparty and its counsel. This is particularly likely to be the case in transactions between a dealer and an end-user, underscoring the importance of end-users transacting with reputable dealer counterparties. These issues highlight the critical need for participants in credit derivatives to involve competent legal counsel in transaction formulation, structure, and terms.

Reputation Risk Banks serving as dealers in credit derivatives face a number of reputation risks. For example, the use of leveraged credit derivative transactions, such as basket default swaps and binary swaps, raises significant risks if the counterparty does not have the requisite sophistication to evaluate a transaction properly. As with leveraged financial derivatives, banks should have policies that call for heightened internal supervisory review of such transactions. A mismatched maturity occurs when the maturity of the credit derivative is shorter than the maturity of the underlying exposure the protection buyer desires to hedge. Some observers have noted that protection sellers on mismatched maturity transactions can face an awkward situation when they recognize a credit event may occur shortly, triggering a payment obligation. The protection seller might evaluate whether short-term credit extended to the reference obligor may delay a default long enough to permit the credit derivative to mature. Thinly veiled attempts to avoid a payment obligation on a credit derivative could have adverse reputation consequences. The desire many dealers have to build credit derivatives volume, and thus distinguish themselves in the marketplace as a leader, can easily lead to transactions of questionable merit and/or which may be inappropriate for client counterparties. Reputation risks are very difficult to measure and thus are difficult to manage.

Strategic Risk Strategic risk is the risk to earnings or capital from poorly conceived business plans and/or weak implementation of strategic initiatives. Before achieving material participation in the credit derivatives market, management should assess the impact on the bank’s risk profile and ensure that adequate internal controls have been established for the conduct of all trading and end-user activities. For example, management should assess: Ω The adequacy of personnel expertise, risk management systems, and operational capacity to support the activity. Ω Whether credit derivative activity is consistent with the bank’s overall business strategy. Ω The level and type of credit derivative activity in which the bank plans to engage (e.g. dealer versus end-user).

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Ω The types and credit quality of underlying reference assets and counterparties. Ω The structures and maturities of transactions. Ω Whether the bank has completed a risk/return analysis and established performance benchmarks. Banks should consider the above issues as part of a new product approval process. The new product approval process should include approval from all relevant bank offices or departments such as risk control, operations, accounting, legal, audit, and line management. Depending on the magnitude of the new product or activity and its impact on the bank’s risk profile, senior management, and in some cases the board, should provide the final approval.

Regulatory capital issues The Basel Capital Accord generally does not recognize differences in the credit quality of bank assets for purposes of allocating risk-based capital requirements. Instead, the Accord’s risk weights consider the type of obligation, or its issuer. Under current capital rules, a ‘Aaa’ corporate loan and a ‘B’ rated corporate loan have the same risk weight, thereby requiring banks to allocate the same amount of regulatory capital for these instruments. This differentiation between regulatory capital requirements and the perceived economic risk of a transaction has caused some banks to engage in ‘regulatory capital arbitrage’ (RCA) strategies to reduce their risk-based capital requirements. Though these strategies can reduce regulatory capital allocations, they often do not materially reduce economic risks. To illustrate the incentives banks have to engage in RCA, Table 11.6 summarizes the Accord’s risk weights for on-balance sheet assets and credit commitments. Table 11.6 Risk-based-capital risk weights for on-balance-sheet assets and commitments Exposure Claims on US government; OECD central governments; credit commitments less than one year Claims on depository institutions incorporated in OECD countries; US government agency obligations First mortgages on residential properties; loans to builders for 1–4 family residential properties; credit commitments greater than one year All other private sector obligations

Risk weight 0% 20% 50% 100%

Risk weighted assets (RWA) are derived by assigning assets to one of the four categories above. For example, a $100 commitment has no risk-based capital requirement if it matures in less than one year, and a $4 capital charge ($100î50%î8%) if greater than one year. If a bank makes a $100 loan to a private sector borrower with a 100% risk weight, the capital requirement is $8 ($100î8%). Under current capital rules, a bank incurs five times the capital requirement for a ‘Aaa’ rated corporate exposure (100% risk weight) than it does for a sub-investment grade exposure to an OECD government (20% risk weight). Moreover, within the 100% risk weight category, regulatory capital requirements are independent of asset quality. A sub-investment grade exposure and an investment grade exposure require the same regulatory capital.

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The current rules provide a regulatory incentive for banks to acquire exposure to lower-rated borrowers, since the greater spreads available on such assets provide a greater return on regulatory capital. When adjusted for risk, however, and after providing the capital to support that risk, banks may not economically benefit from acquiring lower quality exposures. Similarly, because of the low risk of high-quality assets, risk-adjusted returns on these assets may be attractive. Consequently, returns on regulatory and ‘economic’ capital can appear very different. Transactions attractive under one approach may not be attractive under the other. Banks should develop capital allocation models to assign capital based upon economic risks incurred. Economic capital allocation models attempt to ensure that a bank has sufficient capital to support its true risk profile, as opposed to the necessarily simplistic Basel paradigm. Larger banks have implemented capital allocation models, and generally try to manage their business based upon the economic consequences of transactions. While such models generally measure risks more accurately than the Basel paradigm, banks implementing the models often assign an additional capital charge for transactions that incur regulatory capital charges which exceed capital requirements based upon measured economic risk. These additional charges reflect the reality of the cost imposed by higher regulatory capital requirements.

Credit Derivatives 8 Under current interpretations of the Basel Accord, a bank may substitute the risk weight of the protection-selling counterparty for the weight of its underlying exposure. To illustrate this treatment, consider a $50 million, one year bullet loan to XYZ, a high-quality borrower. The loan is subject to a 100% risk-weight, and the bank must allocate regulatory capital for this commitment of $4 million ($50 millionî100% î8%). If the bank earns a spread over its funding costs of 25 basis points, it will net $125 000 on the transaction ($50 millionî0.0025). The bank earns a 3.125% return on regulatory capital (125 000/4 000 000). Because of regulatory capital constraints, the bank may decide to purchase protection on the exposure, via a default swap costing 15 basis points, from an OECD bank. The bank now earns a net spread of 10 basis points, or $50 000 per year. However, it can substitute the risk weight of its counterparty, which is 20%, for that of XYZ, which is 100%. The regulatory capital for the transaction becomes $800 000 ($50 millionî20%î8%), and the return on regulatory capital doubles to 6.25% (50 000/800 000).9 The transaction clearly improves the return on regulatory capital. Because of the credit strength of the borrower, however, the bank in all likelihood does not attribute much economic capital to the exposure. The default swap premium may reduce the return on the loan by more than the economic capital declines by virtue of the enhanced credit position. Therefore, as noted earlier, a transaction that increases the return on regulatory capital may simultaneously reduce the return on economic capital. As discussed previously, many credit derivative transactions do not cover the full maturity of the underlying exposure. The international supervisory community has concerns about mismatches because of the forward capital call that results if banks reduced the risk weight during the protection period. Consequently, some countries do not permit banks to substitute the risk weight of the protection provider for that

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of the underlying exposure when a mismatch exists. Recognizing that a failure to provide capital relief can create disincentives to hedge credit risk, the mismatch issue remains an area of active deliberation within the Basel Committee.

Securitization In an effort to reduce their regulatory capital costs, banks also use various specialpurpose vehicles (SPVs) and securitization techniques to unbundle and repackage risks to achieve more favorable capital treatment. Initially, for corporate exposures, these securitizations have taken the form of collateralized loan obligations (CLOs). More recently, however, banks have explored ways to reduce the high costs of CLOs, and have begun to consider synthetic securitization structures. Asset securitization allows banks to sell their assets and use low-level recourse rules to reduce RWA. Figure 11.5 illustrates a simple asset securitization, in which the bank retains a $50 ‘first loss’ equity piece, and transfers the remaining risk to bond investors. The result of the securitization of commercial credit is to convert numerous individual loans and/or bonds into a single security. Under low-level recourse rules,10 the bank’s capital requirements cannot exceed the level of its risk, which in this case is $50. Therefore, the capital requirement falls from $80 ($1000î8%) to $50, or 37.5%.

Figure 11.5 CLO/BBO asset conversions.

The originating bank in a securitization such as a CLO retains the equity (first loss) piece. The size of this equity piece will vary; it will depend primarily on the quality and diversification of the underlying credits and the desired rating of the senior and junior securities. For example, to obtain the same credit rating, a CLO collateralized by a diversified portfolio of loans with strong credit ratings will require a smaller equity piece than a structure backed by lower quality assets that are more concentrated. The size of the equity piece typically will cover some multiple of the pool’s expected losses. Statistically, the equity piece absorbs, within a certain confidence interval, the entire amount of credit risk. Therefore, a CLO transfers only catastrophic credit risk.11 The retained equity piece bears the expected losses. In this sense, the bank has not changed its economic risks, even though it has reduced its capital requirements. To reduce the cost of CLO issues, banks recently have explored new, lower-cost, ‘synthetic’ vehicles using credit derivatives. The objective of the synthetic structures is to preserve the regulatory capital benefits provided by CLOs, while at the same time lowering funding costs. In these structures, a bank attempting to reduce capital requirements tries to eliminate selling the full amount of securities corresponding to

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the credit exposures. It seeks to avoid paying the credit spread on senior tranches that makes traditional CLOs so expensive. In synthetic transactions seen to date, the bank sponsor retains a first loss position, similar to a traditional CLO. The bank sponsor then creates a trust that sells securities against a small portion of the total exposure, in sharp contrast to the traditional CLO, for which the sponsor sells securities covering the entire pool. Sponsors typically have sold securities against approximately 8% of the underlying collateral pool, an amount that matches the Basel capital requirement for credit exposure. The bank sponsor purchases credit protection from an OECD bank to reduce the risk weight on the top piece to 20%, subject to maturity mismatch limitations imposed by national supervisors. The purpose of these transactions is to bring risk-based capital requirements more in line with the economic capital required to support the risks. Given that banks securitize their highest quality exposures, management should consider whether their institutions have an adequate amount of capital to cover the risks of the remaining, higher-risk, portfolio.

A portfolio approach to credit risk management Since the 1980s, banks have successfully applied modern portfolio theory (MPT) to market risk. Many banks are now using earnings at risk (EaR) and Value-at-Risk (VaR)12 models to manage their interest rate and market risk exposures. Unfortunately, however, even through credit risk remains the largest risk facing most banks, the practical application of MPT to credit risk has lagged. The slow development toward a portfolio approach for credit risk results from the following factors: Ω The traditional view of loans as hold-to-maturity assets. Ω The absence of tools enabling the efficient transfer of credit risk to investors while continuing to maintain bank customer relationships. Ω The lack of effective methodologies to measure portfolio credit risk. Ω Data problems. Banks recognize how credit concentrations can adversely impact financial performance. As a result, a number of sophisticated institutions are actively pursuing quantitative approaches to credit risk measurement. While data problems remain an obstacle, these industry practitioners are making significant progress toward developing tools that measure credit risk in a portfolio context. They are also using credit derivatives to transfer risk efficiently while preserving customer relationships. The combination of these two developments has precipitated vastly accelerated progress in managing credit risk in a portfolio context over the past several years.

