应用CoGVaR方法度量系统风险贡献

Measuring systemic risk contribution with CoGVaR approach

基于系统风险度量的角度,提出了一类新的条件风险度量——广义条件风险价值(CoGVaR).这类新的风险度量是条件分位数的自然广义化,它包括了经典的CoVaR.相较于经典的条件风险价值(CoVaR)和条件expectile(CoExpectile),它在实际中有着更好的应用价值,这一优势来源于它考虑了决策者的风险态度,而这一点目前为止并没有被其他工作关注过.使用加入状态变量的广义分位数回归方法,在道琼斯美国金融指数实例中给出了具体的计算结果,发现这类风险度量为系统风险贡献的度量提供了一种新的角度.除此之外,这一结果也显示了该风险度量能够通过使用更凸的负效用函数来更好地捕捉尾部风险.

We propose a new conditional risk measure,conditional generalized value-at-risk(CoGVaR),from the perspective of measuring systemic risk.The new class of risk measures is a natural generalization of the conditional quantiles including the classic CoVaR.Compared with the classic conditional value-at-risk(CoVaR)and conditional expectile(CoExpectile),it has more potential application in reality as it takes the risk attitude of the decision maker into consideration,which has not been the focus of much study to date.Using generalized quantile regression approach with state variables added,some calculation results are presented in the Dow Jones U.S.Financials Index case,and it is shown that it provides a new perspective on systemic risk contribution.In addition,the result shows that our risk measure can capture the tail risk by using more convex disutility function.