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Estimating Probabilities of Default With Support Vector Machines

  • Wolfgang Härdle
  • Rouslan Moro
  • Dorothea Schäfer

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 German 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://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2007-035.pdf
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Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2007-035.

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Length: 24
Date of creation: Jun 2007
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2007-035
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  1. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2007. "Learning, Structural Instability, and Present Value Calculations," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 253-288.
  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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