This paper introduces a regime-switching combination approach to predict excess stock returns. The approach explicitly incorporates model uncertainty, regime uncertainty, and parameter uncertainty. The empirical findings reveal that the regime-switching combination forecasts of excess returns deliver consistent out-of-sample forecasting gains relative to the historical average and the Rapach et al. (2010) combination forecasts. The findings also reveal that two regimes are related to the business cycle. Based on the business cycle explanation of regimes, excess returns are found to be more predictable during economic contractions than during expansions. Finally, return forecasts are related to the real economy, thus providing insights on the economic sources of return predictability.

In light of inconclusive evidence on the relation between market volatility and stock returns, this paper proposes a multi-factor volatility model and examines its impact on cross-sectional pricing. We also evaluate the out-of-sample performance and economic significance of multi-factor volatility. We find that conditional variances of the size and value dynamic factor earn significant and positive variance risk premia. In addition, multi-factor volatility can significantly improve the out-of-sample return predictability with a positive economic gain in asset allocation.