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Geopolitical Risks and the Predictability of Regional Oil Returns and Volatility

Author

Listed:
  • Riza Demirer

    (Department of Economics & Finance, Southern Illinois University Edwardsville, Edwardsville, USA.)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • Qiang Ji

    (Center for Energy and Environmental Policy Research, Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China.)

  • Aviral Kumar Tiwari

    (Montpellier Business School, Montpellier, France)

Abstract

This paper hypothesizes that global geopolitical risks (GPRs) can predict oil market return and volatility. For our purpose, we use a k-th order nonparametric causality-inquantiles test, applied to a daily data set covering the period of 15th May, 1996 to 31st May, 2018 of six oil prices (the Nigerian Bonny Light, Brent, Dubai, OPEC, Tapis, and WTI). Our results indicate that the relationship between oil returns and GPRs is highly nonlinear and hence, linear tests of Granger causality cannot be relied upon. Based on the data-driven econometric method, we observe that GPRs have predictability for oil returns of the West African Bonny Light, OPEC and Tapis, while in terms of volatility, causality is observed for all oil prices barring the case of Dubai. In sum, the impact of GPRs is primarily on volatility of oil markets, but more importantly, the impact of GPRs is not uniform across the oil markets.

Suggested Citation

  • 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.
    • Handle: RePEc:pre:wpaper:201860
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    References listed on IDEAS

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    Cited by:

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    4. Dutta, Anupam & Soytas, Ugur & Das, Debojyoti & Bhattacharyya, Asit, 2022. "In search of time-varying jumps during the turmoil periods: Evidence from crude oil futures markets," Energy Economics, Elsevier, vol. 114(C).

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    More about this item

    Keywords

    Geopolitical Risks; Oil Prices; Nonparametric Causality-in-Quantiles Test;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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