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Improvements in rating models for the German corporate sector

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  • Förstemann, Till
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    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. --

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    Bibliographic Info

    Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 2: Banking and Financial Studies with number 2011,11.

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    Date of creation: 2011
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    Handle: RePEc:zbw:bubdp2:201111

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    Related research

    Keywords: Credit Risk; Credit Rating; Probability of Default; Logistic Regression;

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