Asset returns under model uncertainty: evidence from the euro area, the U.K. and the U.S.

Abstract

The goal of thes paper is to analyze predictability of future asset returns in the context of model uncertainty. Using data for the euro area, the US and the U.K., we show that one can improve the forecasts of stock returns using a Bayesian Model Averaging (BMA) approach, and there is a large amount of model uncertainty. The empirical evidence for the euro area suggests that several macroeconomic, financial and macro-financial variables are consistently among the most prominent determinants of risk premium. As for the U.S, only a few number of predictors play an important role. In the case of the UK, future stock returns are better forecasted by financial variables. These results are corroborated for both the M-open and the M-closed perspectives and in the context of "in-sample" and "out-of-sample" forescating. Finally, we highlight that the predictive ability of the BMA framework is stronger at longer periods, and clearly outperforms the constant expected returns and the autoregressive benchmark models.