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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0927539817301263
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:46:y:2018:i:c:p:111-129

DOI: 10.1016/j.jempfin.2017.12.008

Access Statistics for this article

Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff

More articles in Journal of Empirical Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().