Title: A new nonparametric tail risk measure
Authors: Keith Law - The University of Hong Kong (Hong Kong)
Wai-keung Li - The Education University of Hong Kong (Hong Kong)
Philip Yu - The University of Hong Kong (Hong Kong) [presenting]
Abstract: The proposition of tail risk as a new asset pricing factor has gained traction in recent years. Recent work proxies the cross-sectional variation of returns by Fama-French portfolios and summarizes the cross-sectional variation by the PCA method to further reduce the dimension to a few basis assets. They then set the state-of-natures to be higher than the basis assets to estimate the stochastic discount factors for risk neutralizing the excess expected shortfall, which is taken as a tail risk measure. As an alternative approach to this double dimension reduction, we develop a nonparametric risk measure by directly forming portfolios which minimize the excess expected shortfall nonparametrically. Our empirical results reveal that the direct measure exhibits higher explanatory power when applied to more liquid and non-lottery style of stock returns.