Asset-by-asset approach Traditionally, banks have taken an asset-by-asset approach to credit risk management. While each bank’s method varies, in general this approach involves periodically evaluating the credit quality of loans and other credit exposures, applying a credit risk rating, and aggregating the results of this analysis to identify a portfolio’s expected losses. The foundation of the asset-by-asset approach is a sound loan review and internal

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credit risk rating system. A loan review and credit risk rating system enables management to identify changes in individual credits, or portfolio trends, in a timely manner. Based on the results of its problem loan identification, loan review, and credit risk rating system, management can make necessary modifications to portfolio strategies or increase the supervision of credits in a timely manner. Banks must determine the appropriate level of the Allowance for Loan and Lease Losses (ALLL) on a quarterly basis. On large problem credits, they assess ranges of expected losses based on their evaluation of a number of factors, such as economic conditions and collateral. On smaller problem credits and on ‘pass’ credits, banks commonly assess the default probability from historical migration analysis. Combining the results of the evaluation of individual large problem credits and historical migration analysis, banks estimate expected losses for the portfolio and determine provision requirements for the ALLL. Migration analysis techniques vary widely between banks, but generally track the loss experience on a fixed or rolling population of loans over a period of years. The purpose of the migration analysis is to determine, based on a bank’s experience over a historical analysis period, the likelihood that credits of a certain risk rating will transition to another risk rating. Table 11.7 illustrates a one-year historical migration matrix for publicly rated corporate bonds. Notice that significant differences in risk exist between the various credit risk rating grades. For example, the transition matrix in Table 11.7 indicates the one-year historical transition of an AAA-rated credit to default is 0.0%, while for a B-rated credit the one-year transition to default is 6.81%. The large differences in default probabilities between high and low grade credits, given a constant 8% capital requirement, has led banks to explore vehicles to reduce the capital cost of higher quality assets, as previously discussed. Table 11.7 Moody’s investor service: one-year transition matrix Initial rating Aaa Aa a Baa Ba B Caa

Aaa

Aa

A

Baa

Ba

B

Caa

Default

93.40 1.61 0.07 0.05 0.02 0.00 0.00

5.94 90.55 2.28 0.26 0.05 0.04 0.00

0.64 7.46 92.44 5.51 0.42 0.13 0.00

0.00 0.26 4.63 88.48 5.16 0.54 0.62

0.02 0.09 0.45 4.76 86.91 6.35 2.05

0.00 0.01 0.12 0.71 5.91 84.22 2.05

0.00 0.00 0.01 0.08 0.24 1.91 69.20

0.00 0.02 0.00 0.15 1.29 6.81 24.06

Source: Lea Carty, Moody’s Investor Service from CreditMetrics – Technical Document

Default probabilities do not, however, indicate loss severity; i.e. how much the bank will lose if a credit defaults. A credit may default, yet expose a bank to a minimal loss risk if the loan is well secured. On the other hand, a default might result in a complete loss. Therefore, banks currently use historical migration matrices with information on recovery rates in default situations to assess the expected loss potential in their portfolios.

Portfolio approach While the asset-by-asset approach is a critical component to managing credit risk, it does not provide a complete view of portfolio credit risk, where the term ‘risk’ refers

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to the possibility that actual losses exceed expected losses. Therefore, to gain greater insights into credit risk, banks increasingly look to complement the asset-by-asset approach with a quantitative portfolio review using a credit model. A primary problem with the asset-by-asset approach is that it does not identify or quantify the probability and severity of unexpected losses. Historical migration analysis and problem loan allocations are two different methods of measuring the same variable; i.e. expected losses. The ALLL absorbs expected losses. However, the nature of credit risk is that there is a small probability of very large losses. Figure 11.6 illustrates this fundamental difference between market and credit portfolios. Market risk returns follow a normal distribution, while credit risk returns exhibit a skewed distribution.

Figure 11.6 Comparison of distribution of credit returns and market returns. (Source: J. P. Morgan)

The practical consequence of these two return distributions is that, while the mean and variance fully describe (i.e. they define all the relevant characteristics of) the distributions of market returns, they do not fully describe the distribution of credit risk returns. For a normal distribution, one can say that the portfolio with the larger variance has greater risk. With a credit risk portfolio, a portfolio with a larger variance need not automatically have greater risk than one with a smaller variance, because the skewed distribution of credit returns does not allow the mean and variance to describe the distribution fully. Credit returns are skewed to the left and exhibit ‘fat tails’; i.e. a probability, albeit very small, of very large losses. While banks extending credit face a high probability of a small gain (payment of interest and return of principal), they face a very low probability of large losses. Depending upon risk tolerance, an investor may consider a credit portfolio with a larger variance less risky than one with a smaller variance, if the smaller variance portfolio has some probability of an unacceptably large loss. Credit risk, in a statistical sense, refers to deviations from expected losses, or unexpected losses. Capital covers unexpected losses, regardless of the source; therefore, the measurement of unexpected losses is an important concern.

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Figure 11.7 Probability of distribution of loss.

Figure 11.7 shows a small probability of very large losses. Banks hold capital to cover ‘unexpected’ loss scenarios consistent with their desired debt rating.13 While the probability of very large losses is small, such scenarios do occur, usually due to excessive credit concentrations, and can create significant problems in the banking system. Banks increasingly attempt to address the inability of the asset-by-asset approach to measure unexpected losses sufficiently by pursuing a ‘portfolio’ approach. One weakness with the asset-by-asset approach is that it has difficulty identifying and measuring concentration risk. Concentration risk refers to additional portfolio risk resulting from increased exposure to a borrower, or to a group of correlated borrowers. For example, the high correlation between energy and real estate prices precipitated a large number of failures of banks that had credit concentrations in those sectors in the mid-1980s. Traditionally, banks have relied upon arbitrary concentration limits to manage concentration risk. For example, banks often set limits on credit exposure to a given industry, or to a geographic area. A portfolio approach helps frame concentration risk in a quantitative context, by considering correlations. Even though two credit exposures may not come from the same industry, they could be highly correlated because of dependence upon common economic factors. An arbitrary industry limit may not be sufficient to protect a bank from unwarranted risk, given these correlations. A model can help portfolio managers set limits in a more risk-focused manner, allocate capital more effectively, and price credit consistent with the portfolio risks entailed.14 It is important to understand what diversification can and cannot do for a portfolio. The goal of diversification in a credit portfolio is to shorten the ‘tail’ of the loss distribution; i.e. to reduce the probability of large, unexpected, credit losses. Diversification cannot transform a portfolio of poor quality assets, with a high level of expected losses, into a higher quality portfolio. Diversification efforts can reduce the uncertainty of losses around the expectation (i.e. credit ‘risk’), but it cannot change the level of expected loss, which is a function of the quality of the constituent assets.

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A low-quality portfolio will have higher expected losses than a high quality portfolio. Because all credit risky assets have exposure to macro-economic conditions, it is impossible to diversify a portfolio completely. Diversification efforts focus on eliminating the issuer specific, or ‘unsystematic’, risk of the portfolio. Credit managers attempt to do this by spreading the risk out over a large number of obligors, with cross correlations as small as possible. Figure 11.8 illustrates how the goal of diversification is to reduce risk to its ‘systematic’ component; i.e. the risks that a manager cannot diversify away because of the dependence of the obligors on the macro economy.

Figure 11.8 Diversiﬁcation beneﬁts. (Source: CIBC World Markets)

While the portfolio management of credit risk has intuitive appeal, its implementation in practice is difficult because of the absence of historical data and measurement problems (e.g. correlations). Currently, while most large banks have initiatives to measure credit risk more quantitatively, few currently manage credit risk based upon the results of such measurements. Fundamental differences between credit and market risks make application of MPT problematic when applied to credit portfolios. Two important assumptions of portfolio credit risk models are: (1) the holding period or planning horizon over which losses are predicted (e.g. one year) and (2) how credit losses will be reported by the model. Models generally report either a default or market value distribution. If a model reports a default distribution, then the model would report no loss unless a default is predicted. If the model uses a market value distribution, then a decline in the market value of the asset would be reflected even if a default did not occur.15 To employ a portfolio model successfully the bank must have a reliable credit risk rating system. Within most credit risk models, the internal risk rating is a critical statistic for summarizing a facility’s probability of defaulting within the planning horizon. The models use the credit risk rating to predict the probability of default by comparing this data against: (1) publicly available historical default rates of similarly rated corporate bonds; (2) the bank’s own historical internal default data; and (3) default data experienced by other banks. A sufficiently stratified (‘granular’) credit risk rating system is also important to credit risk modeling. The more stratified the system, the more precise the model’s predictive capability can be. Moreover, greater granularity in risk ratings assists banks in risk-based pricing and can offer a competitive advantage. The objective of credit risk modeling is to identify exposures that create an

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unacceptable risk/reward profile, such as might arise from credit concentrations. Credit risk management seeks to reduce the unsystematic risk of a portfolio by diversifying risks. As banks gain greater confidence in their portfolio modeling capabilities, it is likely that credit derivatives will become a more significant vehicle to manage portfolio credit risk. While some banks currently use credit derivatives to hedge undesired exposures, much of that activity only involves a desire to reduce regulatory capital requirements, particularly for higher-grade corporate exposures that incur a high regulatory capital tax. But as credit derivatives are used to allow banks to customize their risk exposures, and separate the customer relationship and exposure functions, banks will increasingly find them helpful in applying MPT.

Overreliance on statistical models The asymmetric distribution of credit returns makes it more difficult to measure credit risk than market risk. While banks’ efforts to measure credit risk in a portfolio context can represent an improvement over existing measurement practices, portfolio managers must guard against over-reliance on model results. Portfolio models can complement, but not replace, the seasoned judgment that professional credit personnel provide. Model results depend heavily on the validity of assumptions. Banks must not become complacent as they increase their use of portfolio models, and cease looking critically at model assumptions. Because of their importance in model output, credit correlations in particular deserve close scrutiny. Risk managers must estimate credit correlations since they cannot observe them from historical data. Portfolio models use different approaches to estimating correlations, which can lead to very different estimated loss distributions for the same portfolio. Correlations are not only difficult to determine but can change significantly over time. In times of stress, correlations among assets increase, raising the portfolio’s risk profile because the systematic risk, which is undiversifiable, increases. Credit portfolio managers may believe they have constructed a diversified portfolio, with desired risk and return characteristics. However, changes in economic conditions may cause changes to default correlations. For example, when energy and Texas real estate prices became highly correlated, those correlation changes exposed banks to significant unanticipated losses. It remains to be seen whether portfolio models can identify changes in default correlation early enough to allow risk managers to take appropriate risk-reducing actions. In recent years, there have been widely publicized incidents in which inaccurate price risk measurement models have led to poor trading decisions and unanticipated losses. To identify potential weaknesses in their price risk models, most banks use a combination of independent validation, calibration, and backtesting. However, the same data limitations that make credit risk measurement difficult in the first place also make implementation of these important risk controls problematic. The absence of credit default data and the long planning horizon makes it difficult to determine, in a statistical sense, the accuracy of a credit risk model. Unlike market risk models, for which many data observations exist, and for which the holding period is usually only one day, credit risk models are based on infrequent default observations and a much longer holding period. Backtesting, in particular, is problematic and would involve an impractical number of years of analysis to reach statistically valid conclu-

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sions. In view of these problems, banks must use other means, such as assessing model coverage, verifying the accuracy of mathematical algorithms, and comparing the model against peer group models to determine its accuracy. Stress testing, in particular, is important because models use specified confidence intervals. The essence of risk management is to understand the exposures that lie outside a model’s confidence interval.

Future of credit risk management Credit risk management has two basic processes: transaction oversight and portfolio management. Through transaction oversight, banks make credit decisions on individual transactions. Transaction oversight addresses credit analysis, deal structuring, pricing, borrower limit setting, and account administration. Portfolio management, on the other hand, seeks to identify, measure, and control risks. It focuses on measuring a portfolio’s expected and unexpected losses, and making the portfolio more efficient. Figure 11.9 illustrates the efficient frontier, which represents those portfolios having the maximum return, for any given level of risk, or, for any given level of return, the minimum risk. For example, Portfolio A is inefficient because, given the level of risk it has taken, it should generate an expected return of E(REF ). However, its actual return is only E(RA).

Figure 11.9 The efﬁcient frontier.

Credit portfolio managers actively seek to move their portfolios to the efficient frontier. In practice, they find their portfolios lie inside the frontier. Such portfolios are ‘inefficient’ because there is some combination of the constituent assets that either would increase returns given risk constraints, or reduce risk given return requirements. Consequently, they seek to make portfolio adjustments that enable the portfolio to move closer toward the efficient frontier. Such adjustments include eliminating (or hedging) risk positions that do not, in a portfolio context, exhibit a satisfactory risk/reward trade-off, or changing the size (i.e. the weights) of the

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exposures. It is in this context that credit derivatives are useful, as they can allow banks to shed unwanted credit risk, or acquire more risk (without having to originate a loan) in an efficient manner. Not surprisingly, banks that have the most advanced portfolio modeling efforts tend to be the most active end-users of credit derivatives. As banks increasingly manage credit on a portfolio basis, one can expect credit portfolios to show more market-like characteristics; e.g. less direct borrower contact, fewer credit covenants, and less nonpublic information. The challenge for bank portfolio managers will be to obtain the benefits of diversification and use of more sophisticated risk management techniques, while preserving the positive aspects of more traditional credit management techniques.

