Improvements in rating models for the German corporate sector

Till Förstemann

No 2011,11, Discussion Paper Series 2: Banking and Financial Studies from Deutsche Bundesbank

Abstract: 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.

Keywords: Credit Risk; Credit Rating; Probability of Default; Logistic Regression (search for similar items in EconPapers)
JEL-codes: C52 G21 G33 (search for similar items in EconPapers)
Date: 2011
New Economics Papers: this item is included in nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/50001/1/668914521.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:bubdp2:201111

Access Statistics for this paper

More papers in Discussion Paper Series 2: Banking and Financial Studies from Deutsche Bundesbank Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().