Estimating probabilities of default with support vector machines

Wolfgang Härdle, Rouslan A. Moro and Dorothea Schäfer

No 2007-035, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

Abstract: 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.

Keywords: Bankruptcy; Company rating; Default probability; Support vector machines (search for similar items in EconPapers)
JEL-codes: C14 C45 G33 (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (1)

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Working Paper: Estimating probabilities of default with support vector machines (2007)
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