TAILIEUCHUNG - Forecasting Credit Portfolio Risk - Discussion Paper Series 2: Banking and Financial Supervision No 01/2004

The reverse is true of collection agencies, which provide information to the repositories, but do not use credit data to evaluate consumer creditworthiness, although they may use information in credit reports to locate debtors. Repositories also obtain information by requesting it from public records and government entities and when certain government entities report directly to the repositories, such as for delinquent child or family support payments, unpaid parking tickets, or overpayments of unemployment benefits. Information from collection agencies and public records is primarily derogatory information, such as when an account was sent to collection, or a bankruptcy. | DEUTSCHE BUNDESBANK Forecasting Credit Portfolio Risk Alfred Hamerle Universitat Regensburg Thilo Liebig Deutsche Bundesbank Harald Scheule Universitat Regensburg Discussion Paper Series 2 Banking and Financial Supervision No 01 2004 Discussion Papers represent the authors personal opinions and do not necessarily reflect the views of the Deutsche Bundesbank or its staff. Editorial Board Heinz Herrmann Thilo Liebig Karl-Heinz Todter Deutsche Bundesbank Wilhelm-Epstein-Strasse 14 60431 Frankfurt am Main Postfach 10 06 02 60006 Frankfurt am Main Tel 49 69 9566-1 Telex within Germany 41227 telex from abroad 414431 fax 49 69 5601071 Please address all orders in writing to Deutsche Bundesbank Press and Public Relations Division at the above address or via fax No 49 69 9566-3077 Reproduction permitted only if source is stated. ISBN 3-935821-82-4 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. Keywords asset correlation bank regulation Basel II credit risk default correlation default probability logit model probit model time-discrete hazard rate JEL classification C23 C41 .

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