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Evaluating Credit Risk Models: A Critique and a Proposal
Company: The Journal of Risk
Company Url: Click here to open
Year Of Publication: 2003
Month Of Publication: Summer
Pages: 1-23
Download Count: 1407
View Count: 7060
Comment Num: 0
Language: EN
Source:
Who Can Read: Free
Date: 12-23-2003
Publisher: Administrator
Summary
Evaluating the quality of credit portfolio risk models is an important issue for bothbanks and regulators. Lopez and Saidenberg (2000) suggest cross-sectionalresampling techniques in order to make efficient use of available data. We show thattheir proposal disregards cross-sectional dependence in resampled portfolios, whichrenders standard statistical inference invalid. We proceed by suggesting theBerkowitz (1999) procedure, which relies on standard likelihood ratio tests performedon transformed default data. We simulate the power of this approach in varioussettings including one in which the test is extended to incorporate cross-sectionalinformation. To compare the predictive ability of alternative models, we propose touse 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.
Author(s)
Frerichs, Hergen Sign in to follow this author
Loeffler, Gunter Sign in to follow this author
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