基于Bayesian-Copula方法的商业银行操作风险度量

Measurement of Operational Risk in Commercial Bank Based on Bayesian-Copula Method

本文在对损失分布法分析的基础上,将损失事件划分为内部欺诈、外部欺诈以及违规执行三种类型;引用两阶段分布拟合操作风险的损失强度分布,同时采用贝叶斯理论中的吉布斯抽样来获取参数估计值以减小低频率高损失数据不足带来的误差;考虑到操作风险各损失事件间可能存在的相关性,本文采用Copula函数对操作风险进行整合以获得联合损失分布函数,并计算出不同置信水平下我国商业银行操作风险损失的VaR值与CVaR值。实证研究的结果表明:基于贝叶斯理论的参数估计综合考虑了总体与样本等先验信息,估计出的参数值误差较小;Copula函数的引入与VaR值、CVaR值的测算,能在考虑了损失事件发生概率的同时,估测出操作风险潜在的损失大小,从而可以更准确度量操作风险。

Based on the analysis of loss distribution approach,loss events are divided into three types： internal fraud,external fraud and illegal operation.Then,we apply two-stage distribution to fit the loss intensity distribution of operational risk and use Gibbs sampling of Bayesian theory to obtain the parameter estimates,which can reduce error caused by the insufficient low-frequency and high-loss data.In view of the correlation between different types of operational risk loss,the copula function is applied to integrate the total loss distribution.Finally,we calculate VaR and CVaR for different confidence level of the operational risk of commercial banks in China.The empirical result shows that： Parameter estimation based on Bayesian theory takes into account a priori information such as population and sample information which can reduce the estimated error.The introduced copula function and measured value of VaR and CVaR not only consider the probability of loss events,but also can calculate potential losses of operational risk,so it can get a more accurate measurement result of operational risk.