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Predicting natural gas futures’ volatility using climate risks

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  • Guo, Kun
  • Liu, Fengqi
  • Sun, Xiaolei
  • Zhang, Dayong
  • Ji, Qiang

Abstract

In this paper, we examine the tracking and predictive power of two kinds of climate risks—namely, climate policy uncertainty (CPU) and climate-related disasters—on the price volatility of natural gas futures. The GARCH-MIDAS model was adopted to incorporate daily natural gas futures prices with monthly CPU indices and disaster frequencies. The empirical results showed a robust predictive relationship between disaster frequency and natural gas price volatility under both in-sample and out-of-sample scenarios, while combining the CPU index with other predictors could not improve the out-of-sample forecasting performance. We believe these findings could provide insights for traders and market regulators.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:finlet:v:55:y:2023:i:pa:s1544612323002878
    DOI: 10.1016/j.frl.2023.103915
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