Estimating Probabilities of Default With Support Vector Machines

Wolfgang Karl Härdle, Rouslan Moro and Dorothea Schäfer ()

No SFB649DP2007-035, SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

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 G33 C45 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-bec, nep-cmp, nep-ecm and nep-rmg
Date: 2007-06
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Related works:
Working Paper: Estimating probabilities of default with support vector machines (2007)
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