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http://hdl.handle.net/10419/50001
  

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dc.contributor.authorFörstemann, Tillen_US
dc.date.accessioned2011-09-27en_US
dc.date.accessioned2011-09-29T15:46:16Z-
dc.date.available2011-09-29T15:46:16Z-
dc.date.issued2011en_US
dc.identifier.isbn978-3-86558-745-9en_US
dc.identifier.urihttp://hdl.handle.net/10419/50001-
dc.description.abstractGroup-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.en_US
dc.language.isoengen_US
dc.publisherDeutsche Bundesbank Frankfurt am Mainen_US
dc.relation.ispartofseriesDiscussion Paper Series 2: Banking and Financial Studies 2011,11en_US
dc.subject.jelG21en_US
dc.subject.jelG33en_US
dc.subject.jelC52en_US
dc.subject.ddc330en_US
dc.subject.keywordCredit Risken_US
dc.subject.keywordCredit Ratingen_US
dc.subject.keywordProbability of Defaulten_US
dc.subject.keywordLogistic Regressionen_US
dc.titleImprovements in rating models for the German corporate sectoren_US
dc.typeWorking Paperen_US
dc.identifier.ppn668914521en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
dc.identifier.repecRePEc:zbw:bubdp2:201111-
Appears in Collections:Discussion Paper Series 2: Banking and Financial Studies, Deutsche Bundesbank

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