Evaluating credit risk models: A critique and a proposal
by Hergen Frerichs of the University of Frankfurt, and
Abstract: 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 can invalidate standard statistical inference. We proceed by suggesting the Berkowitz (2001) procedure, which relies on standard likelihood ratio tests performed on transformed loss data. We simulate the power of this approach in various settings including one in which the test is extended to incorporate cross-sectional information. Monte Carlo simulations show that a loss history of ten years can be sufficient to resolve uncertainties currently present in credit risk modeling.
Keywords: credit risk, backtesting, density forecasts, model validation, bank regulation.