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Estimating probabilities of default with support vector machines

  • Härdle, Wolfgang Karl
  • Moro, Rouslan A.
  • Schäfer, Dorothea

This paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of testing our approach on Deutsche Bundesbank data. In particular we discuss the selection of variables and give a comparison with more traditional approaches such as discriminant analysis and the logit regression. The results demonstrate that the SVM has clear advantages over these methods for all variables tested.

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File URL: http://econstor.eu/bitstream/10419/19777/1/200718dkp_b_.pdf
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Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 2: Banking and Financial Studies with number 2007,18.

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Date of creation: 2007
Date of revision:
Handle: RePEc:zbw:bubdp2:6930
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  1. Pesaran, Mohammad Hashem & Pettenuzzo, Davide & Timmermann, Allan, 2006. "Learning, structural instability and present value calculations," Discussion Paper Series 1: Economic Studies 2006,27, Deutsche Bundesbank, Research Centre.
  2. Taylor, Mark P. & Schmidt, Markus & Reitz, Stefan, 2007. "End-user order flow and exchange rate dynamics," Discussion Paper Series 1: Economic Studies 2007,05, Deutsche Bundesbank, Research Centre.
  3. repec:rus:hseeco:318682 is not listed on IDEAS
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