Author’s note Kurt Wilhelm is a national bank examiner in the Treasury & Market Risk unit of the Office of the Comptroller of the Currency (OCC). The views expressed in this chapter are those of the author and do not necessarily reflect official positions of either the OCC or the US Department of the Treasury. The author acknowledges valuable contributions from Denise Dittrich, Donald Lamson, Ron Pasch and P. C. Venkatesh.

Notes 1

1997/1998 British Bankers Association Credit Derivatives Survey. Source: 1997/1998 British Bankers Association Credit Derivatives Survey. 3 For a discussion of the range of practice in the conceptual approaches to modeling credit risk, see the Basel Committee on Banking Supervision’s 21 April 1999 report. It discusses the choice of time horizon, the definition of credit loss, the various approaches to aggregating credits and measuring the connection between default events. 4 Banks may use VaR models to determine the capital requirements for market risk provided that such models/systems meet certain qualitative and quantitative standards. 5 Banks generally should not, however, base their business decisions solely on regulatory capital ramifications. If hedging credit risk makes economic sense, regulatory capital considerations should represent a secondary consideration. 6 On 3 June 1999, the Basel Committee issued a consultative paper proposing a new capital adequacy framework to replace the previous Capital Accord, issued in 1988. The proposal acknowledges that ‘the 1988 Accord does not provide the proper incentives for credit risk mitigation techniques’. Moreover, ‘the Accord’s structure may not have favoured the development of specific forms of credit risk mitigation by placing restrictions on both the type of hedges acceptable for achieving capital reduction and the amount of capital relief’. The Committee proposes to expand the scope for eligible collateral, guarantees, and onbalance sheet netting. 7 In this example, the dealer has exposure to credit losses exceeding the $20 cushion supplied by investors, and must implement procedures to closely monitor the value of the loans and take risk-reducing actions if losses approach $20. 8 This section describes capital requirements for end-users. Dealers use the market risk rule to determine capital requirements. Some institutions may achieve regulatory capital reduction by transferring loans from the banking book to the trading book, if they meet the quantitative and qualitative requirements of the market risk rule. 9 Note that this transaction has no impact on leverage capital (i.e. capital/assets ratio). A 2

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credit derivative hedge does not improve the leverage ratio because the asset remains on the books. 10 Low-level recourse rules are only applicable to US banks. 11 CLO transactions typically carry early amortization triggers that protect investors against rapid credit deterioration in the pool. Such triggers ensure that the security amortizes more quickly and, as a practical matter, shield investors from exposure to credit losses. 12 EAR and VaR represents an estimate of the maximum losses in a portfolio, over a specified horizon, with a given probability. One might say the VaR of a credit portfolio is $50 million over the next year, with 99% confidence, i.e. there is a 1% probability that losses will exceed $50 million in the next 12 months. 13 Banks do not hold capital against outcomes worse than required by their desired debt ratings, as measured by VaR. These scenarios are so extreme that a bank could not hold enough capital against them and compete effectively. 14 For a discussion of setting credit risk limits within a portfolio context see CreditMetricsTechnical document. 15 For an excellent discussion of credit risk modeling techniques, see Credit Risk Models at Major US Banking Institutions: Current State of the Art and Implications for Assessments of Capital Adequacy, Federal Reserve Systems Task Force on Internal Models, May 1998.

12

Operational risk MICHEL CROUHY, DAN GALAI and BOB MARK

Introduction Operational risk (OR) has not been a well-defined concept. It refers to various potential failures in the operation of the firm, unrelated to uncertainties with regard to the demand function for the products and services of the firm. These failures can stem from a computer breakdown, a bug in a major computer software, an error of a decision maker in special situations, etc. The academic literature generally relates operational risk to operational leverage (i.e. to the shape of the production cost function) and in particular to the relationship between fixed and variable cost. OR is a fuzzy concept since it is often hard to make a clear-cut distinction between OR and ‘normal’ uncertainties faced by the organization in its daily operations. For example, if a client failed to pay back a loan, is it then due to ‘normal’ credit risk, or to a human error of the loan officers that should have known better all the information concerning the client and should have declined to approve the loan? Usually all credit-related uncertainties are classified as part of business risk. However, if the loan officer approved a loan against the bank’s guidelines, and maybe he was even given a bribe, this will be classified as an OR. Therefore the management of a bank should first define what is included in OR. In other words, the typology of OR must be clearly articulated and codified. A key problem lies in quantifying operational risk. For example, how can one quantify the risk of a computer breakdown? The risk is a product of the probability and the cost of a computer breakdown. Often OR is in the form of discrete events that don’t occur frequently. Therefore, a computer breakdown today (e.g. a network related failure) is different in both probability and the size of the damage from a computer breakdown 10 years ago. How can we quantify the damage of a computer failure? What historical event can we use in order to make a rational assessment? The problems in assessing OR does not imply that they should be ignored and neglected. On the contrary, management should pay a lot of attention to understanding OR and its potential sources in the organization precisely because it is hard to quantify OR. Possible events or scenarios leading to OR should be analyzed. In the next section we define OR and discuss its typology. In some cases OR can be insured or hedged. For example, computer hardware problems can be insured or the bank can have a backup system. Given the price of insurance or the cost of hedging risks, a question arises concerning the economic rationale of removing the risks. There is the economic issue of assessing the potential loss against the certain insurance cost for each OR event. Regulators require a minimum amount of regulatory capital for price risk in the

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trading book (BIS 98) and credit risk in the banking book (BIS 88), but there are currently no formal capital requirements against operational risk. Nevertheless, the 1999 Basel conceptual paper on a comprehensive framework for arriving at the minimum required regulatory capital includes a requirement for capital to be allocated against operational risk. Previous chapters of the book are devoted to the challenges associated with capital allocation for credit and market risk. This chapter examines the challenges associated with the allocation of capital for OR. In this chapter we look at how to meet these present and future challenges by constructing a framework for operational risk control. After explaining what we think of as a key underlying rule – the control functions of a bank need to be carefully harmonized – we examine the typology of operational risk. We describe four key steps in implementing bank operational risk, and highlight some means of risk reduction. Finally, we look at how a bank can extract value from enhanced operational risk management by improving its capital attribution methodologies. Failure to identify an operational risk, or to defuse it in a timely manner, can translate into a huge loss. Most notoriously, the actions of a single trader at Barings Bank (who was able to take extremely risky positions in a market without authority or detection) led to losses ($1.5 billion) that brought about the liquidation of the bank. The Bank of England report on Barings revealed some lessons about operational risk. First, management teams have the duty to understand fully the businesses they manage. Second, responsibility for each business activity has to be clearly established and communicated. Third, relevant internal controls, including independent risk management, must be established for all business activities. Fourth, top management and the Audit Committee must ensure that significant weaknesses are resolved quickly. Looking to the future, banks are becoming aware that technology is a doubleedged sword. The increasing complexity of instruments and information systems increase the potential for operational risk. Unfamiliarity with instruments may lead to their misuse, and raise the chances of mispricing and wrong hedging; errors in data feeds may also distort the bank’s assessment of its risks. At the same time, advanced analytical techniques combined with sophisticated computer technology create new ways to add value to operational risk management. The British Bankers’ Association (BBA) and Coopers & Lybrand conducted a survey among the BBA’s members during February and March 1997. The results reflect the views of risk directors and managers and senior bank management in 45 of the BBA’s members (covering a broad spectrum of the banking industry in the UK). The survey gives a good picture of how banks are currently managing operational risk and how they are responding to it. Section I of the report indicated that many banks have some way to go to formalize their approach in terms of policies and generally accepted definitions. They pointed out that it is difficult for banks to manage operational risk on a consistent basis without an appropriate framework in place. Section II of the report indicated that experience shows that it is all too easy for different parts of a bank inadvertently to duplicate their efforts in tackling operational risk or for such risks to fall through gaps because no one has been made responsible for them. Section III of the report revealed that modeling operational risk generates the most interest of all operational risk topic areas. However, the survey results suggest that banks have not managed to progress very far in terms of arriving at generally accepted models for operations risk. The report emphasized that this may

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well be because they do not have the relevant data. The survey also revealed that data collection is an area that banks will be focusing on. Section IV revealed that more than 67% of banks thought that operational risk was as (or more) significant as either market or credit risk and that 24% of banks had experienced losses of more than £1 million in the last 3 years. Section VI revealed that the percentage of banks that use internal audit recommendations as the basis of their response to operational risk may appear high, but we suspect this is only in relation to operational risk identified by internal audit rather than all operational risks. Section VII revealed that almost half the banks were satisfied with their present approach to operational risk. However, the report pointed out that there is no complacency among the banks. Further, a majority of them expect to make changes in their approach in the next 2 years. For reasons that we discuss towards the end of the chapter, it is important that the financial industry develop a consistent approach to operational risk. We believe that our approach is in line with the findings of a recent working group of the Basel committee in autumn 1998 as well as with the 20 best-practice recommendations on derivative risk management put forward in the seminal Group of Thirty (G30) report in 1993 (see Appendix 1).

Typology of operational risks What is operational risk? Operational risk is the risk associated with operating the business. One can subdivide operational risk into two components: operational failure risk and operational strategic risk. Operational failure risk arises from the potential for failure in the course of operating the business. A firm uses people, process, and technology to achieve business plans, and any one of these factors may experience a failure of some kind. Accordingly, operational failure risk is the risk that exists within the business unit caused by the failure of people, process or technology. A certain level of the failures may be anticipated and should be built into the business plan. It is the unanticipated and therefore uncertain failures that give rise to risk. These failures can be expected to occur periodically, although both their impact and their frequency may be uncertain. The impact or the financial loss can be divided into the expected amount, the severe unexpected amount and the catastrophic unexpected amount. The firm may provide for the losses that arise from the expected component of these failures by charging revenues with a sufficient amount of reserve. The firm should set aside sufficient economic capital to cover the severe unexpected component. Operational strategic risk arises from environmental factors such as a new competitor that changes the business paradigm, a major political and regulatory regime change, earthquakes and other factors that are generally outside the control of the firm. It also arises from a major new strategic initiative, such as getting into a new line of business or redoing how current business is to be done in the future. All businesses also rely on people, processes and technology outside their business unit, and the same potential for failure exists there. This type of risk will be referred to as external dependencies. In summary, operational failure risk can arise due to the failure of people, process

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or technology and external dependencies (just as market risk can be due to unexpected changes in interest rates, foreign exchange rates, equity prices and commodity prices). In short, operational failure risk and operational strategic risk, as illustrated in Figure 12.1, are the two main categories of operational risks. They can also be defined as ‘internal’ and ‘external’ operational risks.

Figure 12.1 Two broad categories of operational risk.

This chapter focuses on operational failure risk, i.e. on the internal factors that can and should be controlled by management. However, one should observe that a failure to address a strategic risk issue could translate into an operational failure risk. For example, a change in the tax laws is an operational failure risk. Furthermore, from a business unit perspective it might be argued that external dependencies include support groups within the bank, such as information technology. In other words, the two types of operational risk are interrelated and tend to overlap.

Beginning to End Operational risk is often thought to be limited to losses that can occur in operations or processing centers (i.e. where transaction processing errors can occur). This type of operational risk, sometimes referred to as operations risk, is an important component but by no means all of the operational risks facing the firm. Operational risk can arise before, during and after a transaction is processed. Risks exist before processing, while the potential transaction is being designed, during negotiation with the client, regardless whether the negotiation is a lengthy structuring exercise or a routine electronic negotiation, and continues after the negotiation through various continual servicing of the original transaction. A complete picture of operational risk can only be obtained if the activity is analyzed from beginning to end. Take the example of a derivatives sales desk shown in Figure 12.2. Before a transaction can be negotiated several things have to be in place, and

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each exposes the firm to risk. First, sales may be highly dependent on a valued relationship between a particular sales person and the client. Second, sales are usually dependent on the highly specialized skills of the product designer to come up with both a structure and a price that the client finds more attractive than all the other competing offers. These expose the institution to key people risks. The risk arises from the uncertainty as to whether these key people continue to be available. In addition, do they have the capacity to deal with an increase in client needs or are they at full capacity dealing with too many clients to be able to handle increases in client needs? Also do the people have the capability to respond to evolving and perhaps more complex client needs? BEFORE Identify client need RISK: • key people in key roles, esp. for valued client

DURING Structure transaction RISK: • models risk • disclosure • appropriateness

AFTER Deliver product RISK: • limit monitoring • model risk • key person continuity

Figure 12.2 Where operational risk occurs.

