Forecasting oil price realized volatility using information channels from other asset classes. (September 2017)

• Record Type: Journal Article
• Authors: Degiannakis, Stavros; Filis, George
• Is Part Of: Journal of international money and finance. Volume 76(2017)
• Page Start: 28
• Page End: 49
• Publication Date: 2017-09
• Subjects: C22 -- C53 -- G13 -- Q47; Volatility forecasting -- Realized volatility -- Crude oil futures -- Risk management -- HAR -- Asset classes; International finance -- Periodicals; Foreign exchange -- Periodicals; Finances internationales -- Périodiques; Change -- Périodiques; Foreign exchange; International finance; Periodicals; 332.04205
• DOI: 10.1016/j.jimonfin.2017.05.006
• Languages: English
• ISSNs: 0261-5606

Abstract

Highlights: We forecast oil price realized volatility for 1-day to 66-days ahead. Different asset classes' volatilities are considered as information channels. The use of information channels enhances the predictive accuracy of oil volatility. Results are robust to several alternative tests and specifications. Abstract: Motivated from Ross (1989) who maintains that asset volatilities are synonymous to the information flow, we claim that cross-market volatility transmission effects are synonymous to cross-market information flows or "information channels" from one market to another. Based on this assertion we assess whether cross-market volatility flows contain important information that can improve the accuracy of oil price realized volatility forecasting. We concentrate on realized volatilities derived from the intra-day prices of the Brent crude oil and four different asset classes ( Stocks, Forex, Commodities and Macro ), which represent the different "information channels" by which oil price volatility is impacted from. We employ a HAR framework and estimate forecasts for 1-day to 66-days ahead. Our findings provide strong evidence that the use of the different "information channels" enhances the predictive accuracy of oil price realized volatility at all forecasting horizons. Numerous forecasting evaluation tests and alternative model specifications confirm the robustness of our results.