High-Frequency Tail Risk Premium and Stock Return Predictability
提出一种基于高频数据恢复的最小距离状态价格密度来度量市场收益尾部风险溢价，发现该溢价能预测市场权益和方差风险溢价以及按特征排序的投资组合的超额收益，并区分了尾部风险数量与尾部溢价对指数未来分布的不同作用。
Abstract We propose a novel measure of the market return tail risk premium based on minimum-distance state price densities recovered from high-frequency data. The tail risk premium extracted from intra-day S&P 500 returns predicts the market equity and variance risk premiums and expected excess returns on a cross section of characteristics-sorted portfolios. Additionally, we describe the differential role of the quantity of tail risk, and of the tail premium, in shaping the future distribution of index returns. Our results are robust to controlling for established measures of variance and tail risk, and of risk premiums, in the predictive models.