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How useful are energy-related uncertainty for oil price volatility forecasting?

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  • Zhang, Xiaoyun
  • Guo, Qiang

Abstract

Recently, numerous events, such as trade conflicts, the United Kingdom's exit from the European Union, the Russian-Ukrainian war, and the Palestinian-Israeli conflict, have led to increased risks of uncertainty in global economic, political, and energy markets. However, despite the essential role of crude oil markets in national security and economic development, existing studies have paid little attention to how country-level uncertainty affects oil price volatility. Therefore, in this paper, we investigate whether the energy-related uncertainty index (EUI) proposed by Dang et al. (2023) affects oil price volatility through the GARCH-MIDAS framework. We first analyze whether the EUI can play a role in the oil market through parameter estimation of the model and find evidence of information spillovers from the EUI to the oil market. We then use tests to examine the accuracy of the econometric models used in this paper for WTI volatility forecasting. The test results show that the Double Asymmetric GARCH-MIDAS-EUI model has excellent forecasting performance. Therefore, this work is valuable for governments and corporations in formulating energy policies and business strategies to better cope with the risk of market uncertainty.

Suggested Citation

  • Zhang, Xiaoyun & Guo, Qiang, 2024. "How useful are energy-related uncertainty for oil price volatility forecasting?," Finance Research Letters, Elsevier, vol. 60(C).
    • Handle: RePEc:eee:finlet:v:60:y:2024:i:c:s1544612323013259
    DOI: 10.1016/j.frl.2023.104953
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    Cited by:

    1. Li, Hailing & Pei, Xiaoyun & Yang, Yimin & Zhang, Hua, 2024. "Assessing the impact of energy-related uncertainty on G20 stock market returns: A decomposed contemporaneous and lagged R2 connectedness approach," Energy Economics, Elsevier, vol. 132(C).

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    More about this item

    Keywords

    Uncertainty risk; Energy-related uncertainty; Volatility forecasting; Garch-midas;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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