Forecasting stock market volatility under parameter and model uncertainty

We forecast monthly stock market volatility under parameter and model uncertainty. Using a long economic dataset spanning almost a century, we prove that model uncertainty plays a more crucial role than parameter uncertainty in improving volatility predictability. The combination models with model uncertainty, especially dynamic model averaging (DMA), provide very competitive improvements in forecasting accuracy, whose superiority is also reflected in asset allocation and risk hedging. We find two empirical properties of forecast combination: (i) it incorporates information from numerous predictors, helping reduce both the forecast bias and forecast error variance; and (ii) the economic links of the forecasts based on it are significant, and the predictive gains are concentrated in poor economic conditions. Overall, we highlight the importance of considering model uncertainty via forecast combination when investigating the expected stock market volatility.