用支持向量机估计违约概率

W. Härdle, R. Moro, Dorothea Schaefer
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引用次数: 27

摘要

本文提出了一种基于非线性分类方法、支持向量机和将评级分数映射到违约概率的非参数技术的评级方法。我们介绍了基本的统计模型,并代表了在德意志联邦银行数据上测试我们的方法的结果。我们特别讨论了变量的选择,并与更传统的方法,如判别分析和逻辑回归进行了比较。结果表明,对于所有测试变量,支持向量机都具有明显的优势。
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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 Deutsche 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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