Multivariate volatility forecasts for stock market indices

Ines Wilms, Jeroen Rombouts, Christophe Croux

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Volatility forecasts aim to measure future risk and they are key inputs for financial analysis. In this study, we forecast the realized variance as an observable measure of volatility for several major international stock market indices and accounted for the different predictive information present in jump, continuous, and option-implied variance components. We allowed for volatility spillovers in different stock markets by using a multivariate modeling approach. We used heterogeneous autoregressive (HAR)-type models to obtain the forecasts. Based an out-of-sample forecast study, we show that: (i) including option-implied variances in the HAR model substantially improves the forecast accuracy, (ii) lasso-based lag selection methods do not outperform the parsimonious day-week-month lag structure of the HAR model, and (iii) cross-market spillover effects embedded in the multivariate HAR model have long-term forecasting power. (C) 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)484-499
Number of pages16
JournalInternational Journal of Forecasting
Volume37
Issue number2
Early online date28 Sep 2020
DOIs
Publication statusPublished - 2021

Keywords

  • Lasso
  • Volatility spillover
  • international stock markets
  • option-implied variance
  • realized variance
  • Option-implied variance
  • International stock markets
  • Realized variance

Cite this