Quantitative Validation of Rating Models for Low Default Portfolios through Benchmarking

The new capital adequacy framework (Basel II) is one of the most fiercely debated topics the financial sector has seen in the recent past. Following a consultation process that lasted several years, the regulations formally took effect on January 1, 2007. The advanced approaches (the advanced internal ratings-based, or A-IRB, approach and the advanced measurement approach, or AMA) are scheduled to become operational on January 1, 2008. The new framework allows banks to use the IRB approach for the calculation of the assessment base for credit risk. Use of the IRB approach is subject to regulatory approval, which can only be obtained if the internal rating systems meet certain requirements. One of these requirements is that the models employed must have good predictive power. Banks must review this predictive power once a year by performing a qualitative and quantitative validation of the models. The statistical methods used to perform quantitative validation require a significant amount of default data to derive valid statements about the model, but such data are typically scarce in the case of rating models for so-called low default portfolios (LDPs), i.e. portfolios for which banks have little default history. In this paper, we first deal with the general problems of LDPs under the IRB approach and cover the problems of validating rating models for LDPs. We then present an alternative method for the quantitative validation of such models, based on the idea of benchmarking. Finally, we provide an example of the application of the proposed validation method.