Variance risk in aggregate stock returns and time-varying return predictability

Abstract

This paper introduces a new out-of-sample forecasting methodology for monthly market returns using the variance risk premium (VRP) that is both statistically and economically significant. This methodology is motivated by the ‘beta representation,’ which implies that the market risk premium is related to the price of variance risk by the variance risk exposure. Hence, when the slope of the contemporaneous regression of market returns on variance innovation is larger, future returns are more sharply related to the current VRP. Also, predictions are more accurate when market returns are highly correlated to variance shocks.