TERES: Tail Event Risk Expectile Shortfall
提出一种基于期望值的期望短缺广义风险度量，通过混合高斯和拉普拉斯密度设计，利用期望值与期望短缺的解析关系推导插件估计量，并在美、德、英股市及纳斯达克蓝筹股上验证了不同时间频率和风险水平下的有效性。
We propose a generalized risk measure for expectile-based expected shortfall estimation. The generalization is designed with a mixture of Gaussian and Laplace densities. Our plug-in estimator is derived from an analytic relationship between expectiles and expected shortfall. We investigate the sensitivity and robustness of the expected shortfall to the underlying mixture parameter specification and the risk level. Empirical results from the US, German and UK stock markets and for selected NASDAQ blue chip companies indicate that expected shortfall can be successfully estimated using the proposed method on a monthly, weekly, daily and intra-day basis using a 1-year or 1-day time horizon across different risk levels.