Higher-Order Risk Premium, Stock Return Predictability, and Rare Event Dynamics

60 Pages Posted: 12 Feb 2018 Last revised: 16 Mar 2019

See all articles by Zhenzhen Fan

Zhenzhen Fan

Nankai University - School of Finance

Xiao Xiao

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE)

Hao Zhou

Tsinghua University - PBC School of Finance

Date Written: March 14, 2019

Abstract

We find that separating the higher-order risk premium from the pure variance risk premium can significantly improve the stock market predictability, with R-squared up to 14 percent for the 3-month horizon. This finding proves to be economically significant in an asset allocation exercise, becomes even stronger for the portfolio returns of the momentum factor, and survives a series of robustness checks. We show that a consumption-based asset pricing model with rare events can generate the predictability afforded by higher-order risk premium.

Keywords: Equity risk premium; Predictive regression; Higher-order risk premium; Variance risk premium; Rare events

JEL Classification: G12, G13, C22

Suggested Citation

Fan, Zhenzhen and Xiao, Xiao and Zhou, Hao, Higher-Order Risk Premium, Stock Return Predictability, and Rare Event Dynamics (March 14, 2019). Available at SSRN: https://ssrn.com/abstract=3120260 or http://dx.doi.org/10.2139/ssrn.3120260

Zhenzhen Fan

Nankai University - School of Finance ( email )

38 Tongyan Road, Jinnan District
Tianjin, Tianjin 300350
China

Xiao Xiao (Contact Author)

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE) ( email )

P.O. Box 1738
3000 DR Rotterdam, NL 3062 PA
Netherlands

Hao Zhou

Tsinghua University - PBC School of Finance ( email )

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

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