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Forecasting Credit Portfolio Risk

by Alfred Hamerle of the Universitšt Regensburg,
Thilo Liebig of Deutsche Bundesbank, and
Harald Scheule of the Universitšt Regensburg

February 2004

Abstract: The main challenge of forecasting credit default risk in loan portfolios is forecasting the default probabilities and the default correlations. We derive a Merton-style threshold-value model for the default probability which treats the asset value of a firm as unknown and uses a factor model instead. In addition, we demonstrate how default correlations can be easily modeled. The empirical analysis is based on a large data set of German firms provided by Deutsche Bundesbank. We find that the inclusion of variables which are correlated with the business cycle improves the forecasts of default probabilities. Asset and default correlations depend on the factors used to model default probabilities. The better the point-in-time calibration of the estimated default probabilities, the smaller the estimated correlations. Thus, correlations and default probabilities should always be estimated simultaneously.

JEL Classification: C23, C41, G21.

Keywords: asset correlation, bank regulation, Basel II, credit risk, default correlation, default probability, logit model, probit model, time-discrete hazard rate.

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