Liu, RuipengGupta, Rangan2022-07-012022-07-012022Ruipeng Liu & Rangan Gupta (2022) Investors’ Uncertainty and Forecasting Stock Market Volatility, Journal of Behavioral Finance, 23:3, 327-337, DOI: 10.1080/15427560.2020.1867551.1542-7560 (print)1542-7579 (online)10.1080/15427560.2020.1867551https://repository.up.ac.za/handle/2263/86027This article examines whether incorporating investors’ uncertainty, as captured by the conditional volatility of sentiment, can help forecasting volatility of stock markets. In this regard, using the Markov-switching multifractal (MSM) model, we find that investors’ uncertainty can substantially increase the accuracy of the forecasts of stock market volatility according to the forecast encompassing test. We further provide evidence that the MSM outperforms the dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model.en© 2021 Taylor and Francis. This is an submitted version of an article published in Journal of Behavioral Finances, vol. 23, no. 3, pp. 327-337, 2022. doi : 10.1080/15427560.2020.1867551. Journal of Behavioral Finance is available online at : https://www.tandfonline.com/loi/hbhf20.Investors’ uncertaintyStock market riskMarkov-switching multifractal (MSM) modelVolatility forecastingInvestors’ uncertainty and forecasting stock market volatilityPreprint Article