The firm is exposed to several risks during the processing of the transaction. First, the sales person may either willingly or unwillingly not fully disclose the full range of the risk of the transaction to a client. This may be a particular high risk during periods of intense pressure to meet profit and therefore bonus targets for the desk. Related to this is the risk that the sales person persuades the client to engage in a transaction that is totally inappropriate for the client, exposing the firm to potential lawsuits and regulatory sanctions. This is an example of people risk. Second, the sales person may rely on sophisticated financial models to price the transaction, which creates what is commonly, called model risk. The risk arises because the model may be used outside its domain of applicability, or the wrong inputs may be used. Once the transaction is negotiated and a ticket is written, several errors may occur as the transaction is recorded in the various systems or reports. For example, an error may result in delayed settlement giving rise to late penalties, it may be misclassified in the risk reports, understating the exposure and lead to other transactions that would otherwise not have been performed. These are examples of process risk. The system which records the transaction may not be capable of handling the transaction or it may not have the capacity to handle such transactions, or it may not be available (i.e. it may be down). If any one of the steps is outsourced, such as phone transmission, then external dependency risk arises. The list of what can go wrong before, during, and after the transaction, is endless. However, each type of risk can be broadly captured as a people, a process, a technology risk, or an external dependency risk and in turn each can be analyzed in terms of capacity, capability or availability

Who manages operational risk? We believe that a partnership between business, infrastructure, internal audit and risk management is the key to success. How can this partnership be constituted? In

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particular, what is the nature of the relationship between operational risk managers and the bank audit function? The essentials of proper risk management require that (a) appropriate policies be in place that limit the amount of risk taken and (b) authority be provided to change the risk profile, to those who can take action, and (c) that timely and effective monitoring of the risk is in place. No one group can be responsible for setting policies, taking action, and monitoring the risk taken, for to do so would give rise to all sorts of conflict of interest Policy setting remains the responsibility of senior management, even though the development of those policies may be delegated, and submitted to the board of directors for approval (see Figure 12.3).

Figure 12.3 Managing operational risk.

The authority to take action rests with business management, who are responsible for controlling the amount of operational risk taken within their business. Business management often relies on expert areas such as information technology, operations, legal, etc. to supply it with services required to operate the business. These infrastructure and governance groups share with business management the responsibility for managing operational risk. The responsibility for the development of the methodology for measuring operational risk resides with risk management. Risk management also needs to make risks transparent through monitoring and reporting. Risk management should also portfolio manage the firm’s operational risk. Risk management can actively manage residual risk through using tools such as insurance. Portfolio management adds value by ensuring that operational risk is adequately capitalized as well as analyzed for operational risk concentration. Risk management is also responsible for providing a regular review of trends, and needs to ensure that proper operational risk reward analysis is performed in the review of existing business as well as before the introduction of new initiatives and products. In this regard risk management works very closely but is independent of the business infrastructure, and the other governance groups. Operational risk is often managed on an ad hoc basis. and banks can suffer from a lack of coordination among functions such as risk management, internal audit,

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and business management. Most often there are no common bank-wide policies, methodologies or infrastructure. As a result there is also often no consistent reporting on the extent of operational risk within the bank as a whole. Furthermore, most bank-wide capital attribution models rarely incorporate sophisticated measures of operational risk. Senior management needs to know if the delegated responsibilities are actually being followed and if the resulting processes are effective. Internal audit is charged with this responsibility. Audit determines the effectiveness and integrity of the controls that business management puts in place to keep risk within tolerable levels. At regular intervals the internal audit function needs to ensure that the operational risk management process has integrity, and is indeed being implemented along with the appropriate controls. In other words, auditors analyze the degree to which businesses are in compliance with the designated operational risk management process. They also offer an independent assessment of the underlying design of the operational risk management process. This includes examining the process surrounding the building of operational risk measurement models, the adequacy and reliability of the operations risk management systems and compliance with external regulatory guidelines, etc. Audit thus provides an overall assurance on the adequacy of operational risk management. A key audit objective is to evaluate the design and conceptual soundness of the operational value-at-risk (VaR) measure, including any methodologies associated with stress testing, and the reliability of the reporting framework. Audit should also evaluate the operational risks that affect all types of risk management information systems – whether they are used to assess market, credit or operational risk itself – such as the processes used for coding and implementation of the internal models. This includes examining controls concerning the capture of data about market positions, the accuracy and completeness of this data, as well as controls over the parameter estimation processes. Audit would typically also review the adequacy and effectiveness of the processes for monitoring risk. and the documentation relating to compliance with the qualitative/quantitative criteria outlined in any regulatory guidelines. Regulatory guidelines typically also call for auditors to examine the approval process for vetting risk management models and valuation models used by frontand back-office personnel (for reasons made clear in Appendix 2). Auditors also need to examine any significant change in the risk measurement process. Audit should verify the consistency, timeliness and reliability of data sources used to run internal models, including the independence of such data sources. A key role is to examine the accuracy and appropriateness of volatility and correlation assumptions as well as the accuracy of the valuation and risk transformation calculations. Finally, auditors should examine the verification of the model’s accuracy through an examination of the backtesting process.

The key to implementing bank-wide operational risk management In our experience, eight key elements (Figure 12.4) are necessary to successfully implement such a bank-wide operational risk management framework. They involve

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setting policy and establishing a common language of risk identification. One would need to construct business process maps as well as to build a best-practice measurement methodology One would also need to provide exposure management as well as to allocate a timely reporting capability Finally, one wouyld need to perform risk analysis (inclusive of stress testing) as well as to allocate economic capital. Let’s look at these in more detail.

Figure 12.4 Eight key elements to achieve best practice operational risk management.

1 Develop well-defined operational risk policies. This includes articulating explicitly the desired standards for risk measurement. One also needs to establish clear guidelines for practices that may contribute to a reduction of operational risks. For example, the bank needs to establish policies on model vetting, off-hour trading, off-premises trading, legal document vetting, etc. 2 Establish a common language of risk identification. For example, people risk would include a failure to deploy skilled staff. Process risk would include execution errors. Technology risk would include system failures, etc. 3 Develop business process maps of each business. For example, one should map the business process associated with the bank’s dealing with a broker so that it becomes transparent to management and auditors. One should create an ‘operational risk catalogue’ as illustrated in Table 12.1 which categorizes and defines the various operational risks arising from each organizational unit This includes analyzing the products and services that each organizational unit offers, and the action one needs to take to manage operational risk. This catalogue should be a tool to help with operational risk identification and assessment. Again, the catalogue should be based on common definitions and language (as Reference Appendix 3). 4 Develop a comprehensible set of operational risk metrics. Operational risk assessment is a complex process and needs to be performed on a firm-wide basis at regular intervals using standard metrics. In the early days, as illustrated in Figure 12.5, business and infrastructure groups performed their own self-assessment of operational risk. Today, self-assessment has been discredited – the self-assessment of operational risk at Barings Bank contributed to the build-up of market risk at that institution – and is no longer an acceptable approach. Sophisticated financial institutions are trying to develop objective measures of operational risk that build significantly more reliability into the quantification of operational risk. To this end, operational risk assessment needs to include a review of the likelihood of a particular operational risk occurring as well as the severity or magnitude of

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The Professional’s Handbook of Financial Risk Management Table 12.1 Types of operational failure risks 1 People risk:

Incompetency Fraud etc.

2 Process risk: A Model risk (see Appendix 2)

Mark-to-model error Model/methodology error etc. Execution error Product complexity Booking error Settlement error Documentation/contract risk etc. Exceeding limits Security risks Volume risk etc.

B Transaction risk

C Operational control risk

3 Technology risk:

5

6 7

8

System failure Programming error Information risk (see Appendix 4) Telecommunication failure etc.

the impact that the operational risk will have on business objectives. This is no easy task. It can be challenging to assess the probability of a computer failure (or of a programming bug in a valuation model) and to assign a potential loss to any such event. We will examine this challenge in more detail in the next section of this chapter. Decide how one will manage operational risk exposure and take appropriate action to hedge the risks. For example, a bank should address the economic question of the cost–benefit of insuring a given risk for those operational risks that can he insured. Decide on how one will report exposure. For example, an illustrative summary report for the Tokyo equity arbitrage business is shown in Table 12.2. Develop tools for risk analysis and procedures for when these tools should be deployed. For example, risk analysis is typically performed as part of a new product process, periodic business reviews, etc. Stress testing should be a standard part of the risk analyst process. The frequency of risk assessment should be a function of the degree to which operational risks are expected to change over time as businesses undertake new initiatives, or as business circumstances evolve. A bank should update its risk assessment more frequently (say, semiannually) following the initial assessment of operational risk. Further, one should reassess the operational risk whenever the operational risk profile changes significantly (e.g. implementation of a new system, entering a new service, etc). Develop techniques to translate the calculation of operational risk into a required amount of economic capital. Tools and procedures should be developed to enable one to make decisions about operational risk based on incorporating operational

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Figure 12.5 The process of implementing operational risk management.

risk capital into the risk reward analyses, as we discuss in more detail later in the chapter. Clear guiding principles for the operational risk process should be set to ensure that it provides an appropriate measure of operational risk across all business units throughout the bank. These principles are illustrated in Figure 12.6. Objectivity refers to the principle that operational risk should be measured using standard objective criteria. ‘Consistency’ refers to ensuring that similar operational risk profiles in different business units result in similar reported operational risks. Relevance refers to the idea that risk should be reported in a way that makes it easier to take action to address the operational risk. ‘Transparency’ refers to ensuring that all material operational risks are reported and assessed in a way that makes the risk transparent to senior managers. ‘Bank-wide’ refers to the principle that operational risk measures should be designed so that the results can be aggregated across the entire organization. Finally, ‘completeness’ refers to ensuring that all material operational risks are identified and captured.

A four-step measurement process for operational risk As pointed out earlier, one can assess the amount of operational risk in terms of the likelihood of operational failure (net of mitigating controls) and the severity of potential financial loss (given that a failure occurs). This suggests that one should measure operational risk using the four-step operational risk process illustrated in Figure 12.7. We discuss each step below.

Input (step 1) The first step in the operational risk measurement process is to gather the information needed to perform a complete assessment of all significant operational risks. A key

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The Professional’s Handbook of Financial Risk Management Table 12.2 Operational risk reporting worksheet The overall operational risk of the Tokyo Equity Arbitrage Trading desk is Low Risk proﬁle 1. People risk Incompetency Fraud

Low Low

2. Process risk A. Model risk Mark-to-model error Model/methodology error

Low Low

B. Transaction risk: Execution error Product complexity Booking error Settlement error Documentation/contract risk

Low Low Low Low Medium

C. Operational control risk Exceeding limits Security risk Volume risk

Low Low Low/medium

3. Technology risk System failure Programming error Information risk Telecommunication failure

Low Low Low Low

Total operational failure risk measurement Strategic risk Political risk Taxation risk Regulatory risk Total strategic risk measurement

Low Low Low Low/medium Low

source of this information is often the finished products of other groups. For example. a unit that supports a business group often publishes reports or documents that may provide an excellent starting point for the operational risk assessment. Relevant and useful reports (e.g. Table 12.3) include audit reports, regulatory reports, etc. The degree to which one can rely on existing documents for control assessment varies. For example, if one is relying on audit documents as an indication of the degree of control, then one needs to ask if the audit assessment is current and sufficient. Have there been any significant changes made since the last audit assessment? Did the audit scope include the area of operational risk that is of concern to the present risk assessment? Gaps in information are filled through discussion with the relevant managers. Information from primary sources needs to be validated, and updated as necessary. Particular attention should be paid to any changes in the business or operating environment since the information was first produced. Typically, sufficient reliable historical data is not available to project the likelihood

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Guiding Principles

Objectivity

Consistency

Relevance

Transparency

Bankwide

Completeness

Risk measured using standard criteria

Same risk profiles result in same reported

Reported risk is actionable

All material risks are reported

Risk can be aggregated across entire

All material risks are identified and captured

organization

Figure 12.6 Guiding principles for the operational risk measurement.

