Forecasting stock market volatility under parameter and model uncertainty

• Published in: Research in international business and finance 2023-10, Vol.66, p.102084, Article 102084
• Main Authors: Li, Zhao-Chen, Xie, Chi, Wang, Gang-Jin, Zhu, You, Long, Jian-You, Zhou, Yang
• Format: Article
• Language: English
• Subjects: Dynamic model averaging; Forecast combination; Model uncertainty; Parameter uncertainty; Stock market volatility
• ISSN: 0275-5319
• DOI: 10.1016/j.ribaf.2023.102084

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. [Display omitted] •We employ rich models to simulate the parameter and model uncertainty.•Model uncertainty matters more than parameter uncertainty in forecasting.•Forecast combination, especially DMA, solves the model uncertainty problem well.•The revealed volatility predictability is statistically and economically significant.•We explain combination forecasts’ benefits statistically and economically.