在评级分类的断点模型中检查默认相关性和分数相关性

Daniel Tillich
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引用次数: 2

摘要

在信用风险中,不同信用等级的债务人被划分为不同的评级等级。一个问题是定义评级类的边界。从默认观察中估计这些断点的自然方法来自于变化点分析领域。为了解释债务人之间的依赖关系,文献提出了一个断点模型与单因素模型的组合。人们发现评级类别的阈值和相应的违约概率(也称为风险水平)的高度一致的估计。但对模型固有属性的研究至今仍缺失。因此,我们推导了默认相关性,并研究了它与模型参数的关系,即断点、风险水平,以及模拟研究中出现的一个新的相关项,称为得分相关性。最后,我们检查了模拟研究中使用的分数相关性的大小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Checking Default Correlation and Score Correlation in a Breakpoint Model for Rating Classification
Abstract In credit risk, debtors with different creditworthiness are divided into rating classes. One problem is to define the borders of the rating classes. A natural way to estimate these breakpoints from default observations comes out of the field of change point analysis. In order to account for dependency between the debtors, the literature proposes a combination of a breakpoint model with a one-factor model. One finds strongly consistent estimators for the threshold of the rating classes and the corresponding default probabilities, also called risk levels. But an investigation of the inherent model properties is as yet missing. For this reason we derive the default correlation and study its relationship to the model parameters, i.e., the breakpoint, the risk levels, and a new correlation term, named score correlation, appearing in a simulation study. Eventually, we check the magnitude of the score correlation used in the simulation study.
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