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Rating Based Modeling of Credit Risk: Theory and Application of Migration Matrices (Anglais) Relié – 15 janvier 2009

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Revue de presse

"... an excellent overview of theory and application...."
―Frank J. Fabozzi, PhD, CFA, Professor in the Practice of Finance, Yale School of Management, CT

Présentation de l'éditeur

In the last decade rating-based models have become very popular in credit risk management. These systems use the rating of a company as the decisive variable to evaluate the default risk of a bond or loan. The popularity is due to the straightforwardness of the approach, and to the upcoming new capital accord (Basel II), which allows banks to base their capital requirements on internal as well as external rating systems. Because of this, sophisticated credit risk models are being developed or demanded by banks to assess the risk of their credit portfolio better by recognizing the different underlying sources of risk. As a consequence, not only default probabilities for certain rating categories but also the probabilities of moving from one rating state to another are important issues in such models for risk management and pricing.
It is widely accepted that rating migrations and default probabilities show significant variations through time due to macroeconomics conditions or the business cycle. These changes in migration behavior may have a substantial impact on the value-at-risk (VAR) of a credit portfolio or the prices of credit derivatives such as collateralized debt obligations (D+CDOs). In this book the authors develop a much more sophisticated analysis of migration behavior. Their contribution of more sophisticated techniques to measure and forecast changes in migration behavior as well as determining adequate estimators for transition matrices is a major contribution to rating based credit modeling.

*Internal ratings-based systems are widely used in banks to calculate their value-at-risk (VAR) in order to determine their capital requirements for loan and bond portfolios under Basel II
*One aspect of these ratings systems is credit migrations, addressed in a systematic and comprehensive way for the first time in this book
*The book is based on in-depth work by Trueck and Rachev,

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Amazon.com: 4.5 étoiles sur 5 2 commentaires
2 internautes sur 2 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Okay, a little dense for something fairly simple. 23 octobre 2010
Par Bachelier - Publié sur Amazon.com
Format: Relié
This is an okay book and a thorough round up of Markov methods of bond risk management and pricing. It is a little dense for something fairly simple.

Simon Benninga's "Financial Modeling with Excel" has a single chapter with examples that covers the basic thesis here. Which is: if you do an iterated Markov chain of a bond using the historical transition probability matrix from Moody's or S&P you can get a good picture for a *portfolio* of bonds and 1) the portfolio's current value, and 2) the risk profile of the portfolio. You can also test "what if" scenarios under differing yield curves.

Wit that said, the authors are mathematicians, and a lot is said with formulas which would have been better said with 1)Excel and VBA code, or 2) Matlab code, or 3) a CD-ROM with examples.

Still, this is a thorough treatment of the subject and the best book there is on rating-based portfolio and risk analysis of bonds. The knotty question of whether one "BB" rating is strictly and robustly comparable to another "BB" is left to Moody's and S&P to defend (bucket-based ratings are always under attack, horizons are not comparable for same-rated bonds in differing industries, for example, etc.).
5.0 étoiles sur 5 the bible 12 avril 2013
Par euroarc - Publié sur Amazon.com
Format: Relié
For me this book by Stefan Trueck is simply a must have. Stepping away from the convenient path and common behavior of using the rating of a company as the decisive variable to evaluate the default risk of loans is bold and eye-opening. Its just true: the simple answers are rarely the correct answers. This book will help everybody to understand state-of-the-art risk management.
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