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Princeton University Press (Visit publisher website)

2004

English

 

 

 

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Credit risk, default probability estimation, credit ratings and credit scoring, logistic regression, logit specification, probit specification, discriminant analysis, normality assumption of discriminant analysis, Altman`s Z-score, hazard regression, continuous-time survival analysis methods, Nelson-Aalen estimator, Markov chains and transition-probability estimation, a mover-stayer model, estimation of probability of rare transition, discrete-time methods vs. continuous-time methods, ordered probit method of estimating transition probabilities, cumulative accuracy profiles, misspecification tests

 

From the author - It is not always easy to build full structural models which include all the variables that empirically influence estimated default probabilities. The intensity models that we will turn to later try to include more variables in the default pricing, typically (but not necessarily) at the cost of making their influence exogenously specified. Before we enter into these models it is natural to look at some of the dominating methods used for default probability estimation. As we will see, the most natural statistical framework for analyzing defaults is also the natural framework for linking credit scoring with pricing. The focus of this section is on model structure. No properties of the estimators are proved.

 

4.1 Credit Scoring Using Logistic Regression
4.2 Credit Scoring Using Discriminant Analysis
4.3 Hazard Regressions: Discrete Case
4.4 Continuous-Time Survival Analysis Methods
4.5 Markov Chains and Transition-Probability Estimation
4.6 The Difference between Discrete and Continuous
4.7 AWord ofWarning on the Markov Assumption
4.8 Ordered Probits and Ratings
4.9 Cumulative Accuracy Profiles
4.10 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.