Figure 12.7 The operational risk measurement process.

or severity of operational losses with confidence. One often needs to rely on the expertise of business management. The centralized operational risk management group (ORMG) will need to validate any such self-assessment by a business unit in a disciplined way. Often this amounts to a ‘reasonableness’ check that makes use of historical information on operational losses within the business and within the industry as a whole. The time frame employed for all aspects of the assessment process is typically one year. The one-year time horizon is usually selected to align with the business planning cycle of the bank. Nevertheless, while some serious potential operational failures may not occur until after the one-year time horizon, they should be part of the current risk assessment. For example, in 1998 one may have had key employees

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The Professional’s Handbook of Financial Risk Management Table 12.3 Sources of information in the measurement process of operational risk – the input Assessment for: Likelihood of occurrence Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω

Severity

Audit reports Ω Management interviews Regulatory reports Ω Loss history Management reports Ω etc. Expert opinion BRP (Business Recovery Plan) Y2K (year 2000) reports Business plans Budget plans Operations plans etc.

Figure 12.8 Second step in the measurement process of operational risk: risk assessment framework. VH: very high, H: high, M: medium, L: low, VL: very low.

under contract working on the year 2000 problem – the risk that systems will fail on 1 January 2000. These personnel may be employed under contracts that terminate more than 12 months into the future. However, while the risk event may only occur beyond the end of the current one-year review period, current activity directed at mitigating the risk of that future potential failure should be reviewed for the likelihood of failure as part of the current risk assessment.

Risk assessment framework (step 2) The ‘input’ information gathered in step 1 needs to be analyzed and processed through the risk assessment framework sketched in Figure 12.8. The risk of unexpec-

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ted operational failure, as well as the adequacy of management processes and controls to manage this risk, needs to be identified and assessed. This assessment leads to a measure of the net operational risk, in terms of likelihood and severity. Risk categories We mentioned earlier that operational risk can be broken down into four headline risk categories (representing the risk of unexpected loss) due to operational failures in people, process and technology deployed within the business – collectively the internal dependencies and external dependencies. Internal dependencies should each be reviewed according to a common set of factors. Assume, for illustrative purposes, that the common set of factors consist of three key components of capacity, capability and availability. For example, if we examine operational risk arising from the people risk category then one can ask: Ω Does the business have enough people (capacity) to accomplish its business plan? Ω Do the people have the right skills (capability)? Ω Are the people going to be there when needed (availability)? External dependencies are also analyzed in terms of the specific type of external interaction. For example, one would look at clients (external to the bank, or an internal function that is external to the business unit under analysis). Net operational risk Operational risks should be evaluated net of risk mitigants. For example, if one has insurance to cover a potential fraud then one needs to adjust the degree of fraud risk by the amount of insurance. We expect over time that insurance products will play an increasingly larger role in the area of mitigating operational risk. Connectivity and interdependencies The headline risk categories cannot be viewed in isolation from one another. Figure 12.9 illustrates the idea that one needs to examine the degree of interconnected risk exposure across the headline operational risk categories in order to understand the full impact of any risk. For example, assume that a business unit is introducing a new computer technology. The implementation of that new technology may generate

Figure 12.9 Connectivity of operational risk exposure.

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a set of interconnected risks across people, process and technology. For example, have the people who are to work with the new technology been given sufficient training and support? All this suggests that the overall risk may be higher than that accounted for by each of the component risks considered individually. Change, complexity, complacency One should also examine the sources that drive the headline categories of operational risk. For example, one may view the drivers as falling broadly under the categories of change, complexity, and complacency. Change refers to such items as introducing new technology or new products, a merger or acquisition, or moving from internal supply to outsourcing, etc. Complexity refers to such items as complexity in products, process or technology. Complacency refers to ineffective management of the business, particularly in key operational risk areas such as fraud, unauthorized trading, privacy and confidentiality, payment and settlement, model use, etc. Figure 12.10 illustrates how these underlying sources of a risk connect to the headline operational risk categories.

Figure 12.10 Interconnection of operational risks.

Net likelihood assessment The likelihood that an operational failure may occur within the next year should be assessed (net of risk mitigants such as insurance) for each identified risk exposure and for each of the four headline risk categories (i.e. people, process and technology, and external dependencies). This assessment can be expressed as a rating along a

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five-point likelihood continuum from very low (VL) to very high (VH) as set out in Table 12.4. Table 12.4 Five-point likelihood continuum Likelihood that an operational failure will occur within the next year VL L M H VH

Very low (very unlikely to happen: less than 2%) Low (unlikely: 2-5%) Medium (may happen: 5–10%) High (likely to happen: 10–20%) Very high (very likely: greater than 20%)

Severity assessment Severity describes the potential loss to the bank given that an operational failure has occurred. Typically, this will be expressed as a range of dollars (e.g. $50–$100 million), as exact measurements will not usually be possible. Severity should be assessed for each identified risk exposure. As we mentioned above, in practice the operational risk management group is likely to rely on the expertise of business management to recommend appropriate severity amounts. Combining likelihood and severity into an overall operational risk assessment Operational risk measures are not exact in that there is usually no easy way to combine the individual likelihood of loss and severity assessments into an overall measure of operational risk within a business unit. To do so, the likelihood of loss would need to be expressed in numerical terms – e.g. a medium risk represents a 5–10% probability of occurrence. This cannot be accomplished without statistically significant historical data on operational losses. The financial industry for the moment measures operational risk using a combination of both quantitative and qualitative points of view. To be sure, one should strive to take a quantitative approach based on statistical data. However, where the data is unavailable or unreliable – and this is the case for many risk sources – a qualitative approach can be used to generate a risk rating. Neither approach on its own tells the whole story: the quantitative approach is often too rigid, while the qualitative approach is often too vague. The hybrid approach requires a numerical assignment of the amount at risk based on both quantitative and qualitative data. Ideally, one would also calculate the correlation between the various risk exposures and incorporate this into the overall measure of business or firm-wide risk. Given the difficulty of doing this, for the time being risk managers are more likely to simply aggregate individual seventies assessed for each operational risk exposure. Deﬁning cause and effect One should analyze cause and effect of an operational loss. For example, failure to have an independent group vet all mathematical models is a cause, and a loss event arising from using erroneous models is the effect (see Table 12.5). Loss or effect data is easier to collect than the causes of loss data. There may be many causes to one loss. The relationship can be highly subjective and the importance of each cause difficult to assess. Most banks start by collecting the losses and then try to fit the causes to them. The methodology is typically developed later, after

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The Professional’s Handbook of Financial Risk Management Table 12.5 Risk categories: causes, effects and source

Risk category

The cause The effect

People (human resource)

Ω Loss of key staff due to defection of key staff to competitor

Variance in revenues/proﬁts (e.g. cost of recruiting replacements, costs of training, disruption to existing staff)

Process

Ω Declining productivity as volume grows

Variance in process costs from predicted levels (excluding process malfunctions)

Technology

Ω Year 2000 upgrade expenditure Ω Application development

Variance in technology running costs from predicted levels

Source: Extracted from a table by Duncan Wilson, Risk Management Consulting, IBM Global Financial Markets

the data has been collected. One needs to develop a variety of empirical analyses to test the link between cause and effect. Sample risk assessment report What does this approach lead to when put into practice? Assume we have examined Business Unit A and have determined that the sources of operational risk are related to: Ω Ω Ω Ω Ω Ω

outsourcing privacy compliance fraud downsizing and the political environment.

The sample report, as illustrated in Table 12.6, shows that the business has an overall ‘low’ likelihood of operational loss within the next 12 months. Observe that the assessment has led to an overall estimate of the severity as ranging from $150 to $300 million. One typically could display for each business unit a graph showing the relationship between severity and likelihood across each operational risk type (see Appendix 4). The summary report typically contains details of the factors considered in making a likelihood assessment for each operational risk exposure (broken down by people, process, technology and external dependencies) given an operational failure.

Review and validation (step 3) What happens after such a report has been generated? First. the centralized operational risk management group (ORMG) reviews the assessment results with senior business unit management and key officers in order to finalize the proposed operational risk rating. Key officers include those with responsibility for the management and control of operational activities (such as internal audit, compliance, IT, human resources, etc.). Second, ORMG can present its recommended rating to an operational risk rating review committee – a process similar that followed by credit rating agencies

Operational risk

359 Table 12.6 Example of a risk assessment report for Business Unit A Likelihood of event (in 12 months)

Operational risk scenarios

Outsourcing Privacy Compliance Fraud Downsizing Political environment Overall assessment

Internal dependencies People

Process

Technology

L L L L I VL L

VL M VI L VL M M

VL VL VL VL VL VL VL

External dependencies

Overall assessment

Severity ($million)

M L VL VL L VL L

M L L L L L L

50–100 50–100 35–70 5–10 5–10 5–10 150–300

such as Standard & Poors. The operational risk committee comments on the ratings prior to publication. ORMG may clarify or amend its original assessment based on feedback from the committee. The perational risk committee reviews the individual risk assessments to ensure that the framework has been consistently applied across businesses. The committee should have representation from business management, audit, functional areas, and chaired by risk management. Risk management retains the right to veto.

Output (step 4) The final assessment of operational risk should be formally reported to business management, the centralized Raroc group, and the partners in corporate governance (such as internal audit. compliance, etc.). As illustrated in Figure 12.11, the output of the assessment process has two main uses. First, the assessment provides better operational risk information to management for use in improving risk management decisions. Second, the assessment improves the allocation of economic capital to better reflect the extent of operational risk being taken by a business unit (a topic we discuss in more detail below). Overall, operational risk assessment guides management action – for example, in deciding whether to purchase insurance to mitigate some of the risks.

Figure 12.11 Fourth step in the measurement process of operational risk: output.

The overall assessment of the likelihood of operational risk and severity of loss for a business unit can be plotted to provide relative information on operational risk exposures across the bank (or a segment of the bank) as shown in Figure 12.12 (see

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Figure 12.12 Summary risk reporting.

also Appendix 4). Of course, Figure 12.12 is a very simplified way of representing risk, but presenting a full probability distribution for many operational risks is too complex to be justified – and may even be misleading given the lack of historical evidence. In Figure 12.12, one can see very clearly that if a business unit falls in the upper right-hand quadrant then the business unit has a high likelihood of operational risk and a high severity of loss (if failure occurs). These units would be the focus of management’s attention. A business unit may address its operational risks in several ways. First, one can avoid the risk by withdrawing from a business activity. Second, one can transfer the risk to another party (e.g. through more insurance or outsourcing). Third, one can accept and manage the risk, say, through more effective management. Fourth, one can put appropriate fallback plans in place in order to reduce the impact should an operational failure occur. For example, management can ask several insurance companies to submit proposals for insuring key risks. Of course, not all operational risks are insurable, and in the case of those that are insurable the required premium may be prohibitive.

Capital attribution for operational risks One should make sure that businesses that take on operational risk incur a transparent capital charge. The methodology for translating operational risk into capital is typically developed by the Raroc group in partnership with the operational risk management group. Operational risks can be divided into those losses that are expected and those that are unexpected. Management, in the ordinary course of business, knows that certain operational activities will fail. There will be a ‘normal’ amount of operational loss that the business is willing to absorb as a cost of doing business (such as error correction, fraud, etc.). These failures are explicitly or implicitly budgeted for in the annual business plan and are covered by the pricing of the product or service. The focus of this chapter, as illustrated in Figure 12.13, has been on unexpected

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U n expected

E xpe cted

C atastro ph ic

Likeliho od o f L oss

Severe

S everity of Loss Figure 12.13 Distribution of operational losses.

failures, and the associated amount of economic capital that should be attributed to business units to absorb the losses related to the unexpected operational failures. However, as the figure suggests, unexpected failures can themselves be further subdivided: Ω Severe but not catastrophic losses. Unexpected severe operational failures, as illustrated in Table 12.7, should be covered by an appropriate allocation of operational risk capital These kinds of losses will tend to be covered by the measurement processes described in the sections above. Table 12.7 Distribution of operational losses

Operational losses Covered by

Expected event (high probability, low losses) Business plan

Unexpected event (low probability, high losses) Severe ﬁnancial impact

Catastrophic ﬁnancial impact

Operational risk capital

Insurable (risk transfer) or ‘risk ﬁnancing’

Ω Catastrophic losses. These are the most extreme but also the rarest forms of operational risk events – the kind that might destroy the bank entirely. Value-at-Risk (VaR) and Raroc models are not meant to capture catastrophic risk, since potential losses are calculated up to a certain confidence level and catastrophic risks are by their very nature extremely rare. Banks will attempt to find insurance coverage to hedge catastrophic risk since capital will not protect a bank from these risks. Although VaR/Raroc models may not capture catastrophic loss, banks can use these approaches to assist their thought process about insurance. For example, it might be argued that one should retain the risk if the cost of capital to support the asset is less than the cost of insuring it. This sort of risk/reward approach can bring discipline to an insurance program that has evolved over time into a rather ad hoc

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set of policies – often where one type of risk is insured while another is not, with very little underlying rationale. Banks have now begun to develop databases of historical operational risk events in an effort to quantify unexpected risks of various types. They are hoping to use the databases to develop statistically defined ‘worst case’ estimates that may be applicable to a select subset of a bank’s businesses – in the same way that many banks already use historical loss data to drive credit risk measurement. A bank’s internal loss database will most likely be extremely small relative to the major losses in certain other banks. Hence, the database should also reflect the experience of others. Blending internal and external data requires a heavy dose of management judgement. This is a new and evolving area of risk measurement. Some banks are moving to an integrated or concentric approach to the ‘financing’ of operational risks. This financing can be achieved via a combination of external insurance programs (e.g. with floors and caps), capital market tools and self-insurance. If the risk is self-insured, then the risk should be allocated economic capital. How will the increasing emphasis on operational risk and changes in the financial sector affect the overall capital attributions in banking institutions? In the very broadest terms, we would guess that the typical capital attributions in banks now stand at around 20% for operational risk, 10% for market risk, and 70% for credit risk (Figure 12.14). We would expect that both operational risk and market risk might evolve in the future to around 30% each – although, of course, much depends on the nature of the institution. The likely growth in the weighting of operational risk can be attributed to the growing risks associated with people, process, technology and external dependencies. For example, it seems inevitable that financial institutions will experience higher worker mobility, growing product sophistication, increases in business volume, rapid introduction of new technology and increased merger/ acquisitions activity – all of which generate operational risk.

