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Asymmetric impacts of fundamentals on the natural gas futures volatility: An augmented GARCH approach

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  • Ergen, Ibrahim
  • Rizvanoghlu, Islam

Abstract

We investigated the determinants of daily volatility for natural gas nearby-month futures traded on the NYMEX within a GARCH framework augmented with market fundamentals. Consistent with the previous literature, we found that volatility is much higher on the natural gas and crude oil storage report announcement days, on Mondays and during winters. We also confirmed that high volatility is associated with divergence of storage levels and temperatures from seasonal norms. The asymmetric impact of storage levels on volatility across different seasons is empirically investigated and documented. The mainstream finding in the literature that lower storage levels result in higher volatility is valid only during winter. At other times, it is actually higher storage levels causing higher volatility. Also, time to maturity effect is present only in winters. Additionally, weather shocks have asymmetric impact on volatility depending on the sign of the shock. Finally, we found that augmentation with market fundamentals improves the out-of-sample forecast accuracy of standard GARCH models.

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  • Ergen, Ibrahim & Rizvanoghlu, Islam, 2016. "Asymmetric impacts of fundamentals on the natural gas futures volatility: An augmented GARCH approach," Energy Economics, Elsevier, vol. 56(C), pages 64-74.
    • Handle: RePEc:eee:eneeco:v:56:y:2016:i:c:p:64-74
    DOI: 10.1016/j.eneco.2016.02.022
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    Cited by:

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    2. Chang, Chia-Lin & McAleer, Michael & Wang, Yanghuiting, 2018. "Testing Co-Volatility spillovers for natural gas spot, futures and ETF spot using dynamic conditional covariances," Energy, Elsevier, vol. 151(C), pages 984-997.
    3. Hailemariam, Abebe & Smyth, Russell, 2019. "What drives volatility in natural gas prices?," Energy Economics, Elsevier, vol. 80(C), pages 731-742.
    4. Dimitrios I. Vortelinos & Konstantinos Gkillas, 2018. "Intraday realised volatility forecasting and announcements," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 88-118.
    5. Sherzod N. Tashpulatov, 2022. "Modeling Electricity Price Dynamics Using Flexible Distributions," Mathematics, MDPI, vol. 10(10), pages 1-15, May.
    6. 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).
    7. Luo, Keyu & Guo, Qiang & Li, Xiafei, 2022. "Can the return connectedness indices from grey energy to natural gas help to forecast the natural gas returns?," Energy Economics, Elsevier, vol. 109(C).
    8. Huang, Zhouchun & Zheng, Qipeng Phil, 2020. "A multistage stochastic programming approach for preventive maintenance scheduling of GENCOs with natural gas contract," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1036-1051.
    9. Chao Liang & Feng Ma & Lu Wang & Qing Zeng, 2021. "The information content of uncertainty indices for natural gas futures volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1310-1324, November.
    10. Gong, Xue & Ye, Xin & Zhang, Weiguo & Zhang, Yue, 2023. "Predicting energy futures high-frequency volatility using technical indicators: The role of interaction," Energy Economics, Elsevier, vol. 119(C).
    11. Kannika Duangnate & James W. Mjelde, 2020. "Prequential forecasting in the presence of structure breaks in natural gas spot markets," Empirical Economics, Springer, vol. 59(5), pages 2363-2384, November.
    12. Urom, Christian & Ndubuisi, Gideon & Ozor, Jude, 2021. "Economic activity, and financial and commodity markets’ shocks: An analysis of implied volatility indexes," International Economics, Elsevier, vol. 165(C), pages 51-66.
    13. Sherzod N. Tashpulatov, 2021. "Modeling and Estimating Volatility of Day-Ahead Electricity Prices," Mathematics, MDPI, vol. 9(7), pages 1-11, March.
    14. Dai, Peng-Fei & Xiong, Xiong & Zhang, Jin & Zhou, Wei-Xing, 2022. "The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model," Resources Policy, Elsevier, vol. 78(C).
    15. Urom, Christian & Ndubuisi, Gideon & Guesmi, Khaled, 2022. "How do financial and commodity markets volatility react to real economic activity?," Finance Research Letters, Elsevier, vol. 47(PB).
    16. Yanting Chen & Peter R. Hartley & Yihui Lan, 2023. "Temperature, storage, and natural gas futures prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(4), pages 549-575, April.
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    18. Sherzod N. Tashpulatov, 2021. "The Impact of Regulatory Reforms on Demand Weighted Average Prices," Mathematics, MDPI, vol. 9(10), pages 1-15, May.

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    More about this item

    Keywords

    Natural gas futures; GARCH; Price volatility; Storage; Weather; Asymmetric volatility;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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