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arXiv 2009.12092q-fin.RMq-fin.ST
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基于Copula的信用风险分析因子模型

Copula-Based Factor Model for Credit Risk Analysis

Meng-Jou Lu, Cathy Yi-Hsuan Chen, Wolfgang Karl Härdle

摘要:

评估信用风险的标准定量方法采用基于联合多元正态分布特性的因子模型。通过扩展单因子高斯Copula模型,将状态依赖的回收率引入到条件因子加载中,并通过共享唯一的公共因子来对它们进行建模,以实现更准确的违约预测。公因子同时控制违约率和回收率,并隐含地建立了它们之间的关联。根据巴塞尔协议III,本文表明,在繁忙时期,违约的趋势更多地取决于系统风险,而不是特质风险。在所考虑的模型中,具有随机因子加载和状态依赖的恢复率的模型在违约预测上是最优越的。

英文摘要:

A standard quantitative method to access credit risk employs a factor model based on joint multivariate normal distribution properties. By extending a one-factor Gaussian copula model to make a more accurate default forecast, this paper proposes to incorporate a state-dependent recovery rate into the conditional factor loading, and model them by sharing a unique common factor. The common factor governs the default rate and recovery rate simultaneously and creates their association implicitly. In accordance with Basel III, this paper shows that the tendency of default is more governed by systematic risk rather than idiosyncratic risk during a hectic period. Among the models considered, the one with random factor loading and a state-dependent recovery rate turns out to be the most superior on the default prediction.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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