Active Credit Portfolio Management in Practice E-Book

Jeffrey R. Bohn

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Finance Editions
• Sprache: Englisch

Seitenzahl: 1117

Veröffentlichungsjahr: 2009

Beschreibung

State-of-the-art techniques and tools needed to facilitate effective credit portfolio management and robust quantitative credit analysis

Filled with in-depth insights and expert advice, Active Credit Portfolio Management in Practice serves as a comprehensive introduction to both the theory and real-world practice of credit portfolio management. The authors have written a text that is technical enough both in terms of background and implementation to cover what practitioners and researchers need for actually applying these types of risk management tools in large organizations but which at the same time, avoids technical proofs in favor of real applications.  Throughout this book, readers will be introduced to the theoretical foundations of this discipline, and learn about structural, reduced-form, and econometric models successfully used in the market today. The book is full of hands-on examples and anecdotes. Theory is illustrated with practical application. The authors' Website provides additional software tools in the form of Excel spreadsheets, Matlab code and S-Plus code. Each section of the book concludes with review questions designed to spark further discussion and reflection on the concepts presented.

Leseprobe

Table of Contents

Title Page

Copyright Page

Dedication

Foreword

Preface

WHY ACTIVE CREDIT PORTFOLIO MANAGEMENT?

OBJECTIVE OF THIS BOOK

MODELS IN PRACTICE

BASEL II AND OTHER REGULATIONS

OUR APPROACH TO EXPLAINING IDEAS

HOW TO USE THIS BOOK

Acknowledgements

CHAPTER 1 - The Framework: Definitions and Concepts

WHAT IS CREDIT?

EVOLUTION OF CREDIT MARKETS

DEFINING RISK

A WORD ABOUT REGULATION

WHAT ARE CREDIT MODELS GOOD FOR?

ACTIVE CREDIT PORTFOLIO MANAGEMENT (ACPM)

FRAMEWORK AT 30,000 FEET

BUILDING BLOCKS OF PORTFOLIO RISK

USING PDs IN PRACTICE

VALUE, PRICE, AND SPREAD

DEFINING DEFAULT

PORTFOLIO PERFORMANCE METRICS

DATA AND DATA SYSTEMS

REVIEW QUESTIONS

CHAPTER 2 - ACPM in Practice

BANK VALUATION

ORGANIZING FINANCIAL INSTITUTIONS: DIVIDING INTO TWO BUSINESS LINES

EMPHASIS ON CREDIT RISK

MARKET TRENDS SUPPORTING ACPM

FINANCIAL INSTRUMENTS USED FOR HEDGING AND MANAGING RISK IN A CREDIT PORTFOLIO

MARK-TO-MARKET AND TRANSFER PRICING

METRICS FOR MANAGING A CREDIT PORTFOLIO

DATA AND MODELS

EVALUATING AN ACPM UNIT

MANAGING A RESEARCH TEAM

CONCLUSION

REVIEW QUESTIONS

EXERCISES

CHAPTER 3 - Structural Models

STRUCTURAL MODELS IN CONTEXT

A BASIC STRUCTURAL MODEL

BLACK-SCHOLES-MERTON

VALUATION

MODIFYING BSM

FIRST PASSAGE TIME: BLACK-COX

PRACTICAL IMPLEMENTATION: VASICEK-KEALHOFER

STOCHASTIC INTEREST RATES: LONGSTAFF-SCHWARTZ

JUMP-DIFFUSION MODELS: ZHOU

ENDOGENOUS DEFAULT BARRIER (TAXES AND BANKRUPTCY COSTS): LELAND-TOFT

CORPORATE TRANSACTION ANALYSIS

LIQUIDITY

OTHER STRUCTURAL APPROACHES

CONCLUSION

APPENDIX 3A: DERIVATION OF BLACK-SCHOLES-MERTON FRAMEWORK FOR CALCULATING ...

APPENDIX 3B: DERIVATION OF CONVERSION OF PHYSICAL PROBABILITY OF DEFAULT (PD) ...

REVIEW QUESTIONS

EXERCISES

CHAPTER 4 - Econometric Models

DISCRETE-CHOICE MODELS

EARLY DISCRETE-CHOICE MODELS: BEAVER (1966) AND ALTMAN (1968)

HAZARD RATE (DURATION) MODELS

EXAMPLE OF A HAZARD-RATE FRAMEWORK FOR PREDICTING DEFAULT: SHUMWAY (2001)

HAZARD RATES VERSUS DISCRETE CHOICE

PRACTICAL APPLICATIONS: FALKENSTEIN ET AL. (2000) AND DWYER AND STEIN (2004)

CALIBRATING ECONOMETRIC MODELS

CALIBRATING TO PDs

CALIBRATING TO RATINGS

INTERPRETING THE RELATIVE INFLUENCE OF FACTORS IN ECONOMETRIC MODELS

DATA ISSUES

TAXONOMY OF DATA WOES

BIASED SAMPLES CANNOT EASILY BE FIXED

CONCLUSION

APPENDIX 4A: SOME ALTERNATIVE DEFAULT MODEL SPECIFICATIONS

REVIEW QUESTIONS

EXERCISES

CHAPTER 5 - Loss Given Default

ROAD TO RECOVERY: THE TIMELINE OF DEFAULT RESOLUTION

MEASURES OF LGD (RECOVERY)

THE RELATIONSHIP BETWEEN MARKET PRICES AND ULTIMATE RECOVERY

APPROACHES TO MODELING LGD: THE LOSSCALC (2002, 2005) APPROACHES AND EXTENSIONS

CONCLUSION

REVIEW QUESTIONS

EXERCISES

CHAPTER 6 - Reduced-Form Models

REDUCED-FORM MODELS IN CONTEXT

BASIC INTENSITY MODELS

A BRIEF INTERLUDE TO DISCUSS VALUATION

DUFFIE, SINGLETON, LANDO (DSL) INTENSITY MODEL

CREDIT RATING TRANSITION MODELS

DEFAULT PROBABILITY DENSITY VERSION OF INTENSITY MODELS (HULL-WHITE)

GENERIC CREDIT CURVES

CONCLUSION

APPENDIX 6A: KALMAN FILTER

APPENDIX 6B: SAMPLE TRANSITION MATRICES

REVIEW QUESTIONS

EXERCISES

CHAPTER 7 - PD Model Validation

THE BASICS: PARAMETER ROBUSTNESS

MEASURES OF MODEL POWER

MEASURES OF PD LEVELS AND CALIBRATION

SAMPLE SIZE AND CONFIDENCE BOUNDS

ASSESSING THE ECONOMIC VALUE OF MORE POWERFUL PD MODELS

AVOIDING OVERFITTING: A WALK-FORWARD APPROACH TO MODEL TESTING

CONCLUSION

APPENDIX 7A: TYPE I AND TYPE II ERROR: CONVERTING CAP PLOTS INTO CONTINGENCY TABLES

APPENDIX 7B: THE LIKELIHOOD FOR THE GENERAL CASE OF A DEFAULT MODEL

APPENDIX 7C : TABLES OF ROC ε AND n

APPENDIX 7D: PROOF OF THE RELATIONSHIP BETWEEN NPV TERMS AND ROC TERMS

APPENDIX 7E: DERIVATION OF MINIMUM SAMPLE SIZE REQUIRED TO TEST FOR DEFAULT ...

