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Forecasting global stock market implied volatility indices

Stavros Degiannakis (), George Filis () and Hossein Hassani

Journal of Empirical Finance, 2018, vol. 46, issue C, 111-129

Abstract: This study compares parametric and non-parametric techniques in terms of their forecasting power on implied volatility indices. We extend our comparisons using combined and model-averaging models. The forecasting models are applied on eight implied volatility indices of the most important stock market indices. We provide evidence that the non-parametric models of Singular Spectrum Analysis combined with Holt-Winters (SSA-HW) exhibit statistically superior predictive ability for the one and ten trading days ahead forecasting horizon. By contrast, the model-averaged forecasts based on both parametric (Autoregressive Integrated model) and non-parametric models (SSA-HW) are able to provide improved forecasts, particularly for the ten trading days ahead forecasting horizon. For robustness purposes, we build two trading strategies based on the aforementioned forecasts, which further confirm that the SSA-HW and the ARI-SSA-HW are able to generate significantly higher net daily returns in the out-of-sample period.

Keywords: Stock market; Implied volatility; Volatility forecasting; Singular Spectrum Analysis; ARFIMA; HAR; Holt-Winters; Model Confidence Set; Model-averaged forecasts (search for similar items in EconPapers)
JEL-codes: C14 C22 C52 C53 G15 (search for similar items in EconPapers)
Date: 2018
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Handle: RePEc:eee:empfin:v:46:y:2018:i:c:p:111-129