Princeton University Press, 2004. 320pp. Hardcover, US$65.00 (ISBN: 0-691-08929-9)
David Lando's book Credit Risk Modeling: Theory and Applications was popular in the rankings on www.amazon.com months before the book emerged from the publisher, Princeton University Press. Professor Lando's pedigree in credit risk research and analytics is impeccable, ranging from his PhD thesis on the topic under Robert Jarrow in 1994 to his recent publications with Darrell Duffie, who also acts as editor of the Princeton Series in Finance. For readers who are fans of Jarrow and Duffie, the attractions of a book by David Lando are obvious. But there is a lot to be said for Credit Risk Modeling: Theory and Applications even for those who - in the words of one anonymous reviewer on amazon.com- "are a regular Schmoe like myself". This book has a lot to offer readers of all perspectives, mathematician or not, academic or practitioner.
This is obvious in a lucid Chapter 1 of the book, where Lando lays out in clear prose the full range of credit modeling alternatives, starting with the 1973 Merton model of risky debt and its recent extensions, and ending with an overview of the reduced-form hazard rate modeling techniques for which Lando himself is best known. For any banker eager for a dispassionate view of the modeling alternatives, this chapter is a great starting point.
In Chapter 2, after a brief introduction to the Merton model of risky debt, Lando immediately begins to address the key areas in which the Merton model has been critiqued as too simplistic. He discusses the extension of the model to the case of random interest rates, a critical addition to the model if one wants to use it for any kind of fixed-income portfolio management. Lando then shows how the model can be enhanced to allow random "jumps" in asset values above and beyond those implied by the standard Black-Scholes assumptions. This extension is important in fitting short-term credit spreads because the standard Merton model has credit spreads that either approach zero as maturity shortens or explode to very high levels depending on the credit quality of the borrower. Lando shows what kind of assumptions are necessary to allow for multiple bond payments in a Merton context and how the concept of a "default barrier" can be implemented. He closes the chapter with a rich model that incorporates a combination of these features and with a discussion of parameter estimation for the model. For passionate fans of the Merton model, this chapter alone shows the potential for Merton implementations that go far beyond the usual commercial versions of the model.
Chapter 3 is a gold mine of techniques for those who wish to use the Merton model for capital allocation for performance measurement of financial institutions. Lando shows how to extend the model to allow for the trade-off between the tax shelter of taxdeductible bond coupons and the costs of bankruptcy, the key to arriving at an "optimal" capital structure. The commercial versions of the Merton model, by contrast, assume an exogenous capital structure that is highly variable, since the amount of debt outstanding does not vary as company asset values change. Lando also shows how a Merton model variant based on random movements in earnings before interest and taxes can be implemented.
In Chapter 4, Lando introduces logistic regression and discriminant analysis as statistical techniques for estimating default probabilities. He continues with a discussion of the use of transition matrices for modeling credit quality migration, a topic for which Lando is well known in a paper written with Robert Jarrow and Stuart Turnbull (1997). Chapter 5 will be essential for practitioners who are new to the "intensity-based", or reduced-form, credit modeling approach. Although here the mathematics ticks up a notch, the English prose alone is more than enough to make this book worthwhile. Lando summarizes the virtues of the reduced-form approach relative to the Merton or structural approach in a very clear way.
Chapter 6 is devoted to ratings-based credit risk modeling, while Chapter 7 focuses on counterparty credit risk assessment with a special emphasis on the interest rate swap market. These are of interest to a much narrower interest group than the rest of the book. Chapter 8 provides an introduction to credit default swaps, collateralized debt obligations and related products.
Chapter 9 closes the book with a discussion on modeling correlated defaults with each of the modeling technologies presented in the book. In addition, the four appendices offer a rich tool-set of formulae and techniques which will be of recurring interest to quants in the credit risk area. Authors in the field of credit risk, particularly authors of quantitative credit risk books like this one, have been subjected to some humorous reviews on amazon.com. This is true of both the Lando book and its closest peers Schönbucher (Credit derivatives pricing models, John Wiley & Sons, 2003), Bluhm, Overbeck and Wagner (An introduction to credit risk modeling, Chapman & Hall/CRC,m2003), and Duffie and Singleton (Credit risk:pricing, measurement, and management, Princeton University Press 2003). In every case, the authors are schizophrenically criticized for being too quantitative or not quantitative enough, depending on the perspective of the self-appointed critics. These critiques miss the point and the key value of Lando's book - that in one volume the reader gets a very comprehensive introduction to credit risk analytics at any level of mathematical sophistication (for the plain English view, skip the math and read the prose). Lando is particularly successful whether one reads the math or not - he shows how much more choice there is in credit risk modeling than in the 30-year-old Merton technology. Most importantly, he motivates the reader with a quantitative bent to go to the original academic works, well armed, for the micromathematical detail.
I personally have all the books mentioned in the previous paragraph on my desk at all times. They are a great complement to the very successful overview of credit risk by Caouette, Altman and Narayanan (2003) and to risk management-oriented credit books like van Deventer and Imai (2003) or van Deventer, Imai and Mesler (2004). The second edition of the Lando book could be strengthened with some more on credit spreads and the smoothing of credit spread curves. Regarding the New Capital Accords from the Basel Committee on Banking Supervision ("Basel II"), a chapter on the quantitative performance of credit models would also bring some additional strength to Lando's invaluable book. The best compliment to an author is to let him know you plan to buy the second edition, even though you already have the first edition- I certainly will.
Donald R. van Deventer
Chairman and Chief Executive Officer, Kamakura Corporation
Donald van Deventer is author of Financial risk management in banking (1993, with Dr Dennis Uyemura), Financial risk analytics (1996, with Kenji Imai), Credit risk models and the Basel Accords (2003, with Kenji Imai), and Advanced financial risk management (2004, with Kenji Imai and Mark Mesler).