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Investors' Uncertainty and Forecasting Stock Market Volatility

Author

Listed:
  • Ruipeng Liu

    (Department of Finance, Deakin Business School, Deakin University, Melbourne, VIC 3125, Australia)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

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.

Suggested Citation

  • Ruipeng Liu & Rangan Gupta, 2020. "Investors' Uncertainty and Forecasting Stock Market Volatility," Working Papers 202090, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202090
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