Introduction to Credit Risk Modeling, Second Edition

Contains Nearly 100 Pages of New Material

The recent financial crisis has shown that credit risk in particular and finance in general remain important fields for the application of mathematical concepts to real-life situations. While continuing to focus on common mathematical approaches to model credit portfolios, Introduction to Credit Risk Modeling, Second Edition presents updates on model developments that have occurred since the publication of the best-selling first edition.

New to the Second Edition

• An expanded section on techniques for the generation of loss distributions
• Introductory sections on new topics, such as spectral risk measures, an axiomatic approach to capital allocation, and nonhomogeneous Markov chains
• Updated sections on the probability of default, exposure-at-default, loss-given-default, and regulatory capital
• A new section on multi-period models
• Recent developments in structured credit

The financial crisis illustrated the importance of effectively communicating model outcomes and ensuring that the variation in results is clearly understood by decision makers. The crisis also showed that more modeling and more analysis are superior to only one model. This accessible, self-contained book recommends using a variety of models to shed light on different aspects of the true nature of a credit risk problem, thereby allowing the problem to be viewed from different angles.

Illustrating mathematical models for structured credit with practical examples, Introduction to Credit Risk Modeling provides an accessible introduction to the foundations of structured credit portfolio modeling. Updated and expanded, this second edition features additional material on estimation of asset correlations, benchmark correlations based on securitizations of benchmark portfolios in the market, risk contributions and spectral risk measures, nonhomogeneous Markov chain approaches, multi-year models, current agency models, single-tranche CDOs, index tranches, as well as new developments in synthetics. The text also includes new exercises and a supporting website.

Over the years, Christian Bluhm has worked for Deutsche Bank, McKinsey, HypoVereinsbank’s Group Credit Portfolio Management, and Credit Suisse. He earned a Ph.D. in mathematics from the University of Erlangen-Nürnberg.

Ludger Overbeck is a professor of probability theory and quantitative finance and risk management in the Institute of Mathematics at the University of Giessen. During his career, he worked for Deutsche Bundesbank, Deutsche Bank, HypoVereinsbank/UniCredit, DZBank, and Commerzbank. He earned a Ph.D. in mathematics from the University of Bonn.

Christoph Wagner has worked for Deutsche Bank, Allianz Group Center, UniCredit/HypoVereinsbank, and Allianz Risk Transfer. He earned a Ph.D. in statistical physics from the Technical University of Munich.