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Multivariate volatility forecasts for stock market indices

Ines Wilms (), Jeroen Rombouts and Christophe Croux

International Journal of Forecasting, 2021, vol. 37, issue 2, 484-499

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.

Keywords: International stock markets; Lasso; Option-implied variance; Realized variance; Volatility spillover (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.ijforecast.2020.06.012

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