Forecasting global stock market implied volatility indices.

Degiannakis, S., Filis, G. and Hassani, H., 2018. Forecasting global stock market implied volatility indices. Journal of empirical finance, 46, pp. 111-129.

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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.

Item Type:Article
ISSN:0927-5398
Uncontrolled Keywords:Stock market; Implied Volatility; Volatility Forecasting; Singular Spectrum Analysis; ARFIMA; HAR; Holt-Winters; Model Confidence Set; Model- Averaged Forecasts.
Group:Faculty of Management
ID Code:30159
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:03 Jan 2018 11:56
Last Modified:14 Mar 2018 11:53

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