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38

 

Princeton University Press (Visit publisher website)

2004

English

 

 

 

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Credit risk, credit risk pricing models, default probability estimation, modeling dependent defaults, marginal credit risk, modeling correlation structure, correlation and dependence, homogeneous loan portfolios, basic mixed binomial model, choosing the mixing distribution using Merton`s model, contagion, equal correlations but different distributions, estimating default correlation through variation in frequencies, binomial approximation using diversity scores, buckets and multiple binomials, inhomogeneous collections and moment-generating functions, pure death process, asset-value correlation and intensity correlation, multivariate exponential distributions and common default-event factors, correlation through updating of latent variables, copula approach, Gaussian copula, network dependence

 

From the author - What affects the pricing of the tranches and the basket structures is therefore the varying correlation assumptions imposed on the models. Modeling various correlation structures that work with given marginal characteristics is a central focus of this chapter. A second worry in default swap valuation is whether the credit quality of the protection seller is heavily correlated with that of the underlying reference securities. Finally, dependence modeling is necessary in trying to understand the risk of simultaneous defaults by, for example, financial institutions. Such simultaneous defaults could affect the stability of the financial system with profound effects on the entire economy. We finish the chapter by looking at network dependence, which is a first step toward modeling simultaneous defaults from more fundamental quantities.

 

9.1 Some Preliminary Remarks on Correlation and Dependence
9.2 Homogeneous Loan Portfolios
9.3 Asset-Value Correlation and Intensity Correlation
9.4 The Copula Approach
9.5 Network Dependence
9.6 Bibliographical Notes

 

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*** From the publisher ***

Credit risk is today one of the most intensely studied topics in quantitative finance. This book provides an introduction and overview for readers who seek an up-to-date reference to the central problems of the field and to the tools currently used to analyze them. The book is aimed at researchers and students in finance, at quantitative analysts in banks and other financial institutions, and at regulators interested in the modeling aspects of credit risk.

David Lando considers the two broad approaches to credit risk analysis: that based on classical option pricing models on the one hand, and on a direct modeling of the default probability of issuers on the other. He offers insights that can be drawn from each approach and demonstrates that the distinction between the two approaches is not at all clear-cut. The book strikes a fruitful balance between quickly presenting the basic ideas of the models and offering enough detail so readers can derive and implement the models themselves. The discussion of the models and their limitations and five technical appendixes help readers expand and generalize the models themselves or to understand existing generalizations. The book emphasizes models for pricing as well as statistical techniques for estimating their parameters. Applications include rating-based modeling, modeling of dependent defaults, swap- and corporate-yield curve dynamics, credit default swaps, and collateralized debt obligations.