APPENDIX 7F: TABLES FOR LOWER BOUNDS OF ε AND N ON PROBABILITIES OF DEFAULT

REVIEW QUESTIONS

EXERCISES

CHAPTER 8 - Portfolio Models

A STRUCTURAL MODEL OF DEFAULT RISK

MEASUREMENT OF PORTFOLIO DIVERSIFICATION

PORTFOLIO RISK ASSUMING NO CREDIT MIGRATION

STRUCTURAL MODELS OF DEFAULT CORRELATION

CREDIT MIGRATION

A MODEL OF VALUE CORRELATION

PROBABILITY OF LARGE LOSSES

VALUATION

RETURN CALCULATIONS

RISK CALCULATIONS

PORTFOLIO LOSS DISTRIBUTION

CAPITAL

ECONOMIC CAPITAL AND PORTFOLIO MANAGEMENT

IMPROVING PORTFOLIO PERFORMANCE

PERFORMANCE METRICS

REDUCED-FORM MODELS AND PORTFOLIO MODELING

CORRELATION IN INTENSITY MODELS

COPULAS

FRAILTY

INTEGRATING MARKET AND CREDIT RISK

COUNTERPARTY RISK IN CREDIT DEFAULT SWAPS (CDS) AND CREDIT PORTFOLIOS

CONCLUSION

REVIEW QUESTIONS

EXERCISES

CHAPTER 9 - Building a Better Bank A Case Study

DESCRIPTION

CURRENT ORGANIZATION

TRANSFORMING THE CAPITAL ALLOCATION PROCESS

PORTFOLIO ANALYSIS

ACTIVE CREDIT PORTFOLIO MANAGEMENT (ACPM)

DATA, SYSTEMS, AND METRICS

ACPM AND TRANSFORMING THE BANK

APPENDIX: FIGURES

EXERCISES

References

About the Authors

Index

Copyright © 2009 by Jeffrey R. Bohn and Roger M. Stein. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Bohn, Jeffrey R., 1967- Active credit portfolio management in practice / Jeffrey R. Bohn, Roger M. Stein. p. cm. - (Wiley finance series) Includes bibliographical references and index.

eISBN : 978-0-470-45512-8

1. Credit-Management. 2. Portfolio management. 3. Risk management. I. Stein, Roger M., 1966- II. Title. HG3751.B64 2009 332.7-dc22 2008042838

ForBrenda, Brittany, and Ian

—JRB

ForMichal, Ariel, and Tamir

—RMS

Foreword

Jeff Bohn and Roger Stein are ideally positioned to provide us with this artful treatment of credit risk modeling. The book is a masterful collection of accessible and practical guidance placed on strong conceptual foundations. As leading entrepreneurs and practitioners in the quantification of credit risk, and at the same time among the top scholars writing widely on the topic, Jeff and Roger have been riding a wave of exceptional changes in credit markets. The design of many new financial products, the explosive growth of trading in credit derivatives, a major change in bank capital requirements for credit risk, and a surge of new theoretical and empirical research have combined to make this the place to be among all areas of financial markets for the decade up to 2007. And then came the serious credit crisis in which we find ourselves. Roger and Jeff have been through it all.

The credit crisis of 2007-2008 has set us back on our heels. Issuance of structured credit products, not just in the subprime area, is down dramatically, just as issuance of collateralized mortgage obligations fell over a cliff after the 1994 blowout of David Askins’ Granite Fund. Numerous regulators, commercial banks, rating agencies, bond insurers, and buy-side investors are under exceptional scrutiny for their risk management and other failures. It is time to take stock of what we as modelers could have done better. In this excellent book, Jeff and Roger provide state-of-the-art guidance on how to measure credit risk, borrower by borrower and also at the portfolio level.

In my opinion, products were designed, rated, priced, and risk-managed with too much confidence in our ability to reasonably capture default correlation using the current generation of models and methods of data analysis. Had we better models for default correlation, some of the overhanging risks would have been better understood and avoided. Alternatively, with at least a better appreciation of the weaknesses of the models that we have been using, the tide of issuance of relatively complex products might have been stemmed somewhat. Would someone in any case have suffered the ultimate losses on subprime mortgage assets? Those losses were larger than they would have been without such a ready market for structured products, offering credit spreads that might have been appropriate for the risks as measured, but not for the actual risks. Laying off those risks through a food chain of structured products reduced the incentives of the direct lenders and servicers of the underlying loans to screen and monitor the borrowers and to limit credit appropriately.

The failure of our current generation of models to better measure default correlation is not restricted to products backed by subprime credit. For example, the market for collateralized debt obligations (CDOs) backed by corporate debt is also ripe for a crisis of confidence. It would take only a somewhat surprising string of corporate downgrades or defaults for investors, already spooked by the subprime crisis, to reprice bespoke corporate-debt CDOs in a manner that would make the distortions in this market during the events surrounding the GM downgrade of May 2005 seem like a mere hiccup.

Indeed, by mid-2008 the issuance of bank-loan collateralized loan obligations (CLOs) has fallen off significantly in parallel with the virtual disappearance of issuance of subprime-backed CDOs.

In concept, structured credit products like CDOs are well suited to transferring credit risk away from banks and other credit intermediaries, and placing it in the hands of buy-and-hold investors who are less crucial to the provision of liquidity to financial markets. Those investors can indeed be offered properly designed and rated products that are suited to their risk appetites and financial sophistication. Long-run institutional investors such as insurance companies, pension plans, and endowments can be rewarded with extra spreads for holding assets that are relatively illiquid, for they don’t need as much liquidity and should not pay for what they don’t need. For now, however, the well has been tainted.

