Estimating Probabilities of Default With Support Vector Machines
AbstractThis 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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Bibliographic InfoPaper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2007-035.
Date of creation: Jun 2007
Date of revision:
Bankruptcy; Company rating; Default probability; Support vector machines.;
Other versions of this item:
- Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2007. "Estimating probabilities of default with support vector machines," Discussion Paper Series 2: Banking and Financial Studies 2007,18, Deutsche Bundesbank, Research Centre.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-06-11 (All new papers)
- NEP-BEC-2007-06-11 (Business Economics)
- NEP-CMP-2007-06-11 (Computational Economics)
- NEP-ECM-2007-06-11 (Econometrics)
- NEP-RMG-2007-06-11 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- repec:rus:hseeco:318682 is not listed on IDEAS
- Junni L. Zhang & Wolfgang Härdle, 2008.
"The Bayesian Additive Classification Tree Applied to Credit Risk Modelling,"
SFB 649 Discussion Papers
SFB649DP2008-003, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Zhang, Junni L. & Härdle, Wolfgang K., 2010. "The Bayesian Additive Classification Tree applied to credit risk modelling," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1197-1205, May.
- Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2007.
"The Default Risk of Firms Examined with Smooth Support Vector Machines,"
Discussion Papers of DIW Berlin
757, DIW Berlin, German Institute for Economic Research.
- Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2008. "The Default Risk of Firms Examined with Smooth Support Vector Machines," SFB 649 Discussion Papers SFB649DP2008-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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