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Estimating probabilities of default for German savings banks and credit cooperatives

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  • Porath, Daniel

Abstract

A healthy banking system is a fundamental condition for financial stability. When assessing the riskiness of the banking system, analysts often restrict their focus to large banks. This may create a distorted picture in countries like Germany with fragmented banking systems. In Germany, savings banks and cooperative banks taken together are important players in the market. However, little is known about their default risk. The reason is that these banks usually resolve financial distress within their own organisations, which means defaults are not observable from the outside. In this paper we use a new dataset which contains information about financial distress and financial strength of all German savings banks and cooperative banks. The data have been gathered by the Deutsche Bundesbank for microprudential supervision and have never before been exploited for macroprudential purposes. We use the data to identify the main risk drivers. To this end we estimate a default prediction model (hazard model). A second goal of the paper is to analyse the impact of macroeconomic information for forecasting banks' defaults. Recent findings for the USA have cast some doubt on the usefulness of macroeconomic information for banks' risk assessment. Contrary to recent literature, we find that macroeconomic information significantly improves default forecasts.

Suggested Citation

  • Porath, Daniel, 2004. "Estimating probabilities of default for German savings banks and credit cooperatives," Discussion Paper Series 2: Banking and Financial Studies 2004,06, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubdp2:4255
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    References listed on IDEAS

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    1. Hamerle, Alfred & Liebig, Thilo & Scheule, Harald, 2004. "Forecasting Credit Portfolio Risk," Discussion Paper Series 2: Banking and Financial Studies 2004,01, Deutsche Bundesbank.
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    1. Koetter, M. & Bos, J.W.B. & Heid, F. & Kolari, J.W. & Kool, C.J.M. & Porath, D., 2007. "Accounting for distress in bank mergers," Journal of Banking & Finance, Elsevier, vol. 31(10), pages 3200-3217, October.
    2. M. Koetter, 2005. "Evaluating the German Bank Merger Wave," Working Papers 05-16, Utrecht School of Economics.
    3. Martin Kukuk & Michael Rönnberg, 2013. "Corporate credit default models: a mixed logit approach," Review of Quantitative Finance and Accounting, Springer, vol. 40(3), pages 467-483, April.
    4. Buchholst, Birgitte Vølund & Rangvid, Jesper, 2013. "Leading indicators of distress in Danish banks in the period 2008-12," Nationaløkonomisk tidsskrift, Nationaløkonomisk Forening, vol. 2013(2), pages 176-206.
    5. Daniel Porath, 2006. "Estimating probabilities of default for German savings banks and credit cooperatives," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 58(3), pages 214-233, July.
    6. Saeed, Momna & Izzeldin, Marwan, 2016. "Examining the relationship between default risk and efficiency in Islamic and conventional banks," Journal of Economic Behavior & Organization, Elsevier, vol. 132(S), pages 127-154.
    7. Fabrizio Ferriani & Wanda Cornacchia & Paolo Farroni & Eliana Ferrara & Francesco Guarino & Francesco Pisanti, 2019. "An early warning system for less significant Italian banks," Questioni di Economia e Finanza (Occasional Papers) 480, Bank of Italy, Economic Research and International Relations Area.

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    More about this item

    Keywords

    bank failure; default probability; time-discrete hazard rate;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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