TY  - RPRT
A1  - Frerichs, Hergen
A1  - Löffler, Gunter
T1  - Evaluating credit risk models: a critique and a new proposal
N2  - Evaluating the quality of credit portfolio risk models is an important question for both banks and regulators. Lopez and Saidenberg (2000) suggest cross-sectional resampling techniques in order to make efficient use of available data and to produce measures of forecast accuracy. We first show that their proposal disregards crosssectional dependence in simulated subportfolios, which renders standard statistical inference invalid. We proceed by suggesting another evaluation methodology which draws on the concept of likelihood ratio tests. Specifically, we compare the predictive quality of alternative models by comparing the probabilities that observed data have been generated by these models. The distribution of the test statistic can be derived through Monte Carlo simulation. To exploit differences in cross-sectional predictions of alternative models, the test can be based on a linear combination of subportfolio statistics. In the construction of the test, the weight of a subportfolio depends on the difference in the loss distributions which alternative models predict for this particular portfolio. This makes efficient use of the data, and reduces computational burden. Monte Carlo simulations suggest that the power of the tests is satisfactory.

JEL classification: G2; G28; C52
KW  - credit risk
KW  - backtesting
KW  - model validation
KW  - bank regulation
KW  - Kreditrisiko
KW  - Portfoliomanagement
KW  - Gütefunktion
KW  - Parametertest
KW  - Signifikanzniveau
KW  - Statistischer Test
KW  - Testtheorie
Y1  - 2001
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/34990
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-349902
UR  - http://ww2.odu.edu/bpa/efma/hfrerichs.pdf
IS  - Version February 14, 2001
ER  -