Forecasting Credit Portfolio Risk

By Alfred Hamerle, Thilo Liebig, Harald Scheule. Social Science Research Network, 2004

126 citations recorded by the source corpus.

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.

Subfields: Accounting, Finance, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/c3e21314100a8e67ca9c8c9e735f3b9bcf61aa5d

Citations in the corpus

• Credit Risk Drivers: Evaluating the Contribution of Firm Level Information and of Macroeconomic Dynamics (2009, unknown): “The theoretical modelling setup underlying the empirical analysis which will be developed in Section 4 draws to some extent on previous work done by Rosch (2003) and Hamerle et al (2004).”