Figure 12.14 Capital attribution: present and future.

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Self-assessment versus risk management assessment Some would argue that the enormity of the operational risk task implies that the only way to achieve success in terms of managing operational risk without creating an army of risk managers is to have business management self-assess the risks. However, this approach is not likely to elicit the kind of necessary information to effectively control operational risk. It is unlikely that a Nick Leeson would have selfassessed his operational risk accurately. In idealized circumstances senior management aligns, through the use of appropriate incentives, the short- and perhaps long-term interest of the business manager with those of the corporation as a whole. If we assume this idealized alignment then business management is encouraged to share their view of both the opportunities and the risk with senior management. Self-assessment in this idealized environment perhaps would produce an accurate picture of the risk. However, a business manager in difficult situations (that is, when the risks are high) may view high risk as temporary and therefore may not always be motivated towards an accurate selfassessment. In other words, precisely when an accurate measurement of the operational risk would be most useful is when self-assessment would give the most inaccurate measurement. Risk management should do the gathering and processing of this data to ensure objectivity, consistency and transparency. So how is this to be done without the army of risk management personnel? First, as described earlier, a reasonable view of the operational risk can be constructed from the analysis of available information, business management interviews, etc. This can be accomplished over a reasonable timeframe with a small group of knowledgeable risk managers. Risk managers (who have been trained to look for risk and have been made accountable for obtaining an accurate view of the risk at a reasonable cost) must manage this trade-off between accuracy, granularity and timeliness. Second, risk managers must be in the flow of all relevant business management information. This can be accomplished by having risk managers sit in the various regular business management meetings, involved in the new product approval process, and be the regular recipient of selected management reports, etc. This is the same as how either a credit risk manager or a market risk manager keeps a timely and a current view of their respective risks. A second argument often used in favor of self-assessment is that an operational risk manager cannot possibly know as much about the business as the business manager, and therefore a risk assessment by a risk manager will be incomplete or inaccurate. This, however, confuses their respective roles and responsibilities. The business manager should know more about the business than the risk manager, otherwise that itself creates an operational risk and perhaps the risk manager should be running the business. The risk manager is trained in evaluating risk, much like a life insurance risk manager is trained to interpret the risk from a medical report and certain statistics. The risk manager is neither expected to be a medical expert nor even to be able to produce the medical report, only to interpret and extract risk information. This, by the way, is the same with a credit risk manager. A credit risk manager is expected to observe, analyze, interpret information about a company so as to evaluate the credit risk of a company, not be able to manage that company. To demand more from an operational risk manager would be to force that risk manager to lose focus and therefore reduce their value added. Operational risk can be

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mitigated by training personnel on how to use the tools associated with best practice risk management. (see Appendix 5.)

Integrated operational risk At present, most financial institutions have one set of rules to measure market risk, a second set of rules to measure credit risk, and are just beginning to develop a third set of rules to measure operational risk. It seems likely that the leading banks will work to integrate these methodologies (Figure 12.15). For example, they might attempt to first integrate market risk VaR and credit risk VaR and subsequently work to integrate an operational risk VaR measure.

Regulatory Capital

Economic Capital INTEGRATED FRAMEWORK

Market Risk VaR

Credit Risk VaR Operational Risk VaR Figure 12.15 Integrated risk models.

Developing an integrated risk measurement model will have important implications from both a risk transparency and a regulatory capital perspective. For example, if one simply added a market risk VaR plus an operational risk VaR plus a credit risk VaR to obtain a total VaR (rather than developing an integrated model) then one would overstate the amount of risk. The summing ignores the interaction or correlation between market risk, credit risk and operational risk. The Bank for International Settlement (1988) rules for capital adequacy are generally recognized to be quite flawed. We would expect that in time regulators will allow banks to use their own internal models to calculate a credit risk VaR to replace the BIS (1988) rules, in the same way that the BIS 1998 Accord allowed banks to adopt an internal models approach for determining the minimum required regulatory capital for trading market risk. For example, we would expect in the near term that BIS would allow banks to use their own internal risk-grading system for purposes of arriving at the minimum required regulatory capital for credit risk. The banking industry, rather than the regulators, sponsored the original market VaR methodology. (In particular, J. P. Morgan’s release of its RiskMetrics product.) Industry has also sponsored the new wave of credit VaR methodologies such as the

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J. P. Morgan CreditMetrics offering, and CreditRiskò from Credit Suisse Financial Products. Similarly, vendor-led credit VaR packages include a package developed by KMV (which is now in use at 60 financial institutions). The KMV model is based on an expanded version of the Merton model to allow for an empirically accurate approximation in lieu of a theoretically precise approach. All this suggests that, in time the banking industry will sponsor some form of operational risk VaR methodology. We can push the parallel a little further. The financial community, with the advent of products such as credit derivatives, is increasingly moving towards valuing loan products on a mark-to-model basis. Similarly, with the advent of insurance products we will see increased price discovery for operational risk. Moreover, just as we have seen an increasing trend toward applying market-risk-style quantification techniques to measure the credit VaR, we can also expect to see such techniques applied to develop an operational risk VaR. Accounting firms (such as Arthur Anderson) are encouraging the development of a common taxonomy of risk (see Appendix 6). Consulting firms (such as Net Risk) are facilitating access to operational risk data (see Appendix 7). A major challenge for banks is to produce comprehensible and practical approaches to operational risk that will prove acceptable to the regulatory community. Ideally, the integrated risk model of the future will align the regulatory capital approach to operational risk with the economic capital approach.

Conclusions An integrated goal-congruent risk management process that puts all the elements together, as illustrated in Figure 12.16, will open the door to optimal firm-wide management of risk. ‘Integrated’ refers to the need to avoid a fragmented approach to risk management – risk management is only as strong as the weakest link. ‘Goalcongruent’ refers to the need to ensure that policies and methodologies are consistent with each other. Infrastructure includes having the right people, operations technology and data to appropriately control risk. One goal is to have an ‘apple-to-apple’ risk measurement scheme so that one can compare risk across all products and aggregate risk at any level. The end product is a best-practice management of risk that is also consistent with business strategies. This is a ‘one firm, one view’ approach that also recognizes the complexity of each business within the firm. In this chapter we have stressed that operational risk should be managed as a partnership among business units, infrastructure groups, corporate governance units, internal audit and risk management. We should also mention the importance of establishing a risk-aware business culture. Senior managers play a critical role in establishing a corporate environment in which best-practice operational risk management can flourish. Personnel will ultimately behave in a manner dependent on how senior management rewards them. Indeed, arguably the key single challenge for senior management is to harmonize the behavior patterns of business units, infrastructure units, corporate governance units, internal audit and risk management and create an environment in which all sides ‘sink or swim’ together in terms of managing operational risk.

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Hardware

ion licat App ware Soft

Business Strategies

Technology

Risk Tolerance

Accurate Data

Operations

Best Practice Infrastructure

Independent First Class Active Risk Management • • • • • • •

People (Skills)

Best Practice Policies Authorities

Limits Management Risk Analysis Capital attribution Pricing Risk Portfolio Management Risk Education etc.

Disclosure

Best Practice Methodologies (Formulas)

RARO RA C

Pricing and Valuation

Market and Credit Risk

Operational Risk

Figure 12.16 Best practice risk management.

Appendix 1: Group of Thirty recommendations Derivatives and operational risk In 1993 the Group of Thirty (G30) provided 20 best-practice risk management recommendations for dealers and end-users of derivatives. These have proved seminal for many banks structuring their derivatives risk management functions, and here we offer a personal selection of some key findings for operational risk managers in institutions who may be less familiar with the report. The G30 working group was composed of a diverse cross-section of end-users, dealers, academics, accountants, and lawyers involved in derivatives. Input also came from a detailed survey of industry practice among 80 dealers and 72 end-users worldwide, involving both questionnaires and in-depth interviews. In addition, the G30 provides four recommendations for legislators, regulators, and supervisors. The G30 report noted that the credit, market and legal risks of derivatives capture

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most of the attention in public discussion. Nevertheless, the G30 emphasized that the successful implementation of systems operations, and controls is equally important for the management of derivatives activities. The G30 stressed that the complexity and diversity of derivatives activities make the measurement and control of those risks more difficult. This difficulty increases the importance of sophisticated risk management systems and sound management and operating practices These are vital to a firm’s ability to execute, record, and monitor derivatives transactions, and to provide the information needed by management to manage the risks associated with these activities. Similarly, the G30 report stressed the importance of hiring skilled professionals: Recommendation 16 states that one should ‘ensure that derivatives activities are undertaken by professionals in sufficient number and with the appropriate experience, skill levels, and degrees of specialization’. The G30 also stressed the importance of building best-practice systems. According to Recommendation 17, one should ‘ensure that adequate systems for data capture, processing, settlement, and management reporting are in place so that derivatives transactions are conducted in an orderly and efficient manner in compliance with management policies’. Furthermore, ‘one should have risk management systems that measure the risks incurred in their derivatives activities based on their nature, size and complexity’. Recommendation 19 emphasized that accounting practices should highlight the risks being taken. For example, the G30 pointed out that one ‘should account for derivatives transactions used to manage risks so as to achieve a consistency of income recognition treatment between those instruments and the risks being managed’.

People The survey of industry practices examined the involvement in the derivatives activity of people at all levels of the organization and indicated a need for further development of staff involved in back-office administration, accounts, and audit functions, etc. Respondents believed that a new breed of specialist, qualified operational staff, was required. It pointed out that dealers (large and small) and end-users face a common challenge of developing the right control culture for their derivatives activity. The survey highlighted the importance of the ability of people to work in cross functional teams. The survey pointed out that many issues require input from a number of disciplines (e.g. trading, legal and accounting) and demand an integrated approach.

Systems The survey confirmed the view that dealing in derivatives can demand integrated systems to ensure adequate information and operational control. It indicated that dealers were moving toward more integrated systems, between front- and back-office (across types of transactions). The industry has made a huge investment in systems, and almost all large dealers are extensive users of advanced technology. Many derivative groups have their own research and technology teams that develop the mathematical algorithms and systems necessary to price new transactions and to monitor their derivatives portfolios. Many dealers consider their ability to manage the development of systems capabilities an important source of competitive strength. For large dealers there is a requirement that one develop systems that minimize

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manual intervention as well as enhance operating efficiency and reliability, the volume of activity, customization of transactions, number of calculations to be performed, and overall complexity. Systems that integrate the various tasks to be performed for derivatives are complex Because of the rapid development of the business, even the most sophisticated dealers and users often rely on a variety of systems, which may be difficult to integrate in a satisfactory manner. While this situation is inevitable in many organizations, it is not ideal and requires careful monitoring to ensure sufficient consistency to allow reconciliation of results and aggregation of risks where required. The survey results indicated that the largest dealers, recognizing the control risks that separate systems pose and the expense of substantial daily reconciliations, are making extensive investments to integrate back-office systems for derivatives with front-office systems to derivatives as well as other management information.

