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Estimating Probabilities of Default With Support Vector Machines

Author

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  • Wolfgang Härdle
  • Rouslan Moro
  • Dorothea Schäfer

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.

Suggested Citation

  • Wolfgang Härdle & Rouslan Moro & Dorothea Schäfer, 2007. "Estimating Probabilities of Default With Support Vector Machines," SFB 649 Discussion Papers SFB649DP2007-035, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2007-035
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    References listed on IDEAS

    as
    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.
    3. repec:rus:hseeco:318682 is not listed on IDEAS
    4. Strotmann, Harald & Döpke, Jörg & Buch, Claudia M., 2006. "Does trade openness increase firm-level volatility?," Discussion Paper Series 1: Economic Studies 2006,40, Deutsche Bundesbank.
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    Cited by:

    1. Jakubik, Petr & Moinescu, Bogdan, 2015. "Assessing optimal credit growth for an emerging banking system," Economic Systems, Elsevier, vol. 39(4), pages 577-591.
    2. Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2009. "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 512-534.
    3. 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.
    4. Nehrebecka Natalia, 2018. "Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(2), pages 54-73, June.
    5. 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.
    6. Natalia Nehrebecka, 2021. "Internal Credit Risk Models and Digital Transformation: What to Prepare for? An Application to Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 3), pages 719-736.
    7. Tyler Pike & Horacio Sapriza & Tom Zimmermann, 2019. "Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning," Finance and Economics Discussion Series 2019-070, Board of Governors of the Federal Reserve System (U.S.).

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    More about this item

    Keywords

    Bankruptcy; Company rating; Default probability; Support vector machines.;
    All these keywords.

    JEL classification:

    • 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

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