Investors’ Uncertainty and Forecasting Stock Market Volatility

Ruipeng Liu and Rangan Gupta

Journal of Behavioral Finance, 2022, vol. 23, issue 3, 327-337

Abstract: This 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.

Date: 2022
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Working Paper: Investors' Uncertainty and Forecasting Stock Market Volatility (2020)
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DOI: 10.1080/15427560.2020.1867551

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