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Mehrperiodenausfallprognose eines Bankportfolios aus deutschen mittelständischen Unternehmen

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Wolter, M., Rösch, D. Mehrperiodenausfallprognose eines Bankportfolios aus deutschen mittelständischen Unternehmen. Credit and Capital Markets – Kredit und Kapital, 45(2), 189-217. https://doi.org/10.3790/kuk.45.2.189
Wolter, Marcus and Rösch, Daniel "Mehrperiodenausfallprognose eines Bankportfolios aus deutschen mittelständischen Unternehmen" Credit and Capital Markets – Kredit und Kapital 45.2, 2012, 189-217. https://doi.org/10.3790/kuk.45.2.189
Wolter, Marcus/Rösch, Daniel (2012): Mehrperiodenausfallprognose eines Bankportfolios aus deutschen mittelständischen Unternehmen, in: Credit and Capital Markets – Kredit und Kapital, vol. 45, iss. 2, 189-217, [online] https://doi.org/10.3790/kuk.45.2.189

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Mehrperiodenausfallprognose eines Bankportfolios aus deutschen mittelständischen Unternehmen

Wolter, Marcus | Rösch, Daniel

Credit and Capital Markets – Kredit und Kapital, Vol. 45 (2012), Iss. 2 : pp. 189–217

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Marcus Wolter, Leibniz Universität Hannover, Institut für Banken und Finanzierung, Königsworther Platz 1, D-30167 Hannover

Prof. Dr. Daniel Rösch, Leibniz Universität Hannover, Institut für Banken und Finanzierung, Königsworther Platz 1, D-30167 Hannover

Abstract

Multi-Period Loss Forecasting for a Bank Portfolio Composed of German Mid-Sized Enterprise Stocks

This article analyses a comprehensive set of data including annual financial statements and default probability information relating to German small and medium-sized enterprises. This data set, which must be deemed typical of a business-customer credit portfolio of a large bank, is used as basis for developing an enterprise-specific default probability forecasting model. This model permits to identify significant company-specific and macroeconomic risk drivers and to forecast default probability risks over a multi-annual horizon. On the basis of the time-specific modes of behaviour of the default probabilities so ascertained, multi-period portfolio loss distributions have been estimated for bank-specific credit portfolios. The analyses are based on a data set relating to 5,930 German small and mediumsized enterprises. For these enterprises, a total of over 23,000 annual financial statements relating to the period 2002/2007 have been analysed. The results may be used as a basis on which to develop action strategies allowing credit portfolio losses to be more realistically estimated for several periods.