Improvements in rating models for the German corporate sector

Arbeitspapier

Group-specific estimations can significantly improve the predictive power of accountingbased rating models. This is shown using a binary logistic regression model applied to the Deutsche Bundesbank's USTAN dataset, which contains 300,000 financial statements provided by German companies for the years 1994 to 2002, i. e. throughout a complete business-cycle. The robustness and the representability of this result is verified through out-of-sample tests and through comparisons with a benchmark model which applies the variables of Moody's RiskCalcTM for Germany.

Object Details

• ISBN: 978-3-86558-745-9
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Discussion Paper Series 2 ; No. 2011,11

Classification and Topics

• Classification: Wirtschaft ; Banks; Depository Institutions; Micro Finance Institutions; Mortgages ; Bankruptcy; Liquidation ; Model Evaluation, Validation, and Selection
• Subject: Credit Risk ; Credit Rating ; Probability of Default ; Logistic Regression

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Förstemann, Till
• Event: Veröffentlichung
• (who): Deutsche Bundesbank
• (where): Frankfurt a. M.
• (when): 2011

Further information

• Handle: http://hdl.handle.net/10419/50001
• Last update: 10.03.2025, 11:43 AM CET