非正态数据下商业银行信用风险和经济资本度量

信用风险和经济资本度量是商业银行风险管理最重要的目标之一. 通过使用Johnson变换解决非正态数据情况下经济资本的计算问题, 克服以往研究中在Copula方法下进行Monte Carlo模拟时对正态或t分布要求的局限性, 将实际数据转换为标准正态分布, 非常方便地使用Monte Carlo模拟度量违约时间和计算经济资本. 研究结果表明, 基于Johnson变换下的Copula方法可行而且合理, 该研究为我国商业银行在《巴塞尔新资本协议》(Basel II)下进行有效的风险管理提供一些参考和新的思路.

Measuring credit risk and economic capital for commercial banks under non-Gaussian data

Measuring the credit risk and economic capital is one of the most important aims in risk management for commercial banks. The authors use Johnson transformation to solve the problem of economic capital under non-Gaussian data. By transforming real data to standard normal distribution, the authors overcome the restricts of Monte Carlo simulation in Copula method, which often requires the Gaussian or t distribution. It is convenient to simulate the correlated default time and the economic capital for banks. From the comparative analysis, the Johnson transformation is feasible and reasonable. This paper provides an effective quantitative method in risk management for commercial banks under "Basel Capital Accord" (Basel II) and the algorithm has a certain reference.