Operations The role of the back-office is to perform a variety of functions in a timely fashion. This includes recording transactions, issuing and monitoring confirmations, ensuring legal documentation for transactions is completed, settling transactions, producing information for management and control purposes. This information includes reports of positions against trading and counterparty limits, reports on profitability, and reports on exceptions. There has been significant evolution in the competence of staff and the adequacy of procedures and systems in the back office. Derivatives businesses, like other credit or securities businesses, give the back-office the principal function of recording, documenting, and confirming the actions of the dealers. The wide range of volume and complexity that exists among dealers and end-users has led to a range of acceptable solutions The long timescales between the trade date and the settlement date, which is a feature of some products, means that errors not detected by the confirmation process may not be discovered for some time. While it is necessary to ensure that the systems are adequate for the organization’s volume and the complexity of derivatives activities, there can be no single prescriptive solution to the management challenges that derivatives pose to the back office. This reflects the diversity in activity between different market participants.

Controls Derivative activities, by their very nature, cross many boundaries of traditional financial activity. Therefore the control function must be necessarily broad, covering all aspects of activity. The primary element of control lies in the organization itself. Allocation of responsibilities for derivatives activities, with segregation of authority where appropriate, should be reflected in job descriptions and organization charts. Authority to commit the institution to transactions is normally defined by level or position. It is the role of management to ensure that the conduct of activity is consistent with delegated authority. There is no substitute for internal controls; however, dealers and end-users should communicate information that clearly indicates which individuals within the organization have the authority to make commitments. At the same time, all participants should fully recognize that the legal doctrine

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of ‘apparent authority’ may govern the transactions to which individuals within their organization commit. Definition of authority within an organization should also address issues of suitability of use of derivatives. End-users of derivatives transactions are usually institutional borrowers and investors and as such should possess the capability to understand and quantify risks inherent in their business. Institutional investors may also be buyers of structured securities exhibiting features of derivatives. While the exposures to derivatives will normally be similar to those on institutional balance sheets, it is possible that in some cases the complexity of such derivatives used might exceed the ability of an entity to understand fully the associated risks. The recommendations provide guidelines for management practice and give any firm considering the appropriate use of derivatives a useful framework for assessing suitability and developing policy consistent with its over-all risk management and capital policies. Organizational controls can then be established to ensure activities are consistent with a firm’s needs and objectives.

Audit The G30 pointed out that internal audit plays an important role in the procedures and control framework by providing an independent, internal assessment of the effectiveness of this framework. The principal challenge for management is to ensure that internal audit staff has sufficient expertise to carry out work in both the front and back office. Able individuals with the appropriate financial and systems skills are required to carry out the specialist aspects of the work. Considerable investment in training is needed to ensure that staff understand the nature and characteristics of the instruments being transacted and the models that are used to price them. Although not part of the formal control framework of the organization, external auditors and regulatory examiners provide a check on procedures and controls. They also face the challenge of developing and maintaining the appropriate degree of expertise in this area.

Appendix 2: Model risk Model risk relates to the risks involved in the erroneous use of models to value and hedge securities and is typically defined as a component of operational risk. It may seem to be insignificant for simple instruments (such as stocks and straight bonds) but can become a major operational risk for institutions that trade sophisticated OTC derivative products and execute complex arbitrage strategies. The market price is (on average) the best indicator of an asset’s value in liquid (and more or less efficient) securities markets. However, in the absence of such a price discovery mechanism, theoretical valuation models are required to ‘mark-to-model’ the position. In these circumstances the trader and the risk manager are like the pilot and co-pilot of a plane which flies under Instrument Flight Rules (IFR), relying only on sophisticated instruments to land the aircraft. An error in the electronics on board can be fatal to the plane.

Pace of model development The pace of model development over the past several years has accelerated to support the rapid growth of financial innovations such as caps, floors, swaptions, spread

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options and other exotic derivatives. These innovations were made possible by developments in financial theory that allow one to efficiently capture the many facets of financial risk. At the same time these models could never have been implemented on the trading floor had the growth in computing power not accelerated so dramatically. In March 1995, Alan Greenspan commented, ‘The technology that is available has increased substantially the potential for creating losses’. Financial innovations, model development and computing power are engaged in a sort of leapfrog, whereby financial innovations call for more model development, which in turn requires more computing power, which in turn results in more complex models. The more sophisticated the instrument, the larger the profit margin – and the greater the incentive to innovate. If the risk management function does not have the authority to approve (vet) new models, then this dynamic process can create significant operational risk. Models need to be used with caution. In many instances, too great a faith in models has led institutions to make unwitting bets on the key model parameters – such as volatilities or correlations – which are difficult to predict and often prove unstable over time. The difficulty of controlling model risk is further aggravated by errors in implementing the theoretical models, and by inexplicable differences between market prices and theoretical values. For example, we still have no satisfactory explanation as to why investors in convertible bonds do not exercise their conversion option in a way that is consistent with the predictions of models.

Different types of model risk Model risk, as illustrated in Figure 12A.1, has a number of sources: Ω Ω Ω Ω

The data input can be wrong One may wrongly estimate a key parameter of the model The model may be flawed or incorrect Models may give rise to significant hedging risk.

Figure 12A.1 Various levels of model risks.

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In fact, when most people talk about model risk they are referring to the risk of flawed or incorrect models. Modern traders often rely heavily on the use of mathematical models that involve complex equations and advanced mathematics. Flaws may be caused by mistakes in the setting of equations, or wrong assumptions may have been made about the underlying asset price process. For example, a model may be based on a flat and fixed term structure, while the actual term structure of interest rates is steep and unstable.

Appendix 3: Types of operational risk losses Operational risk is multifaceted. The type of loss can take many different forms such as damage to physical assets, unauthorized activity, unexpected taxation, etc. These various operational risk types need to be tightly defined. For example, an illustrative table of definitions such as illustrated in Table 12A.1 should be developed. This list is not meant to be exhaustive. It is critical that operational risk management groups are clear when they communicate with line management (in one direction) and senior managers (in the other).

Appendix 4: Operational risk assessment The process of operational risk assessment needs to include a review of the likelihood (or frequency) of a particular operational risk occurring as well as the magnitude (or severity) of the effect that the operational risk will have on the business. The assessment should include the options available to manage and take appropriate action to reduce operational risk. One should regularly publish graphs as shown in Figure 12A.2 displaying the relationship between the potential severity and frequency for each operational risk.

Appendix 5: Training and risk education One major source of operational risk is people – the human factor. Undoubtedly, operational risk due to people can be mitigated through better educated and trained staff. First-class risk education is a key component of any optimal firm-wide risk management program. Staff should be aware of why they may have to change the way they do things. Staff are more comfortable if they know new risk procedures exist for a good business reason. Staff need to clearly understand more than basic limit monitoring techniques (i.e. the lowest level of knowledge illustrated in Figure 12A.3). Managers need to be provided with the necessary training to understand the mathematics behind risk analysis. Business units, infrastructure units, corporate governance units and internal audit should also be educated on how risk can be used as the basis for allocating economic capital. Staff should also learn how to utilize measures of risk as a basis for pricing transactions. Finally, as illustrated in the upper-right corner of the figure, one should educate business managers and risk managers on how to utilize the risk measurement tools to enhance their portfolio management skills.

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The Professional’s Handbook of Financial Risk Management Table 12A.1 Illustrative deﬁnitions of operational risk loss

Nature of loss

Deﬁnition

Asset loss or damage

Ω Risk of either an uninsured or irrecoverable loss or damage to bank assets caused by ﬁre, ﬂooding, power supply, weather, natural disaster, physical accident, etc. Ω Risk of having to use bank assets to compensate clients for uninsured or irrecoverable loss or damage to client assets under bank custody Note: Excludes loss or damage due to either theft, fraud or malicious damage (see below for separate category) Ω Risk of projects or other initiatives costing more than budgeted Ω Risk of operational failure (failure of people, process, technology, external dependencies) resulting in credit losses Ω This is an internal failure unrelated to the creditworthiness of the borrower or guarantor e.g. inexperienced credit adjudicator assigns higher than appropriate risk rating – loan priced incorrectly and not monitored as it should be for the risk that it is, with greater risk of credit loss than should have been Ω Risk of losing current customers and being unable to attract new customers, with a consequent loss of revenue. This deﬁnition would include reputation risk as it applies to clients Ω Risk of having to make payments to settle disputes either through lawsuit or negotiated settlement (includes disputes with clients, employees, suppliers, competitors, etc.) Ω Risk of operational failure (failure of people, process, technology, external dependencies) resulting in market losses Ω This is an internal failure unrelated to market movements – e.g. incomplete or inaccurate data used in calculating VaR – true exposures not known and decisions made based on inaccurate VaR, with greater risk of market loss than should have been Ω Models used for risk measurement and valuation are wrong Ω Risk of inaccurate reporting of positions and results. If the true numbers were understood, then action could have been taken to stem losses or otherwise improve results. Risk of potential losses where model not programmed correctly or inappropriate or incorrect inputs to the model, or inappropriate use of model results, etc. Ω Risk of regulatory ﬁnes, penalties, client restitution payments or other ﬁnancial cost to be paid Ω Risk of regulatory sanctions (such as restricting or removal of one’s license, increased capital requirements, etc.) resulting in reduced ability to generate revenue or achieve targeted proﬁtability Ω Risk of incurring greater tax liabilities than anticipated Ω Risk of uninsured and irrecoverable loss of bank assets due to either theft, fraud or malicious damage. The loss may be caused by either internal or external persons. Ω Risk of having to use bank assets to compensate clients for either uninsured or irrecoverable loss of their assets under bank custody due to either theft, fraud or malicious damage Note: Excludes rogue trading (see below for separate category) Ω Risk of loss to the bank where unable to process transactions. This includes cost of correcting the problem which prevented transactions from being processed Ω Risk of a cost to the bank to correct errors made in processing transactions, or in failing to complete a transaction. This includes cost of making client whole for transactions which were processed incorrectly (e.g. restitution payments) Ω Risk of a loss of or Increased expenses as a result of unauthorized activity. For example, this includes the risk of trading loss caused by unauthorized or rogue trading activities

Cost management Credit losses due to operational failures

Customer satisfaction Disputes

Market losses due to operational failures

Model risk (see Appendix 2)

Regulatory/ compliance

Taxation Theft/fraud/ malicious damage

Transaction processing, errors and omissions

Unauthorized activity (e.g. rogue trading)

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Figure 12A.2 Severity versus frequency. A1–A10 are symbolic of 10 key risks.

Figure 12A.3 Increased operational risk knowledge required.

Appendix 6: Taxonomy for risk Arthur Anderson has developed a useful taxonomy for risk. Anderson divides risk into ‘environmental risk’, ‘process risk’, and ‘information for decision making risk’ (Figure 12A.4). These three broad categories of risk are further divided. For example, process risk is divided into operations risk, empowerment risk, information processing/technology risk, integrity risk and financial risk. Each of these risks are further subdivided. For example, financial risk is further subdivided into price, liquidity and credit risk.

