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Predicting volatility in natural gas under a cloud of uncertainties

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  • Chen, Juan
  • Xiao, Zuoping
  • Bai, Jiancheng
  • Guo, Hongling

Abstract

The COVID-19 pandemic has triggered an economic crisis and the ensuing global uncertainty. The current Russian-Ukrainian conflict has escalated tensions in various regions and increased various uncertainties in the financial and economic system. These uncertainties have had a significant impact on the development of the natural gas market during the current critical period of carbon neutrality and energy transition. This paper explores the impact of various uncertainties on price volatility in the U.S. natural gas futures market using the GARCH-MIDAS model. We considered eleven types of uncertainties, including four US economic policy uncertainties, four global uncertainty indicators, and oil supply-demand uncertainty closely related to the natural gas market. The in-sample empirical results find that various uncertainties can impact the natural gas market. However, through out-of-sample testing, we find that economic policy uncertainty has more predictive power than other indicators in predicting natural gas price fluctuations. Interestingly, oil supply-demand uncertainty surpasses global indicators and can provide forecasting information for natural gas markets. Therefore, in the current context of high uncertainty, our research may offer better decision-making opinions for market participants.

Suggested Citation

  • Chen, Juan & Xiao, Zuoping & Bai, Jiancheng & Guo, Hongling, 2023. "Predicting volatility in natural gas under a cloud of uncertainties," Resources Policy, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:jrpoli:v:82:y:2023:i:c:s0301420723001447
    DOI: 10.1016/j.resourpol.2023.103436
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    More about this item

    Keywords

    Uncertainty; Forecast; Natural gas; 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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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