Economics at your fingertips  

Investors' Uncertainty and Forecasting Stock Market Volatility

Ruipeng Liu () and Rangan Gupta ()
Additional contact information
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
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Working Papers from University of Pretoria, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Rangan Gupta ().

Page updated 2020-11-28
Handle: RePEc:pre:wpaper:202090