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Natural gas volatility predictability in a data-rich world

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  • Lu, Fei
  • Ma, Feng
  • Li, Pan
  • Huang, Dengshi

Abstract

This study employs macroeconomic variables and economic indices to forecast natural gas volatility. The out-of-sample results show that the forecasting performance of the macroeconomic variables outperforms the economic indices. Additionally, the forecasting performance of the mixed data sampling model, which combines the least absolute contraction and the selection operator (MIDAS-LASSO), is better than that of other competing models, and it still has a good predictive ability under certain conditions (e.g., business cycles). Our study confirms the superiority of the MIDAS-LASSO model for natural gas volatility forecasting.

Suggested Citation

  • Lu, Fei & Ma, Feng & Li, Pan & Huang, Dengshi, 2022. "Natural gas volatility predictability in a data-rich world," International Review of Financial Analysis, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:finana:v:83:y:2022:i:c:s105752192200179x
    DOI: 10.1016/j.irfa.2022.102218
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    Cited by:

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    2. 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).
    3. Oleksandr Castello & Marina Resta, 2023. "A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling," Energies, MDPI, vol. 16(12), pages 1-22, June.
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    5. Jozef Barunik & Lukas Vacha, 2024. "Forecasting Volatility of Oil-based Commodities: The Model of Dynamic Persistence," Papers 2402.01354, arXiv.org.
    6. Wang, Lu & Wu, Rui & Ma, WeiChun & Xu, Weiju, 2023. "Examining the volatility of soybean market in the MIDAS framework: The importance of bagging-based weather information," International Review of Financial Analysis, Elsevier, vol. 89(C).

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