Estimating Default Correlations from Short Panels of Credit Rating Performance Data
by Michael B. Gordy of the Federal Reserve Board, and
January 29, 2002
Abstract: When models of portfolio credit risk are calibrated to historical ratings performance data, parameters that capture cross-obligor dependence can be (and often are) fit directly to estimated default correlations. The accuracy of our measures of credit value-at-risk therefore rests on the precision with which default correlations can be estimated. In practice, data are always scarce. The rating system may cover many obligors, but performance data span, at most, two or three decades. Nonetheless, the moments-based estimators commonly used by practitioners make minimal use of parameter restrictions. In this paper, we demonstrate that these estimators perform quite badly on the sample sizes typically available, and generally produce a large downward bias in estimated default correlations. Models calibrated in this manner are thus likely to understate value-at-risk quite significantly.