Two Out-of-Sample Forecasting Models of the Equity Premium

Abstract

I derive two valid forecasting models of the equity premium in monthly frequency, based on little more than no-arbitrage: A "predictability timing" version of partial least squares, given that predictability is theoretically time-varying; and a least squares model with realized market premiums in monthly frequency as the regressor, since realized returns are theoretically correlated to risk and to the price of risk. This evidence is consistent with the instability inherent to monthly equity premium forecasts based on standard partial least squares and disaggregated book-to-markets as regressors, and with the fact that taking one extra lag of book-to-markets in predictive return regressions improves the estimates.

Keywords: predictability, out-of-sample, equity premium, disaggregated book-to-markets

JEL Classification: G11, G12, G14.