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ENVIRONMENTAL RISK Competitor Catastrophic Loss

Sensitivity Sovereign /Political

Shareholder Relations

Legal

Regulatory

Capital Availability Financial Markets

Industry

PROCESS RISK EMPOWERMENT RISK

OPERATIONS RISK Customer Satisfaction Human Resources Product Development Efficiency Capacity Performance Gap Cycle Time Sourcing Obsolescence/ Shrinkage Compliance Business Interruption Product/Service Failure Environmental Health and Safety Trademark/ Brand Name Erosion

Leadership Authority/Limit Outsourcing Performance Incentives Change Readiness Communications

FINANCIAL RISK

Price

Interest Rate Currency Equity Commodity Financial Instrument

INFORMATION PROCESSING/ TECHNOLOGY RISK Relevance Integrity Access Availability Infrastructure INTEGRITY RISK

Liquidity

Credit

Cash Flow Opportunity Cost Concentration Default Concentration Settlement Collateral

Management Fraud Employee Fraud Illegal Acts Unauthorized Use Reputation

INFORMATION FOR DECISION MAKING RISK OPERATIONAL

FINANCIAL

STRATEGIC

Pricing Contract Commitment Performance Measurement Alignment Regulatory Reporting

Budget and Planning Accounting Information Financial Reporting Evaluation Taxation Pension Fund Investment Evaluation Regulatory Reporting

Environmental Scan Business Portfolio Valuation Performance Measurement Organization Structure Resource Allocation Planning Life Cycle

Figure 12A.4 A taxonomy for cataloguing risk. (Source: Arthur Anderson)

Appendix 7: Identifying and quantifying operational risk Consulting firms are providing value added operational risk services. For example, Net Risk has developed a tool which allows the user to identify operational risk causes and quantify them (Figure 12A.5). For example, the RiskOps product offered by Net Risk enables the user to utilize a ‘cause hierarchy’ to arrive at a pie chart of

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loss amount by cause as well as a frequency histogram of loss amounts. The RiskOps product also provides a description of specific losses. For example, as shown on the bottom of Figure 12A.5, RiskOps indicates that Prudential settled a class action suit for $2 million arising from improper sales techniques. Further, as shown in the middle of Figure 12A.6, one can see under the ‘personnel cause’ screen that Prudential had six different operational risk incidents ranging from firing an employee who reported improper sales practices to failure to supervise the operations of its retail CMO trading desk.

Figure 12A.5 RiskOpsTM identiﬁes operational risk causes and impacts and quantiﬁes them.

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Figure 12A.6.

13

Operational risk DUNCAN WILSON

Introduction The purpose of this chapter is initially to identify and explain the reasons why banks are focusing on operational risk management in relation to the following key issues: Ω Ω Ω Ω Ω Ω Ω

Why invest in operational risk management? Defining operational risk Measuring operational risk Technology risk What is best practice? Regulatory guidance Operational risk systems

The main objective of the chapter is to highlight the pros and cons of some of the alternative approaches taken by financial institutions to address the issues and to recommend the most practical route to take in addressing them.

Why invest in operational risk management? This section will explain the reason why operational risk has become such an important issue. Over the past five years there have been a series of financial losses in financial institutions which have caused them to rethink their approach to the management of operational risk. It has been argued that mainstream methods such as control self-assessment and internal audit have failed to provide management with the tools necessary to manage operational risk. It is useful to note The Economist’s comments in their 17 October 1998 issue on Long Term Capital Management which caused some banks to provide for over US$1billion each due to credit losses: The fund, it now appears, did not borrow more than a typical investment bank. Nor was it especially risky. What went wrong was the firm’s risk-management model – which is similar to those used by the best and brightest bank.

The Economist states further that ‘Regulators have criticized LTCM and banks for not stress-testing risk models against extreme market movements’. This confusing mixture of market risks, credit risks and liquidity risks is not assisted by many banks insistence to ‘silo’ the management of these three risks into different departments (market risk management, credit risk management and treasury). This silo mentality results in many banks arguing about who is to blame

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about the credit losses suffered because of their exposures to LTCM. Within LTCM itself the main risk appears to be operational according to The Economist: lack of stress-testing the risk models. This lack of a key control is exactly the issue being addressed by regulators in their thinking about operational risks. Although much of the recent focus has been on improving internal controls this is still associated with internal audit and control self-assessment. Perhaps internal controls are the best place to start in managing operational risk because of this emphasis. The Basel Committee in January 1998 published Internal Controls and this has been welcomed by most of the banking industry, as its objective was to try to provide a regulatory framework for regulators of banks. Many regulators are now reviewing and updating their own supervisory approaches to operational risk. Some banking associations such as the British Bankers Association have conducted surveys to assess what the industry consider to be sound practice. The benefits (and therefore the goals) of investing in an improved operational risk framework are: Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω

Avoidance of large unexpected losses Avoidance of a large number of small losses Improved operational efficiency Improved return on capital Reduced earnings volatility Better capital allocation Improved customer satisfaction Improved awareness of operational risk within management Better management of the knowledge and intellectual capital within the firm Assurance to senior management and the shareholders that risks are properly being addressed.

Avoidance of unexpected loss is one of the most common justifications of investing in operational risk management. Such losses are the high-impact low-frequency losses like those caused by rogue traders. In order to bring attention to senior management and better manage a firm’s exposure to such losses it is now becoming best practice to quantify the potential for such events. The difficulty and one of the greatest challenges for firms is to assess the magnitude and likelihood of a wide variety of such events. This has led some banks to investigate the more quantitative aspects of operational risk management. This will be addressed below. The regulators in different regions of the world have also started to scrutinize the approach of banks to operational risk management. The papers on Operational Risk and Internal Control from the Basel Committee on Banking Supervision are instructive and this new focus on operational risk implies that regulatory guidance or rules will have to be complied within the next year or two. This interest by the regulators and in the industry as a whole has caused many banks to worry about their current approach to operational risk. One of the first problems that banks are unfortunately encountering is in relation to the definition of this risk.

Deﬁning operational risk The problem of defining operational risk is perplexing financial institutions. Many banks have adopted the approach of listing categories of risk, analyzing what they

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are and deciding whether they should be reporting and controlling them as a separate risk ‘silo’ within their risk management framework as many of them have done for market and credit risk. It is also important to note that operational risk is not confined to financial institutions and useful examples of approaches to defining and measuring operational risk can be gained from the nuclear, oil, gas, construction and other industries. Not surprisingly, operational risk is already being managed locally within each business area with the support of functions such as legal, compliance and internal audit. It is at the group level where the confusion is taking place on defining operational risk. Therefore a good place to start is to internally survey ‘local’ practice within each business unit. Such surveys will invariably result in a risk subcategorisation of operational risk as follows: Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω Ω

Control risk Process risk Reputational risk Human resources risk Legal risk Takeover risk Marketing risk Systems outages Aging technology Tax changes Regulatory changes Business capacity Legal risk Project risk Security Supplier management

The above may be described in the following ways:

Control risk This is the risk that an unexpected loss occurs due to both the lack of an appropriate control or the effectiveness of an appropriate control and may be split into two main categories: Ω Inherent risk is the risk of a particular business activity, irrespective of related internal controls. Complex business areas only understood by a few key people contain higher inherent risk such as exotic derivatives trading. Ω Control risk is the risk that a financial loss or misstatement would not be prevented or detected and corrected on a timely basis by the internal control framework. Inherent risk and control risk are mixed together in many banks but it useful to make the distinction because it enables an operational risk manager to assess the degree of investment required in the internal control systems. This assessment of the relative operational risk of two different business may result in one being more inherently risky than the other and may require a higher level of internal control.

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Optimal control means that unexpected losses can happen but their frequency and severity are significantly reduced.

Process risk This is the risk that the business process is inefficient and causes unexpected losses. Process risk is closely related to internal control as internal control itself, according to COSO, should be seen as a process. It is differentiated from internal control clearly when a process is seen as a continuing activity such as risk management but the internal controls within the risk management process are depicted as ‘control points’.

Reputational risk This is the risk of an unexpected loss in share price or revenue due to the impact upon the reputation of the firm. Such a loss in reputation could occur due to mis-selling of derivatives for example. A good ‘control’ for or mitigating action for reputational risk is strong ethical values and integrity of the firm’s employees and a good public relations machine when things do go wrong.

Human resources risk Human resources risk is not just the activities of the human resources department although they do contribute to controlling the risk. However, there are particular conditions which reside within the control of the business areas themselves which the operational risk manager should be aware or when making an assessment. For example, the firm’s performance may hinge on a small number of key teams or people or the age of the teams may be skewed to young or old without an appropriate mix of skills and experience to satisfy the business objectives which have been set. Given the rogue trader problems which some banks have suffered it is also important that the operational risk manager checks that the human resources department has sufficient controls in relation to personnel security. Key items the manager should assess on personnel security are as follows: Hiring procedures: Ω references and work credentials Ω existing/ongoing security training and awareness program Ω job descriptions defining security roles and responsibilities Termination procedures: Ω the extent of the termination debriefing: reaffirm non-competition and nondisclosure agreements, Ω ensure revocation of physical access: cards, keys, system access authority, IDs, timescales Human Resources can help mitigate these risks by setting corporate standards and establishing an infrastructure such as ‘knowledge management’ databases and appropriate training and career progression. However, higher than average staff turnover or the ratio of temporary contractors to permanent staff is one indication that things are not working. Another indication of human resources risk is evidence of clashing management styles or poor morale.

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Legal risk Legal risk can be split into four areas: Ω The risk of suffering legal claims due to product liability or employee actions Ω The risk that a legal opinion on a matter of law turns out to be incorrect in a court of law. This latter risk is applicable to netting or new products such as credit derivatives where the enforceability of the agreements may not be proven in particular countries Ω Where the legal agreement covering the transaction is so complicated that the cash flows cannot be incorporated into the accounting or settlement systems of the company Ω Ability to enforce the decision in one jurisdiction in a different jurisdiction.

Takeover risk Takeover risk is highly strategic but can be controllable by making it uneconomic for a predator to take over the firm. This could be done by attaching ‘golden parachutes’ to the directors’ contracts which push up the price of the firm.

Marketing risk Marketing risk can occur in the following circumstances: Ω The benefits claimed about a products are misrepresented in the marketing material or Ω The product fails due to the wrong marketing strategy. Marketing risk is therefore at the heart of business strategy as are many of the risk subcategories.

Technology risk Systems risk in the wide definition will include all systems risks including external pressure such as the risk of not keeping up with the progress of changing technology when a company insists on developing risk management applications in-house. Technology risk is at the heart of a business such as investment banking.

Tax changes If tax changes occur, particularly retrospectively, they may make a business immediately unprofitable. A good example of this are changes in the deductibility of expenses such as depreciation of fixed assets. Normally the business should address the possibility of tax changes by making the customer pay. However, it normally comes down to a business decision of whether the firm or the customer takes the risk.

Regulatory changes Changes in regulations need to be monitored closely by firms. The effect on a business can be extremely important and the risk of volatility of returns high. A good example of this is the imminent changes in risk weighted asset percentages to be implemented.

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Business capacity If the processes, people and IT infrastructure cannot support a growing business the risks of major systems failure is high.

Project risk Project failure is one of the biggest causes for concern in most firms, particularly with the impact of some of the current project (year 2000 testing on the business).

Security The firms assets need to be secure from both internal and external theft. Such assets include not just the firm’s money or other securities/loans but also customer assets and the firm’s intellectual property.

Supplier management risk If your business is exposed to the performance of third parties you are exposed to this risk.

Natural catastrophe risk Natural catastrophes are one of the main causes of financial loss. The operational risk manager should asses whether the building is likely to be affected by: major landslide/mudslide, snow storm/blizzard, subsidence faulting, thunder/electrical storm, seasonal/local/tidal flooding, volcano, geomorphic erosion (landslip), or be located in an earthquake zone. Past history is normally used to assess such risks.

Man-made catastrophe risks There may also be man-made catastrophes such as those caused by activities inherently risky located nearby such as a prison, airport, transportation route (rail, road), chemical works, landfill site, nuclear plant, military base, defence plant, foreign embassy, petrol station, terrorist target, tube/rail station, exclusion zone. There may be other factors which need to be taken into account based on historical experience such as whether the area is likely to be affected by: epidemic, radioactive/ toxic contamination, gas, bomb threat, arson, act of war, political/union/religious/ activism, a high incidence of criminal activity. The questions above are similar to those that would be asked by any insurer of the buildings against the events described.

Other approaches Dr Jack King of Algorithmics has developed a general operational risk approach and proposes the following definition and criteria for operational risk: ‘Operational risk is the uncertainty of loss due to the failure in processing of the firms goods and services.’ For a full discussion of the rationale and consequences of adopting this definition, see his article in the January 1999 edition of the Algorithmics Research Quarterly. Peter Slater, Head of Operations Risk of Warburg Dillon Read, recently spoke at

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the IBC conference on Operational risk in London (December 1998). At that conference Peter explained that his bank split risks into the following categories: Ω Ω Ω Ω Ω Ω Ω Ω Ω

Credit and Settlement Market Operations Funding Legal IT Tax Physical and crime Compliance

He defined operations risk narrowly to be the risk of a unexpected losses due to deficiencies in internal controls or information systems caused by human error, s