Going forward, we need to pay more attention to the development and use of models with stronger conceptual foundations, fed by better and more relevant data. This excellent book by my long-valued colleagues, Jeff Bohn and Roger Stein, is a good place to start.

DARRELL DUFFIE LausanneJune 2008

Preface

Sen ri no michi mo ippo kara. (Even the thousand mile road starts from a single step.)

—Japanese Proverb

In theory there is no difference between theory and practice. In practice there is.

—Yogi Berra

Several years ago, a commercial banker asked one of us why he needed to calculate expected loss for his loan portfolio since he didn’t “expect” to lose anything. Shortly after this conversation, this banker’s bank experienced an unprecedented default in its portfolio, and this default impacted the profitability of the bank. The bank quickly moved to introduce more quantitative analytics to manage its risk and the banker who hadn’t expected any loss took early retirement.

Up until the past 10 years or so, calculating any portfolio level analytic, such as portfolio expected loss, was considered by many to be irrelevant to the executives driving the businesses at large financial institutions. Credit analysis consisted of qualitative characterization of a borrower’s health coupled with a few financial ratios they saw as necessary to keep regulators happy. The world has changed.

Today credit analysis encompasses both qualitative and quantitative analysis. Most executives at large financial institutions expect to see analytics such as portfolio expected loss. They also request estimates of unexpected loss (also known as portfolio volatility) and the likelihood of extreme losses (tail risk) that may impair the institution’s ability to run its business. The most recent credit crisis notwithstanding, it is a rare financial executive who does not now require a quantitative characterization of the overall risk faced by that institution. Financial institutions without the infrastructure to measure, monitor, and manage their credit exposure run the risk of sudden demise or possible takeover.

New strategies and instruments facilitate active diversification of a credit portfolio to better weather the current crisis and prepare for the next one. Financial institutions are in the midst of an unprecedented shift in the way they are managed and evaluated. In this book, we present a collection of ideas, models, and techniques for understanding and interpreting these changes.

With the rapid growth in quantitative credit risk management, we found in writing this book that many of our colleagues in academia and banking have also been busy writing. In fact, a number of excellent texts have been written in the past several years that provide a rich theoretical context for a diversity of credit models. Our goal in writing this book is perhaps far more modest but specific. We have tried to produce a practical text that presents a number of compelling ideas and descriptions in a way that makes clear how these techniques can be applied in practice. We have framed most of the discussions in the context of real business applications requiring the implementation of tools to support credit trading, active credit portfolio management (ACPM), and management of economic capital. When useful, we have included key derivations in the context of our model descriptions; however, more detailed understanding of the mathematics behind many of these models will require referencing one of the books or papers that we include in the References list.

Thus, our goal has been to write a book that provides substantial insight into our experiences in implementing credit-risk models and methodologies from the trenches, without necessarily providing a full complement of rigorous mathematical results in each case. By the same token, however, this is not intended to be a recipe book on financial engineering or a statistics manual. We have tried to limit our presentation of detailed algorithms to those that are not widely covered in other sources. So, for example, we do not discuss how to implement algorithms such as loess, the bootstrap, or Newton-Raphson, but do provide details on how to calibrate PD models to ratings from a number of different perspectives, or how to estimate asset volatility effectively for a structural model.

As in many endeavors, we will almost certainly disappoint some readers, but hope (in expectation) to generally satisfy most. There is a joke about three statisticians who go hunting. They spot a bird overhead in the distance. The first statistician steps up, fires, and shoots 50 feet in front of the bird. The second steps up, fires, and shoots 50 feet behind the bird. The third steps up, looks through his binoculars and declares, “We got him.”

We hope to do better than this!

To illustrate the practical challenges of using these models, we provide specific advice on various details of implementation, which we include in boxes throughout the text. We have also included a composite case study based on our experience working with financial firms building and managing credit, capital, and portfolio management systems. This case study and the practical examples throughout the book reflect the synthesis of our collective experience from interacting with hundreds of banks and financial institutions over the past 20 years.

This approach mirrors the evolution of credit models, tools, and systems in recent years. The models and analytics have become more standardized and more widely understood. Many of the good books we mention are available to take readers deep into the derivations of these models (see Duffie and Singleton 2003; Lando 2004; and Schonbucher 2003 for more detailed and comprehensive descriptions of the literature and derivation of credit models). The conceptual foundation of why these tools should be used has become more widely accepted. This was not always the case; however, the wave of research in credit modeling over the past decade and a half, led by these authors and their academic colleagues, has resulted in a body of theoretical work that is far better developed than it has ever been.

In industry, we now find that the bigger practical challenge is implementing systems that actually make use of these new analytics and tools in a way that realizes their conceptual promise. As many practitioners have discovered as they begin to implement credit analytic systems and procedures within financial firms, the size of the gap between theory and practice can be large. Our goal is to help fill this gap.

The broad concepts underlying ACPM and its associated economic capital management approaches are easy to enumerate and easy to explain. We consider five important ideas to be our catalysts for the value-enhancing characteristics of the models and frameworks we describe in this book:

1. Default probabilities are dynamic and, for many asset classes, can be accurately estimated.

2. Credit exposure correlations and loss given default can be estimated (though with considerably less precision than default probabilities), leading to a quantification of a credit portfolio’s risk.

3. Active management of credit portfolios can lead to higher return per unit of this quantified portfolio risk.

4. Economic capital is a scarce resource for a financial institution attempting to build a profitable business and is determined by a target credit quality.

5. Managing a portfolio of credit-risky instruments and managing a portfolio of business franchises require different business models, managers, and cultures to be successful. Transfer pricing of risk is an efficient tool for separating incentives associated with the credit portfolio and the portfolio of businesses.

In this book, we discuss the approaches to measuring quantities such as default probabilities and correlations that we have found most useful, and we attempt to provide insight as to how they can be used to facilitate active portfolio management and economic capital allocation. Along the way we will explore related themes such as quantitative risk management, valuation, and credit trading. The dynamic nature of default probabilities (from peak to trough of the credit cycle, typical default probabilities may change by a factor of five or six), coupled with the empirical fact that cross-sectionally they can range over a large spectrum (the range is typically one basis point to thousands of basis points) creates an opportunity in which implementation of powerful default probability models will lead to substantial savings as a financial institution minimizes its bad lending decisions.

Many financial institutions choose to implement single-obligor risk management systems only. Somehow, in practice, focusing on the stories behind each name tends to trump a less personal portfolio perspective. While we believe that any effort to implement best-practice systems is a positive step (even if that system focuses just on quantifying single-obligor risk), we will repeatedly emphasize our view that a portfolio view of credit is ultimately the best and most prudent way to manage a financial institution exposed to credit risk. Said another way, it is hard to make money (and avoid large losses) consistently by only focusing on single-name credit decisions without reference back to a portfolio.

The emphasis on the portfolio perspective of credit arises from the nature of the credit return distribution. Any quantitative analysis of credit begins with the skewed, non-normal return distributions typical of both individual credit exposures and portfolios of those exposures. (While correlations of credit exposures tend to be lower than the correlations of other types of securities such as equity, when coupled with the asymmetric payoff of credit exposures, they can create substantial skewness in the loss distributions of these assets.) Herein lies the source of diversification benefits from large portfolios. A holder of a credit portfolio continues to benefit in terms of diversification as more small and minimally correlated exposures are added to the portfolio. With symmetric distributions such as those exhibited by equities, the incremental benefit of diversification is quite small once the portfolio is in the hundreds of names (some researchers argue incremental diversification benefit stops in the tens). In contrast, the probability of correlated extreme losses is small in credit portfolios, but not negligible and certainly not economically insignificant. Unlike an equity portfolio with a (fairly) symmetrical return distribution, a so-called fully diversified credit portfolio may still have substantial volatility due to this nondiversifiable component of its correlation structure.

Ironically, credit markets originate credit in a decidedly undiversified way. As a consequence, holding the (local) market-weighted portfolio of outstanding credit produces dangerously concentrated portfolios. These circumstances contrast with the equity market where the market-weighted portfolio is well diversified. The implication is that while active management does not seem to produce much benefit for equity portfolios, it does produce substantial benefit for credit portfolios. This observation sets the stage for the importance of implementing systems, models, and tools to support active credit portfolio management.

However, although our knowledge and technical abilities regarding credit-risk quantification have expanded dramatically, there remain substantial hurdles. Paramount among these is the practical difficulty in estimating and validating correlation models, which are essential to effective portfolio management. In the case of single-obligor default risk modeling, we now often have enough data to draw conclusions about the performance of a model. (This was not always the case. As recently as a decade ago, default probability models were sold in terms of their conceptual coherence or anecdotal behavior. As more data became available, these conceptual discussions were backed up by the development of rigorous validation frameworks and techniques, which moved the discussions from model coherence to empirical performance.) In contrast, even today, we are still in the conceptual stage of understanding many correlation models. Partly due to the nature of correlations and partly due to a lack of data, often we cannot make strong statements about correlation models on the basis of rigorous validation. Nonetheless, correlations are an integral part of good portfolio models and we must often make do with the best tools that are available, augmented with judgment and experience.

Another practical difficulty in implementing active credit portfolio management has more to do with the psychology of lenders than the limitations of our mathematics. Financial institutions thrive on the creation of customer relationships, and executives love a good story. Shifting to a portfolio perspective often replaces some of the anecdotal discussions of industry structure, a company’s product, and the personality of a CEO with reams of data presented in an abstract way. Executives at leading financial institutions understand the importance of portfolio-based decision making, but they and their staff still lean toward single-obligor analyses. While industry experience and common sense are crucial to using credit models wisely, they cannot generally, in and of themselves, form the basis of credit policies for complicated portfolios of correlated assets.

In our judgment, it is useful for organizations to segment the portfolio management function into a central group, while at the same time providing relationship managers with incentives to cross-sell services into their client base. In this way, the anecdote- and relationship-based approaches can still have relevance alongside those of the individuals with more of a quantitative bent who will migrate to the portfolio management function. The economic capital allocated to support the relationship business, which generates fee income from selling financial products and services, can then be differentiated from the economic capital allocated to support the central management of the credit risk in the portfolio.

The portfolio perspective does not release a bank’s management from the responsibility to stay vigilant as to the possibility of fraud and poor monitoring, which some have asserted were common in the run-up to the subprime difficulties witnessed in recent years. Rather, the portfolio perspective—informed by quantitative characterization of the return and risk profiles of a bank’s portfolio—should be part of senior management’s toolkit. Models serve the specific purpose of distilling information and reducing the level of complexity in understanding the return and risk of a portfolio exposure. Unfortunately, model output can sometimes become a crutch for managers unwilling to drill into the details of a transaction or portfolio strategy. This book is one attempt at demystifying key credit models so that more participants in the financial markets can better understand the underlying drivers of the risks to which they are exposed.

In a number of places in this book, we make a point of relating abstract financial theory to quantifiable financial costs and benefits that can be used for the purpose of better aligning incentives with share-value maximizing behavior. The result should be a more valuable financial institution.

WHY ACTIVE CREDIT PORTFOLIO MANAGEMENT?

Several trends in the financial markets reflect the growing recognition of the benefit of active credit portfolio management (ACPM). There are a number of reasons for this. First, analyses of past banking crises highlight one major common source of bank failures: too much portfolio concentration. If a bank develops a strong business in a particular area, and if it does its job well, over time it will generate concentrated exposure to this area as the bank and clients seek each other out in these areas of specialization. In a global market, the correlations may be less apparent, but no less dangerous. Actively managing a portfolio mitigates this concentration risk to the extent possible.

Second, the development of credit derivatives such as credit default swaps (CDSs) and synthetic collateralized debt obligations (CDOs) has presented a new set of tools for managing diversification. Recent difficulties in the structured finance market have dented some of the enthusiasm for CDO and collateralized loan obligation (CLO) structures. The broader credit crisis of 2007 and 2008 has cast doubt on the usefulness of CDSs. Nonetheless, when used for hedging, rather than as investment vehicles in and of themselves (particularly when the investment is highly levered), users of synthetic structures and credit derivatives can improve diversification relatively cheaply compared to transacting in the underlying assets individually. However, along with these powerful instruments comes responsibility. Participants in this market for credit derivatives must continue to work on building a robust and viable market with natural buyers and sellers trading in all market conditions and at reasonable leverage levels. Much of the analysis that benefits portfolio analysis can also be applied to these synthetic versions of credit portfolios. On the other hand, when these instruments are used to “take a position” on the market directly, rather than to hedge an existing position, they can actually increase concentration and can work against prudent portfolio management practice.

Third, financial institutions that manage their credit portfolios appear historically to weather economic downturns more effectively. One of the more recent economic downturns in the United States, following the dot-com bust at the start of the new millennium, highlighted the resilience of U.S. commercial banks with diversified portfolios. This recession was marked by a lack of bank failures, due in no small part to how credit exposure was managed. More recent banking difficulties have been partly a consequence of disappearing liquidity in the financial markets; however, many of the larger failures were also a consequence of large portfolios with concentrated exposure to the U.S. real estate market. One of the authors has heard from some credit portfolio managers that they were never given the opportunity to manage credit exposure that entered their institution’s portfolio in the form of tranches in structures with mortgages as collateral. These same managers have successfully minimized losses in portfolios of large corporate loans that have historically been the source of concentration risk in bank portfolios. Hopefully, more financial institutions will begin to manage all of their credit exposures from a portfolio perspective (not just large corporate exposure).

Despite the advances in managing credit portfolios, the recent difficulties triggered by the subprime crisis in the United States suggests that many institutions still have work to do in terms of managing their exposure to liquidity risk that arises when too many market participants end up on the same side of every trade. In the end, however, even the best risk management systems are still only a component of a business strategy. Management still must take firm control of the institution and rely actively on both risk control systems and sound business judgment to provide guidance.

Ultimately, the emphasis on ACPM derives from a premise that underlies our thinking with respect to banking: Bank managers should be making decisions to maximize the value of the bank’s equity shares. This emphasis by bank managers will result in substantially different portfolios than those at banks whose managers focus on maximizing the amount of assets held in the bank’s portfolio.

That is, a large bank portfolio does not necessarily translate into higher bank market capitalization. The fact that defaults are rare and the somewhat abstract nature of how capital underlies the ability of a bank to make a loan make it difficult for some bank managers to understand why concentration risk in a portfolio is such a bad thing. Since bank failures are very uncommon, a manager may see healthy income from a large, concentrated portfolio for years before a cluster of defaults throws the bank into difficulty. The bank’s share price should, however, reflect this risk. Without proper incentives, a bank manager may conclude that he should capture as much income as possible now and worry later if the bank portfolio deteriorates. Our perspective, reflected throughout this book, is that share price, not portfolio size or portfolio income, should be integrated into a bank’s performance management and compensation framework as a natural mechanism by which credit risk can be managed. Since the share price reflects the market’s assessment of the firm’s equity value, including the risk of insolvency, focusing on share price will align incentives of the bank’s senior management and its line staff with the objectives of the shareholders.

Finance theory suggests that ACPM and the models used to separate the credit portfolio from a bank’s (or other financial institution’s) other businesses will lead the bank toward a higher share price. It is our view that operating in an environment where managers make decisions that lead to a higher market capitalization will, on balance, be best for the bank, its employees, and the country or countries in which it is located. It will also lead institutions away from a short-sighted search for profit at the expense of longer-term risk, given the objectives of management and the appetite of the shareholder base. Active credit portfolio management makes these trade-offs explicit and transparent.

OBJECTIVE OF THIS BOOK

As any new field of analysis develops, pockets of inefficiency and mischaracterization persist. Quantitative analysis is both revered and reviled. Some practitioners extol the virtues of returning to qualitative analysis of credit. Others dismiss existing models as oversimplifications of the world and insist that credit risk management demands more complex solutions—or much simpler ones. We tend to view the correct balance as sitting somewhere in the middle. A large swath of credit analysts still focus on fundamental analysis only. The number of vendors of credit analytics has increased, each pitching its own version of a credit risk management platform. Despite the increase in analytic firepower, the field is new enough to make standardization of approaches and techniques difficult in practice. The trends in the market married with the availability of analytics and tools make understanding the concepts underlying these models an essential part of financial education today.

Our objective in writing this book is to provide a coherent and comprehensive (to the extent possible) framework for understanding and implementing effective credit risk management and credit portfolio management systems, evaluating credit trades, and constructing credit portfolios. It is worth repeating that this book is not intended to be an exhaustive survey of the broad literature on credit models or of all frameworks that have been developed or used. The References section at the end of the book provides sources to satisfy the reader’s curiosity about other models and frameworks.

In our discussions, we tend to focus a bit more on a structural approach to analyzing credit risk, supplemented by other methods we think are useful in particular applications where the structural framework falls short. As it turns out, there are many applications where we will recommend the reduced-form modeling approach or a data-driven econometric one. A well-trained analyst will be comfortable with a variety of models and frameworks. While our preferred framework is grounded in economic explanations of default, our discussions of other frameworks are generally motivated by the challenge of making use of existing data. While we often find structural models most appealing from an intuitive perspective, in a number of settings such models cannot be practically implemented and thus pragmatism, rather then dogma, guides us. When helpful in highlighting our recommended approach, we discuss some other popular implementations of the models for certain applications.

In order to increase the reader’s understanding of the models ultimately in use, we have tried to provide an (extremely) abbreviated history of how the models have evolved over time. We hope that this contextualization of how models have changed will improve the reader’s grasp of the underlying concepts. We have not necessarily been comprehensive in these descriptions (again we refer the reader to the texts cited in the References to expand on our exposition); but we have highlighted the key developments that lead us to where we are today in terms of how models are used in practice.

MODELS IN PRACTICE

In all of these discussions, we warn the reader that we have developed strong views over the years and that we tend not to hide our opinions. We have acquired almost 20 years’ experience in the credit arena and have developed deep-seated views about what a financial institution should and should not do to build value in a credit-related business. We plan to share this perspective. By way of disclosure, we note that in practice, an effective, practical framework will be rough around the edges with the odd inconsistency here and there (usually to deal with available data or the lack thereof). Sometimes two seemingly incompatible models can have value in specific contexts, resulting in retention of both models despite the fact that they may not be consistent with each other from a theoretical perspective. In fact, we recommend that financial institutions look to multiple models and incorporate stress testing and reality checks frequently when building credit risk systems as a method for mitigating model risk.

Importantly, though, all models are not created equal and some models are better avoided. How can we make a determination as to the quality and usefulness of a particular model? Over time, we have developed the view that five criteria for evaluating a model or framework in the context of actual implementation of a credit risk and portfolio management system are useful:

1. Possibility of objective evaluation.

2. Interpretability of model output.

3. Relevance of model output to real and important business decisions.

4. Contribution to financial institution’s value.

5. Reasonable cost relative to benefit of using the model.

Notice that criterion 1 immediately leads us down the applied modeling path. Many elegant mathematical credit models cannot currently (and in some cases may never) be tested, for lack of the right data. Other models reflect esoteric issues irrelevant to the real world of lending and trading.

Implicit in these five criteria is the view that objective evaluation will be facilitated by quantitative analyses and that those analyses will validate the performance of the model. Many times, however, we encounter quants who stop at criterion 1: quantitative validation. Their institutions will suffer for this narrow focus. The list is vitally important and speaks to the manner in which a model or framework changes and orients an organization.

In the end, a model will only be as good as the way in which it is used. The nature of the model—its fit along the dimensions previously outlined—can materially impact the probability of it being used well. One consequence of our perspective is that sometimes less elegant models from a theoretical (usually mathematical) perspective will be judged superior to models that reflect a theoretical infrastructure appealing to academicians. A good model will become integrated into the way a financial institution is managed on a day-to-day basis.

These five criteria lead us to the following conclusions:

1. Whenever possible, use models based on observable data and, if possible, choose market data.

2. Models should be transparent—no black boxes. Models should provide some sort of explanation for their output in addition to the output itself.

3. Economic, causal models tend to be more useful.

4. Simple models are, all else equal, better; however, the world is complicated and models should not be too simple.

5. Managers should align carefully the way in which the models are used with organizational incentives.

The last conclusion will make choosing models, frameworks, and systems very much an exercise in adaptation. Both models and organizations will need to be adapted to each other. We will provide our own insights based on the experience of observing this adaptation and evolution at financial institutions around the world. While our work has tended to be weighted toward interaction with large, global financial institutions, in our experience, financial institutions of all sizes will benefit from moving toward a quantitative orientation in credit and portfolio management coupled with incentives that target maximization of share value.

BASEL II AND OTHER REGULATIONS

While regulations and regulatory authorities are a necessary part of the financial system, we will only refer to regulations in passing. Many other books explain the intricacies of bank regulation in general and Basel II in particular (e.g., Ong and KPMG 2004). Please look to those publications for details on strategies for regulatory compliance. This book is not intended as a source for in-depth explanation of bank regulations. In fact, we view bank regulation similarly to financial accounting—something that has to be done, both for the sake of the bank and the broader financial system, but not something that should be the main driver of an institution’s business decisions. Regulatory capital should be measured so that if a bank discovers it is facing constraints in this dimension, it can take measures to secure regulatory capital relief. Decisions as to which businesses to develop and which transactions to do should be driven from economic capital models (subject to regulatory compliance).

Unfortunately, the good intentions and insights of the Basel committee are sometimes buried beneath the need for local regulators to turn these principles into guidelines that are practically implementable in their local markets. These interpretations, and the limitations of the local environments in which they are executed, sometimes create distortions in the markets as financial institutions scramble to respond to (and sometimes try to arbitrage) the rules.

Regulatory capital should not be mistaken for market-based economic capital. While it is important to distinguish between economic and regulatory capital, we will only touch briefly on Basel II in the context of some of our discussions. We find that in practice the value of Basel II, which is high, stems mostly from the discipline that it creates within organizations as well as the allocation of budgets to risk management groups that are typically underfunded and understaffed. Regardless of our perspective, Basel II is a development that most financial institutions with a global presence will implement in some form and to which they will thus pay close attention. We support the efforts of knowledgeable bankers to engage in working with the Bank for International Settlements (BIS) and their own local regulators to continue to develop useful and practical rules for determining regulatory capital. That said, from a business perspective, most of an institution’s effort and budgets for capital management systems will typically end up being focused on economic capital and its return.

OUR APPROACH TO EXPLAINING IDEAS

The exposition in this book will tend toward practical examples with minimal presentation of lengthy mathematical proofs. Of course, mathematical derivations can sometimes highlight conceptual points, and the field of credit risk management is at its core mathematical. Some readers may judge that we have been too liberal in our use of mathematical formulations while others will thirst for more technical detail. Wherever possible, we have tried to provide references to other work that can satisfy a more mathematical reader’s desire to achieve a better understanding of any particular model’s foundations. While both of us have taught in academic environments for many years, our focus in presentation here is more on how a particular theoretical construct or model can help solve a real business problem rather than on the elegance of various theories from an intellectual or aesthetics standpoint. As a consequence of this rather pragmatic perspective, we have tried to highlight the applications of various models in the context of our real-world experience.

After providing an overview in Chapter 1 of credit analysis, credit portfolio management, and associated concepts, we move to the models and frameworks used in this area. Before we tackle the specifics of the models, the second chapter discusses a number of organizational issues associated with ACPM in practice to set the stage for the other discussions. Each subsequent chapter then addresses a different aspect of the tools and frameworks that can change the processes and systems used by financial institutions today. The first several of these cover the range of PD and valuation models used in these credit portfolio management systems: structural, econometric, and reduced-form. Since differentiating the usefulness of models becomes key to effective system implementation, we have set aside an entire chapter on model validation. Though applied work on estimating loss given default (LGD) is still in its early stages of development, we have also included a short chapter that discusses this problem and some approaches to modeling LGD.

The penultimate chapter of the book focuses on the portfolio modeling question with an emphasis on correlation, estimation of loss distributions, and touches on how structured credit works. The final chapter presents a case study of a bank implementing these tools to build an ACPM and economic capital allocation function. This case study is a composite drawn from a number of actual implementations completed at a number of financial institutions with which we have worked. The composite nature of the case study allows us to highlight the range of issues when implementing these systems that often go beyond just choosing models.

HOW TO USE THIS BOOK

As we mentioned a number of times in this Preface, several more detailed and more comprehensive texts than ours have been written on credit already (see our References for a list). While we did not sit down to write another such book, we did hope to write this text to serve as a valuable field guide for practitioners, an industry-focused text for business schools, and an excellent complement to several of the other more theoretical texts. Moreover, we provide detailed discussions of the practical issues on which we have not seen other authors previously opine.

The first two chapters are essential background reading for the rest of the book. After these chapters, the reader may choose to read the rest of the book in any order, though our recommendation is for readers to get comfortable with single-obligor credit modeling before moving on to the discussions of credit portfolio modeling. Regardless, each chapter is designed to stand on its own. In fact, readers who read the book cover to cover will find some of the discussions and concepts repeated in several chapters. This is not an accident. Our experience in teaching this material is that some repetition of material—for example, of material on correlation or risk-neutral concepts—improves the reader’s ability to retain understanding. For readers who pick and choose what they wish to learn, the complete discussion in each relevant context minimizes the need to flip back and forth through the book (although we expect that this jumping around in the text cannot be completely avoided).

Supplemental materials are provided to complement the text. In particular, we offer review questions and problems at the back of each chapter. Other materials, such as computer source code, can be found on the web site www.creditrisklib.com. As it turns out, we have not been able to find a single computational tool that seems to suffice for building models for all the applications covered in this field. Even between the two of us, we find the need to use a number of platforms. That is why you may find code written with several different languages and tools. Instead of bemoaning the lack of standardization, we accept our fate, celebrate the diversity of coding platforms, and try to use each to its best effect. Since we are continually updating and adding to these materials, we recommend you refer back to our web site often. We retain the copyright to this code; however, it can be used without charge as long as you prominently provide attribution whenever you use all or a portion of these materials. We also encourage you to add to our growing store of credit risk tools on the web site. Please send contributions you wish to share with the public to [email protected]. Finally, if you have comments or corrections on the text, please let us know at [email protected].

Acknowledgments

Who would have guessed 30 years ago that the interaction of John A. (“Mac”) McQuown, Oldrich Vasicek, Fischer Black, Myron Scholes, and Robert Merton would have created such a lasting impact on the world of credit? We are grateful to all of them for building the foundation for modern, quantitative credit risk and portfolio management. The addition of Stephen Kealhofer to this list of quantitative finance visionaries rounds out a powerful group of thinkers who have given us much of the tools and vocabulary to write this book. We express our thanks and acknowledge our intellectual debt to this group. We also thank John Rutherfurd and Mac McQuown for facilitating the creation of the company, Moody’s KMV, that enabled the two of us to work together.

Over the years, many people at Moody’s and KMV have contributed to the stockpile of credit knowledge reflected in this book. We thank all the members and former members of these companies who worked with us on developing better implementations of quantitative credit tools. In particular we thank Deepak Agrawal, Jalal Akhavein, Navneet Arora, Richard Cantor, Lea V. Carty, Ren-Raw Chen, Peter Crosbie, Ashish Das, Douglas Dwyer, Ken Emery, Greg Gupton, David Hamilton, Shota Ishii, Felipe Jorda˜o, Sean Keenan, Andrew Kimball, Ahmet Kocagil, Kyle Kung, Matt Kurbat, Som-Lok Leung, Amnon Levy, Douglas Lucas, Christopher Mann, Albert Metz, Hans Mikkelsen, Bill Morokoff, Norah Qian, Jody Rasch, Alex Reyngold, Joachim Skor, Jorge Sobehart, Ben Zhang, and Jing Zhang. In addition, we thank the many clients of Moody’s and MKMV that worked in intellectual partnership with us over the years as many of these ideas became instantiated in tools and processes facilitating quantitative risk management and active credit portfolio management. We also thank the following current and former colleagues at Shinsei Bank: Roger Browning, Mark Cutis, Rahul Gupta, Nick James, and Thierry Porte.

During the course of this project we received valuable feedback from a number of colleagues. In particular, several of them read large portions of this text and provided extensive comments on the content: Ed Altman, Marcia Banks, Richard Cantor, Ashish Das, Darrell Duffie, Douglas Dwyer, Gus Harris, David Keisman, Andrew Kimball, David Lando, Terry Marsh, and Jing Zhang. We are grateful for your time and patience in crafting your feedback which greatly improved the book.

Over the past eight years we both have benefited greatly from the feedback of members of Moody’s Academic Advisory and Research Committee (MAARC), both during presentations at our semiannual meetings and through individual correspondence on particular topics. We would like to thank past and present members of the committee for their continued support and feedback: Pierre Collin-Dufresne, Darrell Duffie, Steve Figlewski, Gary Gorton, David Heath, John Hull, David Lando, Andrew Lo, William Perraudin, Mitchell Petersen, Raghu Rajan, Stephen Schaefer, Ken Singleton, Jeremy Stein, and Alan White. MAARC has been a constant source of feedback and new ideas.

Jeffrey R. Bohn also thanks the students in U.C. Berkeley’s master’s in financial engineering program who took his credit risk modeling course for providing feedback on the manner in which much of this material is described. Roger M. Stein thanks the students in the NYU Stern School of Business who took the courses he taught on modeling and attended his lectures over the past decade and who, through questions (and occasional blank stares), helped shape the presentation of this content.

Portions of the material here appeared in earlier published articles in a number of journals. We are grateful to the editors of the Journal of Banking and Finance, the Journal of Investment Management, and the Journal of Risk Model Validation for granting permission, where necessary, to use this material.

We also thank our editors, Bill Falloon, Emilie Herman, and Michael Lisk of Wiley who gave us excellent guidance on developing the material and shaping it into book form.

We created the “word cloud” images at the beginning of each chapter at www.wordle.net

While we have tried to make this book as useful as possible, there is no single recipe for most of the problems we discuss. Rather than attempt to offer a one-size-fits-all blueprint, we have tried to provide sufficient detail and practical advice to motivate readers to think about and implement some of the tools in this book and to begin to take informed action. However, as with any technical field, there is always a gap between reading about something and doing it. Setting up an active credit portfolio management function requires hundreds of small and large decisions, and these require more specialized and customized expertise than can fit into any general text.

This field still has a ways to go. We look forward to maintaining and extending associations with the members of the global credit community to overcome the long list of issues still to be resolved in this dynamic field of quantitative credit risk modeling.

Importantly, we express a number of strong views in this text. These views are wholly our own, and do not represent the views of our current or former employers (Moody’s Investors Service, Moody’s Risk Management Services, Moody’s KMV, and Shinsei Bank) or any of their affiliates. Accordingly, all of the foregoing companies and their affiliates expressly disclaim all responsibility for the content and information contained herein.

CHAPTER 1

The Framework: Definitions and Concepts

Commercial credit is the creation of modern times and belongs in its highest perfection only to the most enlightened and best governed nations. Credit is the vital air of the system of modern commerce. It has done more, a thousand times more, to enrich nations than all of the mines of the world.

—Daniel Webster, 1934 (excerpt from speech in the U.S. Senate)

Theories of the known, which are described by different physical ideas, may be equivalent in all their predictions and are hence scientifically indistinguishable. However, they are not psychologically identical when trying to move from that base into the unknown. For different views suggest different kinds of modifications which might be made and hence are not equivalent in the hypotheses one generates from them in one’s attempt to understand what is not yet understood.

