Size Distortions in Robust Estimators: Implications for Asset Pricing

Abstract

Predictors of excess returns exhibit persistence and time-varying variance, implying the need for heteroskedastic and autocorrelation consistent errors (HAC) in linear tests. Using simulations, we show that although they lead to important improvements, such corrections fail to provide adequate size properties under the null hypothesis of zero abnormal returns. Even optimally specified robust estimators suffer from size distortions, implying that the best HACs remain imperfect. We propose a standardization of the robust estimator that addresses the problem, albeit not completely. We find that between 2006 and 2021, more than 20% of a wide panel of predictors differ in significance status at the standard 5% level in comparing this estimator to ordinary least squares, and more than 30% at a more restrictive level.

Keywords: anomalies, asset pricing, autocorrelation, heteroscedasticity, robust estimation

JEL Classification: C12, C14, C21, C58, G12