Efficiently Weighted Estimation of Tail and Interquantile Expectations
提出一个半参数联合估计量，用于估计尾部期望和分位数间期望，无需指定条件分布，可检验条件分布异质性，尤其关注尾部行为差异，在金融组合策略中检验尾部事件对平均异常收益的非比例贡献。
Abstract Tail expectations have recently attracted much attention in economics for their ability to capture risk. We develop a semiparametric estimator for the joint estimation of (nonlinear) models of tail expectations with some tail quantile as the left or right threshold, and interquantile expectations, partial expectations between two thresholding quantiles. The joint estimator of these quantities can be used to test for heterogeneity in the conditional distribution, with special attention to distinct tail behavior. We derive efficient weights and asymptotic properties of the estimator for time-series data. The estimator does not require the specification of the conditional distribution, and its computation relies on standard techniques. In an empirical application in finance, we test for a disproportionate contribution of tail events to the average abnormal return of portfolio strategies.