A Study on Operational Risk and Credit Portfolio Risk Estimation Using Data Analytics*
构建了一个结合数据分析的结构化信用模型，用于分析信用组合的联合违约风险，并提出高效模拟方法估计违约概率和尾部概率，帮助管理者做出最优操作决策以对冲供应链风险。
ABSTRACT In this article we consider operational risk and use data analytics to estimate the credit portfolio risk. Specifically, we consider situations in which managers need to make the optimal operational decision on total provision for risk to hedge against the potential risk in the entire supply chain. We build a new structural credit model integrated with data analytics to analyze the joint default risk of credit portfolio. Our model enables the decision maker to better assess the risk of a supply chain, so that they could determine the optimal operational decisions with total provision for risk, and react in a timely manner to economic and environmental changes. We propose an efficient simulation method to estimate the default probability of the credit portfolio with the risk factors having the multivariate t ‐copula. Moreover, we develop a three‐step importance sampling (IS) method for the t ‐copula credit portfolio risk measurement model to achieve an accurate estimation of the tail probability of the credit portfolio loss distribution. We apply the Levenberg–Marquardt algorithm to estimate the mean‐shift vector of the systematic risk factors after the probability measure change. Besides, we empirically examine the changes in the credit portfolio risks of 60 listed Chinese firms in different industries using our proposed method. The results show that our model can help the decision maker make the optimal operational decisions with total provision for risk, which hedges against the potential risk in the entire supply chain.