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Variance dynamics and term structure of the natural gas market

Author

Listed:
  • Shao, Chengwu
  • Bhar, Ramaprasad
  • Colwell, David B.
  • Sheng, Ni
  • Wei, Xinyang

Abstract

In this paper, we develop a stochastic model to investigate the variance dynamics and term structure of the US natural gas market. The model features two variance factors, the Samuelson effect, seasonality and linearly analytical pricing formula. With variance swap rates synthesized from options prices spanning more than 20 years, we show that the two-factor variance model has better performance and is preferred to describe the variance term structure. Moreover, we find that the two variance factors are particularly needed to capture humps in variance term curves implied from the market. Finally, we apply the two-factor model to hedging natural gas straddles and obtain improved performance. These results highlight the importance of introducing multiple variance factors to the natural gas market, and may be applied to other similar sectors.

Suggested Citation

  • Shao, Chengwu & Bhar, Ramaprasad & Colwell, David B. & Sheng, Ni & Wei, Xinyang, 2024. "Variance dynamics and term structure of the natural gas market," Energy Economics, Elsevier, vol. 137(C).
    • Handle: RePEc:eee:eneeco:v:137:y:2024:i:c:s0140988324004882
    DOI: 10.1016/j.eneco.2024.107780
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    More about this item

    Keywords

    Natural gas; Two variance factors; Hump-shaped variance term structure; Hedging;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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