 Book Review: An Introduction to Credit Risk ModelingGreg M. Gupton Abstract: An Introduction;to Credit Risk Modeling by Christian Bluhm, Ludger Overbeck and Christoph Wagner, Chapman & Hall/CRC, 2002.;297pp. Hardcover, US$75.00.;ISBN: 1-58488-326-X. An Introduction to Credit Risk Modeling débuted two years ago and it is a safe bet that it will be around for at least another two. The book continues in its popularity for three reasons: it is very readable; it squarely hits the "sweet spot" of formulae-to-prose that is;just right; and it is eminently practical. The;authors each have experience in the internal risk management functions of large commercial;banks. Their backgrounds underscore the;most likely buyers of this book: (1) anyone;working at a commercial bank or on a buyside credit portfolio; (2) anyone required to;deal with or understand vendor credit-VAR;models; or (3) anyone new to the details of credit risk modeling who would like to start;understanding the math in a gentle way. The opening chapter sets the tone that this;is a book written by skilled practitioners rather;than rarified scholars. The reader is immediately;shown two handy tricks of the trade that;one could likely use many times over. There;is also a nice highlighting of techniques that;constitute "best practice" as opposed to merely;common practice. One caution that the authors;themselves underscore in the closing two;pages of Chapter 1 is that the book cannot;address Basel II since the regulations were;too much in flux at the time of writing.;These, plus two more Basel-related pages in;Chapter 8, total only four pages that have;become obsolete since the book's writing.; Chapter 2 discusses in some detail the;methodologies behind the major vended;credit-VAR models in approximate proportion;to their "market share". These include;the MKMV models of EDF and Portfolio-;Manager, CreditMetrics, CreditRisk+, CreditPortfolioView, and intensity models.;For someone who has to deal with these;models as part of their job, this chapter alone;is a godsend as it details much of the practical;workings and specifics. I personally found;paragraph 2.6.1 interesting because it showed;me how I could model different copulas;(eg, with t-distributed marginals) in an Excel;spreadsheet. The next two chapters give more depth to;the two prevailing "camps" of credit VAR;modeling approaches. These are the structural;frameworks of MKMV and CreditMetrics;(Chapter 3) and the insurance-type approach;exemplified by CreditRisk+ (Chapter 4).;After linking asset-value models to the;academic literature, Chapter 3 takes the;reader step-by-step through a quick review of;option pricing, the mechanics of geometric;Brownian motion and the extensions of the;Merton "classical" model. The reader is provided;with a full framework that will enable;him or her to understand the relationships,;assumptions and theoretical foundations of;the so-called "structural" frameworks. Chapter 4 gives detail to, specifically, the;CreditRisk+ framework. After giving a brief overview (which rather repeats a section in Chapter 2), the authors then step through how to build up the specification of obligors, individual sectors, default distributions and compound sectors. I found this very straightforward description to be more readable than;the original CreditRisk+ technical document and recommend it to anyone who works with- or whose job is affected by - CreditRisk+. Chapter 5 is a brief 17-page comment to the effect that value-at-risk is only one possible;choice of risk measure. Indeed, for very;logical reasons that are known as coherency,;VAR may be a poor choice. While I suppose;it is good for the reader to be aware of this;debate, the seemingly alternative measure,;expected shortfall, has its own issues and is;more computationally expensive. Overall, I;feel this chapter does not add to the goals of;the book and I would have advocated leaving;it out entirely. Chapter 6 discusses default term structure;modeling. After laying out the nomenclature;and notation common to most hazard rate;models, the authors give a nice discussion;of the difference between risk-neutral and;physical default probabilities. The distinction;is important because risk-neutral figures are;needed for pricing a security. Then, since;many models are parameterized with rating;agency default data, the authors detail concepts;such as default cohorts, transition matrices;and Q-generator matrices. Some of their;suggestions on how to coerce matrices to be;"better behaved" are a bit ad hoc and I would;commend a diligent reader to papers such as;"Non-parametric analysis of rating transition;and default data" by Fledelius, Lando and;Nielson (2004) in the Journal of Investment;Management, vol. 2(2), pp. 71-85. The final two chapters embark on necessarily;brief surveys of topics that have become;enormously wide in the past few years: credit;derivatives and credit default swaps. A full;treatment of these is better found in other;books that are entirely given over to these;specialized areas. In their book the authors;do a nice job of introducing the topics and;relating the specialized models used in these;areas to the more general modeling frameworks;that are the bread and butter of credit-VAR frameworks. Chapter 7 presents a nice itemization of;the most common credit derivative structures:;total return swaps, credit default swaps,;baskets, credit spread products, and creditlinked;notes. The authors highlight some of;the pivotal modeling assumptions, such as;correlation estimation. They also touch on;the added complexities of counterparty risk. Chapter 8 is a whirlwind taxonomy of the;plethora of CDO structures, with a handy;family-tree chart. It is an interesting historical;fact that the rating agencies were the leaders;in developing models of CDOs, and their;legacy, such as the binomial expansion technique;(BTE) and the Moody's diversity score,;while quaint, are still broadly influential. The;authors discuss both, but the original papers;are available on www.Moodys.com.; There is some unevenness in the fit and;finish of the book that I might blame on the;publisher. For example, I have never before;seen formulae continue across on to the next;page. This occurs more than once and involves;equations that are only two or three lines long.;Another criticism is that the notation is sometimes;clumsy. For example, in discussing;basket credit derivatives, notations for the kthto-;default include the "skth" spread and the;"F(f:n)kth=i ... probability distribution of the kth;order statistic of the default times...". It is not the purpose of this book to prove;that the modeling frameworks presented are;good at explaining the empirical evidence,;nor to declare any one framework superior;to all others, nor even to advocate that one;framework should be used by all diligent;institutions. Anyone with such agendas is;missing the point. The book succeeds wonderfully;at its goal, which is to provide the reader with a deeper understanding of the;currently available credit risk modeling frameworks. Mr Gupton is the lead author of CreditMetrics;and recently has been leading the loss given;default research efforts of MKMV with;LossCalc. In his spare time he operates the;DefaultRisk.com website. References: Add references at CitEc Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:rsk:journ1:2160734 Access Statistics for this article More articles in Journal of Credit Risk from Journal of Credit Risk |
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