Persistent Predictors and the Cross-Section of Stock Returns

Abstract

We show that when returns are predictable, persistent predictors, known to bias time-series predictive regressions, also bias the estimation of the cross-sectional moments of asset return distribution, especially the variance-covariance matrix of returns. Our findings, further, suggest that the underlying persistence levels together with sample size influence the conclusions of asset pricing tests. We define a test statistic that is immune to this bias and shows consistent results across the persistence levels and sample sizes.

Keywords: asset pricing, persistent predictors, stochastic discount factor bounds, conditioning information

JEL Classification: G11, G12