Moment Risk Premia and Stock Return Predictability

54 Pages Posted: 12 Feb 2018 Last revised: 12 Aug 2020

See all articles by Zhenzhen Fan

Zhenzhen Fan

Gordon S Lang School of Business and Economics, University of Guelph, Guelph, Canada - Department of Economics and Finance

Xiao Xiao

City University London - Bayes Business School

Hao Zhou

Tsinghua University - PBC School of Finance; SUSTech Business School

Date Written: June 27, 2020

Abstract

We study the predictive power of option-implied moment risk premia embedded in the
conventional variance risk premium. We find that while the second moment risk premium
predicts market returns in short horizons with positive coefficients, the third (fourth)
moment risk premium predicts market returns in medium horizons with negative (positive)
coefficients. Combining the higher moment risk premia with the second moment risk
premium improves the stock return predictability over multiple horizons, both in-sample
and out-of-sample. The finding is economically significant in an asset allocation exercise,
and survives a series of robustness checks.

Keywords: Moment risk premia; Variance risk premium; Option-implied moments; Stock return predictability; Predictive regression

JEL Classification: G12, G13, C22

Suggested Citation

Fan, Zhenzhen and Xiao, Xiao and Zhou, Hao, Moment Risk Premia and Stock Return Predictability (June 27, 2020). Available at SSRN: https://ssrn.com/abstract=3120260 or http://dx.doi.org/10.2139/ssrn.3120260

Zhenzhen Fan

Gordon S Lang School of Business and Economics, University of Guelph, Guelph, Canada - Department of Economics and Finance ( email )

Canada

Xiao Xiao (Contact Author)

City University London - Bayes Business School ( email )

United Kingdom

Hao Zhou

Tsinghua University - PBC School of Finance ( email )

No. 43, Chengfu Road
Haidian District
Beijing, 100083
China
+86-10-62790655 (Phone)

SUSTech Business School ( email )

1088 Xueyuan Avenue, Nanshan District
Southern University of Science and Technology
Shenzhen, Guangdong 518055
China

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