Estimating Probabilities of Default With Support Vector Machines
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.
|Date of creation:||Jun 2007|
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- 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.
- Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006.
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IEPR Working Papers
06.42, Institute of Economic Policy Research (IEPR).
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- M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Learning, Structural Instability and Present Value Calculations," CESifo Working Paper Series 1650, CESifo Group Munich.
- Pesaran, M.H. & Pettenuzzo, D. & Timmermann, A., 2006. "Learning, Structural Instability and Present Value Calculations," Cambridge Working Papers in Economics 0602, Faculty of Economics, University of Cambridge.
- Pesaran, Mohammad Hashem & Pettenuzzo, Davide & Timmermann, Allan, 2006. "Learning, structural instability and present value calculations," Discussion Paper Series 1: Economic Studies 2006,27, Deutsche Bundesbank, Research Centre.
- M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Learning, structural instability and present value calculations," Computing in Economics and Finance 2006 529, Society for Computational Economics.
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