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Global Financial Cycle and the Predictability of Oil Market Volatility: Evidence from a GARCH-MIDAS Model

Author

Listed:
  • Afees A. Salisu

    (Centre for Econometric and Allied Research, University of Ibadan, Ibadan, Nigeria)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa)

  • Riza Demirer

    (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA)

Abstract

This study examines the predictive power of the global financial cycle (GFCy) over oil market volatility using the GARCH-MIDAS framework. The GARCH-MIDAS model provides an appropriate setting to forecast high frequency oil market volatility using global predictors that are only available at low frequency. We show that the global financial cycle carries significant predictive information over both oil market volatility proxies, both in- and out-of-sample. The predictive relationship is found to be positive, more strongly during the pre-GFC period, suggesting that rising global asset prices coupled with improved cross-border capital flows are associated with rising volatility in the oil market. While the GARCH-MIDAS model incorporating GFCy or any other proxy of global financial/economic conditions yields economic gains compared to the conventional GARCH-MIDAS-RV specification, especially in the pre-GFC period; the stance is found to be robust to risk aversion and leverage ratio. The economic gains observed from the GFCy-based model particularly during the pre-GFC period when world markets experienced a steady rise in global asset prices and cross-border capital flows underline the potential role of risk appetite (or behavioural factors) in forecasting applications. Overall, our results suggest that incorporating low frequency proxies of global asset market conditions can provide significant forecasting gains for energy market models, with significant implications for both investors and policymakers.

Suggested Citation

  • Afees A. Salisu & Rangan Gupta & Riza Demirer, 2021. "Global Financial Cycle and the Predictability of Oil Market Volatility: Evidence from a GARCH-MIDAS Model," Working Papers 202121, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:202121
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    4. Liang, Chao & Xia, Zhenglan & Lai, Xiaodong & Wang, Lu, 2022. "Natural gas volatility prediction: Fresh evidence from extreme weather and extended GARCH-MIDAS-ES model," Energy Economics, Elsevier, vol. 116(C).
    5. Harrison, Andre & Liu, Xiaochun & Stewart, Shamar L., 2023. "Structural sources of oil market volatility and correlation dynamics," Energy Economics, Elsevier, vol. 121(C).
    6. Liu, Feng & Shao, Shuai & Li, Xin & Pan, Na & Qi, Yu, 2023. "Economic policy uncertainty, jump dynamics, and oil price volatility," Energy Economics, Elsevier, vol. 120(C).
    7. Ding, Shusheng & Cui, Tianxiang & Zhang, Yongmin, 2022. "Futures volatility forecasting based on big data analytics with incorporating an order imbalance effect," International Review of Financial Analysis, Elsevier, vol. 83(C).
    8. Zhang, Lixia & Bai, Jiancheng & Zhang, Yueyan & Cui, Can, 2023. "Global economic uncertainty and the Chinese stock market: Assessing the impacts of global indicators," Research in International Business and Finance, Elsevier, vol. 65(C).
    9. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Lasisi, Lukman & Olaniran, Abeeb, 2022. "Geopolitical risk and stock market volatility in emerging markets: A GARCH – MIDAS approach," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    10. Chen, Chun-Da & Demirer, Rıza, 2022. "Oil beta uncertainty and global stock returns," Energy Economics, Elsevier, vol. 112(C).
    11. Guo, Kun & Liu, Fengqi & Sun, Xiaolei & Zhang, Dayong & Ji, Qiang, 2023. "Predicting natural gas futures’ volatility using climate risks," Finance Research Letters, Elsevier, vol. 55(PA).
    12. Wang, Lu & Zhao, Chenchen & Liang, Chao & Jiu, Song, 2022. "Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 48(C).

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    More about this item

    Keywords

    Global Financial Cycle; Oil Volatility; Predictability; MIDAS models;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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