Investors' Uncertainty and Forecasting Stock Market Volatility

Ruipeng Liu () and Rangan Gupta
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Ruipeng Liu: Department of Finance, Deakin Business School, Deakin University, Melbourne, VIC 3125, Australia

No 202090, Working Papers from University of Pretoria, Department of Economics

Abstract: This paper examines if 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.

Keywords: Investors' uncertainty; Stock market risk; MSM; Volatility forecasting (search for similar items in EconPapers)
Pages: 15 pages
Date: 2020-09
New Economics Papers: this item is included in nep-ets, nep-fmk, nep-for, nep-ore and nep-rmg
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Citations: View citations in EconPapers (16)

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Journal Article: Investors’ Uncertainty and Forecasting Stock Market Volatility (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202090

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