Investors’ Uncertainty and Forecasting Stock Market Volatility

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.

Keywords:

• Investors’ uncertainty
• Stock market risk
• MSM
• Volatility forecasting

Notes

1 The MDH postulates that the innovation on returns is a linear combination of the intraday return movements. The intraday return increment incorporates the number of information flows arrived into the market in a given day. Since the intraday price movement is random, daily returns follow a mixture of normally distributed random variables with the information flow into the market as a mixing variable. To sum up, daily price changes are driven by a set of information flow, and the arrival of unexpected news is accompanied by the above average trading activity. On the other hand, the SIAH questions the instantaneous relationship as predicted by MDH and provides a different explanation. It argues that each trader observes the information signal differently at time and the information may not receive simultaneously, thereby generating a series of incomplete equilibria. Market equilibrium can be established provided that all traders receive same set of information simultaneously. Thus, the shift of new information is not immediate as considered in the MDH. Nevertheless, both MDH and SIAH believe that the price volatility of the market can be potentially predictable through the knowledge of trading volume and that the relationship of volume and volatility is positive.