论文标题
基于Copula的信用风险分析模型
Copula-Based Factor Model for Credit Risk Analysis
论文作者
论文摘要
访问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素模型。通过将一因素高斯模型扩展以进行更准确的默认预测，本文提议将依赖于状态的恢复速率纳入条件因子加载中，并通过共享独特的共同因素进行建模。共同因素同时控制默认率和恢复率，并隐含地创建其关联。根据巴塞尔三世（Basel III），本文表明，默认的趋势更受系统性风险而不是特殊风险支配。在考虑的模型中，具有随机因子加载和状态依赖的恢复率的模型被证明是默认预测最优越的。
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.
