Measuring Skewness Premia

54 Pages Posted: 20 Mar 2018 Last revised: 15 Dec 2018

Date Written: November 16, 2018

Abstract

We provide a new methodology to empirically investigate the respective roles of systematic and idiosyncratic skewness in explaining expected stock returns. Using a large number of predictors, we forecast the cross-sectional ranks of systematic and idiosyncratic skewness which are easier to predict than their actual values. Compared to other measures of ex ante systematic skewness, our forecasts create a significant spread in ex post systematic skewness. A predicted systematic skewness risk factor carries a significant risk premium that ranges from 6% to 12% per year and is robust to the inclusion of downside beta, size, value, momentum, profitability, and investment factors. In contrast to systematic skewness, the role of idiosyncratic skewness in pricing stocks is less robust. Finally, we document how the determinants of systematic and idiosyncratic skewness differ.

Keywords: Systematic skewness, coskewness, idiosyncratic skewness, large panel regression, forecasting

JEL Classification: G12

Suggested Citation

Langlois, Hugues, Measuring Skewness Premia (November 16, 2018). HEC Paris Research Paper No. FIN-2018-1256. Available at SSRN: https://ssrn.com/abstract=3141416 or http://dx.doi.org/10.2139/ssrn.3141416

Hugues Langlois (Contact Author)

HEC Paris - Finance Department ( email )

France

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