Predicting Equity Risk Premium Using the Smooth Cross-Sectional Tail Risk: The Importance of Correlation

45 Pages Posted: 6 Sep 2015 Last revised: 23 Mar 2018

See all articles by José Afonso Faias

José Afonso Faias

Catholic University of Portugal (UCP)

Pavel Onyshchnenko

EDP Renewables North America

Date Written: July 28, 2017

Abstract

We provide a new monthly cross-sectional measure of stock market tail risk, defined as the average of the daily cross-sectional tail risk, rather than the tail risk of the pooled daily returns within a month. The former better captures monthly tail risk rather than merely the tail risk on specific days within a month. Using simulations, we attest that this is due to the low value of daily correlations. We show that this difference is important in generating strong in- and out-of-sample predictability and performs better than the historical risk premium and other commonly used predictors for short- and long-term horizons. This strong predictability improves investor performance in a mean-variance setting.

Keywords: Equity Premium, Prediction, Cross-sectional

JEL Classification: G11, G14, G17

Suggested Citation

Faias, José and Onyshchnenko, Pavel, Predicting Equity Risk Premium Using the Smooth Cross-Sectional Tail Risk: The Importance of Correlation (July 28, 2017). Available at SSRN: https://ssrn.com/abstract=2656171 or http://dx.doi.org/10.2139/ssrn.2656171

José Faias (Contact Author)

Catholic University of Portugal (UCP) ( email )

Palma de Cima
Lisboa, 1649-023
Portugal

Pavel Onyshchnenko

EDP Renewables North America ( email )

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