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Forecasting oil price realized volatility using information channels from other asset classes

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  • Degiannakis, Stavros
  • Filis, George

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.

Suggested Citation

  • Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
  • Handle: RePEc:eee:jimfin:v:76:y:2017:i:c:p:28-49
    DOI: 10.1016/j.jimonfin.2017.05.006
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    1. repec:eee:eneeco:v:74:y:2018:i:c:p:370-386 is not listed on IDEAS
    2. Manabu Asai & Rangan Gupta & Michael McAleer, 2019. "The Impact of jumps and leverage in forecasting the co-volatility of oil and gold futures," Documentos de Trabajo del ICAE 2019-12, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    3. Riza Demirer & Rangan Gupta & Qiang Ji & Aviral Kumar Tiwari, 2018. "Geopolitical Risks and the Predictability of Regional Oil Returns and Volatility," Working Papers 201860, University of Pretoria, Department of Economics.
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    5. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Gold Volatility: Is there a Role of Geopolitical Risks?," Working Papers 201943, University of Pretoria, Department of Economics.
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    7. Hardik A. Marfatia & Rangan Gupta & Esin Cakan, 2019. "Dynamic Impact of the U.S. Monetary Policy on Oil Market Returns and Volatility," Working Papers 201916, University of Pretoria, Department of Economics.
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    9. Stavros Degiannakis, George Filis, and Vipin Arora, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
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    13. Manabu Asai & Rangan Gupta & Michael McAleer, 2019. "Forecasting Volatility and Co-volatility of Crude Oil and Gold Futures: Effects of Leverage, Jumps, Spillovers, and Geopolitical Risks," Working Papers 201951, University of Pretoria, Department of Economics.
    14. Rangan Gupta & Patrick Kanda & Aviral Kumar Tiwari & Mark E. Wohar, 2018. "Time-Varying Predictability of Oil Market Movements Over a Century of Data: The Role of US Financial Stress," Working Papers 201848, University of Pretoria, Department of Economics.
    15. Degiannakis, Stavros & Filis, George, 2019. "Oil price volatility forecasts: What do investors need to know?," MPRA Paper 94445, University Library of Munich, Germany.
    16. repec:eee:eneeco:v:75:y:2018:i:c:p:400-409 is not listed on IDEAS

    More about this item

    Keywords

    Volatility forecasting; Realized volatility; Crude oil futures; Risk management; HAR; Asset classes;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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