Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
It is well known that when investors evaluate risk or opportunity, they often depart from predictions of expected utility. In addition, for both academic and financial communities it is a familiar stylized fact that stock return distributions are not normal. Both empirical evidence and experimental evidence indicate that distributional information of asset returns has an important impact on investors. In this paper, we argue that the right-tail distributional information of returns can provide very valuable information to investors and portfolio managers, and right-tail information should be used together with other (say, left-tail) information in analyzing financial markets. Here, we introduce measures for the right-tail distribution. Quantile regression estimators for the right-tail measures are proposed, and their asymptotic properties are developed. Statistical inference on testing for changes of right-tail distribution is also discussed. A Monte Carlo experiment is conducted to evaluate the performance of the proposed estimator. The proposed estimation method may also be applied to estimation of other measures in finance.