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 alteative 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.
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 alteative 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.