Predicting risk premium under changes in the conditional distribution of stock returns

The goal of this paper is to assess time-variation in asset returns while considering the whole conditional distribution. We use a quantile regression framework and quarterly data for the U.S., and show that the probabilistic distribution of expectations about future stock returns changes in response to variation in commonly used explanatory variables. Moreover, our results support the idea that lower quantiles are less stable than upper quantiles, thus, suggesting that asset pricing models are particularly accurate in capturing the expectations that less risk-averse agents have about future returns.