by Christian Bluhm, Ludger Overbeck, Christoph Wagner
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Product Description
In today's increasingly competitive financial world, successful risk management, portfolio management, and financial structuring demand more than up-to-date financial know-how. They also call for quantitative expertise, including the ability to effectively apply mathematical modeling tools and techniques. An Introduction to Credit Risk Modeling supplies both the bricks and the mortar of risk management. In a gentle and concise lecture-note style, it introduces the fundamentals of credit risk management, provides a broad treatment of the related modeling theory and methods, and explores their application to credit portfolio securitization, credit risk in a trading portfolio, and credit derivatives risk. The presentation is thorough but refreshingly accessible, foregoing unnecessary technical details yet remaining mathematically precise. Whether you are a risk manager looking for a more quantitative approach to credit risk or you are planning a move from the academic arena to a career in professional credit risk management, An Introduction to Credit Risk Modeling is the book you've been looking for. It will bring you quickly up to speed with information needed to resolve the questions and quandaries encountered in practice.
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Average Customer Review:
3 of 5 people found the following review helpful:
 Excellent introduction, 2007-12-02
Not only because of the Basel II Accords but also because of the enormous impact of credit derivatives, collateralized debt obligations, and other forms of credit risk management, the modeling of credit risk has become a multi-million dollar industry. The current credit crisis has certainly pointed to the need for more powerful credit risk modeling, to a degree where most of the concepts used to this date must be discarded or at least radically revised. The challenge will be to find algorithms and reasoning patterns that can be straightforwardly implemented and have the capability of monitoring, acquiring, and modeling accurately the credit markets in a manner that is independent of the time scale of the credit transactions.
Although short in comparison to most books on financial modeling and financial engineering, this book gives a sound overview of some of the more popular approaches to credit risk modeling, with references included for those readers who need more details. The authors' emphasis is on the conceptual background, and so a lot of the more straightforward computational routines are left out. In particular, even though the authors mention Monte Carlo simulations they are not performed explicitly in the book. Proofs of some of the main results are also left to the references. In addition, it is a monograph rather than a textbook, so no exercises will be found. Instructors who intend to use the book in an actual course on credit risk modeling will have to devise their own.
The book's unique feature, and one that it is widely cited for, is that it includes detailed discussion of correlated defaults. Some other features of the book that make it stand out include: 1. For those interested in Basel II, included is a discussion of the `exposure at default' (EAD), which is defined simply as the sum of the outstanding balances and a fraction of the loan commitments. This fraction is the expectation value of the random variable that governs the fluctuations of the utilization of the loan commitment. 2. The discussion of `copula functions' and their use in risk management. A copula is simply a multivariate distribution that is constructed so that it has standard uniform marginal distributions. Their use has exploded in credit risk modeling, mostly in the area of credit derivatives, which the author devotes a chapter to in the book. The authors discuss their use in the CreditMetrics/KMV model, with emphasis on the ability of copulas to manipulate the tail dependence of multivariate distributions. The authors point to the need for a calibration methodology for fitting copulas to credit portfolios. 3. The superb discussion on alternative risk measures via the concept of a `coherent' risk measure. Most interesting is the discussion on `expected shortfall' and its comparison with VAR (which is not a coherent risk measure). Also of great interest, and very well written, is the treatment of the variance/covariance approach, which as the authors remark is similar to beta-factor models and can be validated in much the same way. 4. The treatment of the term structure of default probabilities, which follows in much the same way the methodologies and concepts in interest rate modeling. The authors' discussions of this topic lead one to believe that a kind of `unified' theory of credit and interest rate modeling is viable, and in fact there are signs in some commercial products, such as CreditMetrics/KMV, of this unification.
3 of 4 people found the following review helpful:
 read this before going for it, 2007-04-23
Well first off I would like to tell anyone who doesn't have a solid working knowledge of calculus (including multivariate) to avoid this book as it requires multiple integrals and infinite series and sequences. Now onto the good and the bad:
THE GOOD:
This text explains concepts very well and is FULL of examples. I mean literally 3/4 of the book, maybe more, is examples. Every chapter also has a section of problems that have partial solutions, which can come in very handy. This is pretty much all that is good about this text, but keep in mind that explaination is the most important part of any textbook.
THE BAD:
The proofs skip plenty of steps. And I mean plenty, so much that a proof in the book would take 5 lines but when my professor proved it in class it would take him nearly 15. Also while there are tonnes of examples, too many are theoretical and very hard. The book costs a hefty amount of change and is suprisingly small, Author couldl have given few more examples to make it interesting. However the worst thing about this book is how the author leaves important things in with the text often. However most these things are small, and overall the text is a good intro to probability theory.
6 of 6 people found the following review helpful:
a very good book, 2006-10-31
The authors wanted to write the book that they themselves would have liked to read before starting a profession in risk management. I am working for a treasury consultancy firm. This book was the best of the five I bought. The text is very clear yet does not assume too much prior knowledge. It covers theory as well as industry practice. The book contains much advanced statistics and readers must have some background in order to handle this. The authors keep it simple but not too simple. Their approach is pragmatic throughout. I am really happy to have read this book when I started doing work in credit risk management.
0 of 3 people found the following review helpful:
good combination of math and finance, 2006-02-22
As indicated on the back of the book, the authors are aiming at audience who have some knowledge in both math and finance but may be weak in one and strong in another. Either way, this is a good book to read on credit risk.
2 of 3 people found the following review helpful:
Clear and comprehensive, 2005-10-26
This book clearly articulates basic concepts of credit risk modeling. At the same time it is mathematically rigorous. This book enables non mathematician with some (basic) knowledge in probability statistic to better understand and develop his risk management skills.
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