—Richard Feynman, 1965

Objectives

After reading this chapter, you should understand the following:

• Definition of credit.

• Evolution of credit markets.

• The importance of a portfolio perspective of credit.

• Conceptual building blocks of credit portfolio models.

• Conceptually how credit models are used in practice.

• The impact of bank regulation on portfolio management.

• Why we advocate active credit portfolio management (ACPM).

WHAT IS CREDIT?

Credit is one of the oldest innovations in commercial practice. Historically, credit has been defined in terms of the borrowing and lending of money. Credit transactions differ from other investments in the nature of the contract they represent. Contracts where fixed payments are determined up front over a finite time horizon differentiate a credit instrument from an equity instrument. Unlike credit instruments, equity instruments tend to have no specific time horizon in their structure and reflect a claim to a share of an entity’s future profits, no matter how large these profits become. While some equity instruments pay dividends, these payments are not guaranteed, and most equity is defined by not having any predetermined fixed payments.

In contrast, traditional credit instruments facilitate transactions in which one party borrows from another with specified repayment terms over a specific horizon. These instruments include fixed-coupon bonds and floating-rate loans (the coupon payments are determined by adding a spread to an underlying benchmark rate such as the U.S. Treasury rate or LIBOR1). Corporations are well-known issuers of these types of debt instruments; however, they are not the only borrowers. The past several decades have seen an explosion of consumer credit (particularly in the United States) in the form of home mortgages, credit card balances, and consumer loans. Other borrowers (also called obligors) include governments (usually termed sovereigns) and supranational organizations such as the World Bank. The credit risk of these instruments depends on the ability of the sovereign, corporation, or consumer to generate sufficient future cash flow (through operations or asset sales) to meet the interest and principal payments of the outstanding debt.

As financial engineering technology has advanced, the definition of credit has expanded to cover a wider variety of exposures through various derivative contracts whose risk and payoffs are dependent on the credit risk of some other instrument or entity. The key characteristic of these instruments is that, here again, the risk tends to lie in a predetermined payment stream over the life of the security or contract. Credit default swaps (CDS) exemplify this trend which aims to isolate the credit risk of a particular firm, the reference obligor, by linking a derivative’s value to the solvency of the reference obligor, only. These contracts require the protection buyer to pay a regular fee (or spread) to the protection seller. In the event the reference obligor defaults (per the specification of the CDS contract), the protection seller is required to make the protection buyer whole per the terms of the contract. Conceptually, the contract represents an insurance policy between the buyer (the insured) and the seller (the insurance provider). Extending the metaphor, the regular fee represents an insurance premium and the payout in the event of default represents an insurance claim under the policy. While a myriad of contract types now trade in the market, fundamentally they all represent a view on the credit risk of the underlying reference obligor.

While a CDS refers to a single name, derivative contracts on indexes of many named obligors can also be purchased as contracts on a specific basket of assets. These instruments expand the ability of credit portfolio managers to manage a large number of exposures without always resorting to hedging on a name-by-name basis or selling assets outright.

A related set of securities requiring financial engineering are broadly defined as structured credit. Popular forms of structured credit (also known as securitization) include collateralized debt obligations (CDOs) and asset-backed securities (ABS). In recent times, the credit crisis has made discussion of CDOs and ABSs more common in the media. Many commentators have called for drastic measures to curtail the use of structured credit. While abuse of these instruments can increase risk in institutions and markets, structured financial products can also be used responsibly to reduce risk in the financial system. Some regional banks, for example, have successfully hedged the concentration risk in their portfolios that results from most of their loans being originated in a single geography. They do this by selling some of their portfolio risk via structured credit. Other investors have purchased this risk and integrated it into their own portfolios as diversifying investments, creating lower volatility portfolios with improved return per unit of risk profiles. All market participants benefit from this kind of trading. Of course, these instruments can be abused when combined with excessive leverage or when market participants attempt to speculate using structures they do not fully understand.

But even the simplest of financial instruments such as equity can be inappropriate for particular investors in certain situations. The same is true of structured credit. We try to be careful to distinguish the purpose from the characteristics of particular instruments.

Conceptually, the basic structure of these instruments is straightforward: A number of securities or derivative contracts called collateral are placed in a structure called a special purpose vehicle (SPV) or special purpose company (SPC), creating a corporate vehicle to direct the cash flows from the collateral. In its simplest form, the purpose of the SPV is to borrow cash, typically through debt issuance, and to use this cash to purchase the collateral: some type of credit-sensitive obligation. The collateral may be provided by a financial institution, such as a bank that issues mortgages, or purchased in the secondary market, such as the case of corporate bonds.

Why could not a financial institution just issue the bonds directly rather than through an SPV? The purpose of an SPV is typically to create bankruptcy remoteness for the issuance of the debt. This means that the ownership of collateral is legally transferred from, say, the bank that made the loans, to the SPV. The objective is to ensure that if the bank goes into default, the collateral will not be considered part of the assets of the bank. Said another way, the SPV structure ensures that the collateral will be used only for the benefit of the holders of the structured securities issued by the SPV, regardless of where it was originated.

The SPV uses the cash flow from the collateral to pay back the debt as the collateral generates payment income through, for example, amortization and interest payments. The cash flow from the collateral is paid out to holders of each class of the liability structure (called a tranche) of the SPV per a set of rules called a cash flow waterfall. The tranching of debt creates a priority of payments (or of loss positions) such that more junior tranches (i.e., those lower in the capital structure) absorb losses first, followed by the next most senior, and so on. The motivation behind these structures is the desire to change the return/risk profile of the collateral into a set of securities or tranches with different return/risk profiles, with lower tranches exposed to more risk and higher tranches enjoying greater protection from collateral losses. In many structures there are also rules that specify that all cash be directed to more senior tranches if the performance of the collateral begins to deteriorate, providing still further protection for the higher tranches. It should be obvious that the analysis of many types of structured instruments is therefore quite similar to the analysis of a portfolio of assets in any financial institution but with the added complication of waterfalls and other structural provisions.

The names of these structures, such as CDO or ABS, reflect this collateralized nature of these instruments. Each specific structure name refers to the nature of the collateral:

• CLO: Collateralized loan obligation.

• CBO: Collateralized bond obligation.

• CDO-squared: CDO of tranches issued by other CDOs.

• RMBS: Residential mortgage-backed security.

• CMBS: Commercial mortgage-backed security.