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Improvements in rating models for the German corporate sector

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  • Förstemann, Till

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.

Suggested Citation

  • Förstemann, Till, 2011. "Improvements in rating models for the German corporate sector," Discussion Paper Series 2: Banking and Financial Studies 2011,11, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubdp2:201111
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    Cited by:

    1. Markus Behn & Rainer Haselmann & Paul Wachtel, 2016. "Procyclical Capital Regulation and Lending," Journal of Finance, American Finance Association, vol. 71(2), pages 919-956, April.
    2. Haselmann, Rainer & Schoenherr, David & Vig, Vikrant, 2017. "Rent-seeking in elite networks," SAFE Working Paper Series 132, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.

    More about this item

    Keywords

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

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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