New Testing Approaches for Mean-Variance Predictability

We propose tests for smooth but persistent serial correlation in risk premia and volatilities that exploit the non-normality of financial asset returns. Our parametric tests are robust to distributional misspecification, while our nonparametric tests are as powerful as if we knew the true distribution of excess returns. Local power analyses confirm their gains over existing methods, while Monte Carlo exercises document their finite sample reliability. We apply our methods to the Fama-French factors for US stocks. We find mean predictability for the size and value factors but not the market, and variance predictability for all of them.

Erik Kole