Deakin University
Browse

File(s) under permanent embargo

Investors’ uncertainty and forecasting stock market volatility

journal contribution
posted on 2020-01-01, 00:00 authored by Ruipeng LiuRuipeng Liu, Rangan Gupta
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.

History

Journal

Journal of Behavioral Finance

Issue

Latest Articles

Publisher

Routledge

Location

Philadelphia, Pa.

ISSN

1542-7560

eISSN

1542-7579

Language

eng

Publication classification

C1 Refereed article in a scholarly journal; C Journal article

Copyright notice

2020, Institute of Behavioral Finance

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC