Evaluating credit risk models : a critique and a proposal

  • Evaluating the quality of credit portfolio risk models is an important issue for both banks and regulators. Lopez and Saidenberg (2000) suggest cross-sectional resampling techniques in order to make efficient use of available data. We show that their proposal disregards cross-sectional dependence in resampled portfolios, which renders standard statistical inference invalid. We proceed by suggesting the Berkowitz (1999) procedure, which relies on standard likelihood ratio tests performed on transformed default data. We simulate the power of this approach in various settings including one in which the test is extended to incorporate cross-sectional information. To compare the predictive ability of alternative models, we propose to use either Bonferroni bounds or the likelihood-ratio of the two models. Monte Carlo simulations show that a default history of ten years can be sufficient to resolve uncertainties currently present in credit risk modeling.

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Author:Hergen Frerichs, Gunter Löffler
Document Type:Conference Proceeding
Year of Completion:2001
Year of first Publication:2001
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2014/09/17
Tag:backtesting,; bank regulation; credit risk; density forecasts; model validation
GND Keyword:Kreditrisiko; Portfoliomanagement; Gütefunktion; Parametertest; Signifikanzniveau; Statistischer Test; Testtheorie
Issue:Version: October 9, 2001
Page Number:36
EFMA 2001 Lugano Meetings
Institutes:Wirtschaftswissenschaften / Wirtschaftswissenschaften
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
JEL-Classification:C Mathematical and Quantitative Methods / C5 Econometric Modeling / C52 Model Evaluation and Selection
Licence (German):License LogoDeutsches Urheberrecht