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Understanding the US natural gas market: A Markov switching VAR approach

Author

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  • Chenghan Hou
  • Bao H. Nguyen

Abstract

Over the past three decades, the US natural gas market has witnessed significant changes. Utilizing a standard Bayesian model comparison method, this paper formally determines four regimes existing in the market. It then employs a Markov switching vector autoregressive model to investigate the regime-dependent responses of the market to its fundamental shocks. The results reveal that the US natural gas market tends to be much more sensitive to shocks occurring in regimes existing after the Decontrol Act 1989 than the other regimes. The paper also finds that shocks to the natural gas demand and price have negligible effects on natural gas production while the price of natural gas is mainly driven by specific demand shocks. Augmenting the model by incorporating the price of crude oil, the results show that the impacts of oil price shocks on natural gas prices are relatively small and regime-dependent.

Suggested Citation

  • Chenghan Hou & Bao H. Nguyen, 2018. "Understanding the US natural gas market: A Markov switching VAR approach," CAMA Working Papers 2018-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2018-14
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    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2018-04/14_2018_hou_nguyen.pdf
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    Citations

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    Cited by:

    1. Jamie L. Cross & Chenghan Hou & Bao H. Nguyen, 2018. "On the China factor in international oil markets: A regime switching approach," Working Papers No 11/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    2. Feng, Gen-Fu & Wang, Quan-Jing & Chu, Yin & Wen, Jun & Chang, Chun-Ping, 2021. "Does the shale gas boom change the natural gas price-production relationship? Evidence from the U.S. market," Energy Economics, Elsevier, vol. 93(C).
    3. Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2021. "Forecasting natural gas prices using highly flexible time-varying parameter models," Economic Modelling, Elsevier, vol. 105(C).
    4. Li, Wei & Lu, Can, 2019. "The multiple effectiveness of state natural gas consumption constraint policies for achieving sustainable development targets in China," Applied Energy, Elsevier, vol. 235(C), pages 685-698.
    5. Arezoo Ghazanfari & Armin Razmjoo, 2022. "The Effect of Market Isolation on Competitive Behavior in Retail Petrol Markets," Sustainability, MDPI, vol. 14(13), pages 1-33, July.
    6. Sun, Chuanwang & Zhan, Yanhong & Peng, Yiqi & Cai, Weiyi, 2022. "Crude oil price and exchange rate: Evidence from the period before and after the launch of China's crude oil futures," Energy Economics, Elsevier, vol. 105(C).
    7. Oscar V. De la Torre-Torres & Francisco Venegas-Martínez & Mᵃ Isabel Martínez-Torre-Enciso, 2021. "Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models," Mathematics, MDPI, vol. 9(2), pages 1-22, January.
    8. Miao, Xiaoyu & Wang, Qunwei & Dai, Xingyu, 2022. "Is oil-gas price decoupling happening in China? A multi-scale quantile-on-quantile approach," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 450-470.
    9. Oscar V. De la Torre-Torres & Dora Aguilasocho-Montoya & María de la Cruz del Río-Rama, 2020. "A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures," Mathematics, MDPI, vol. 8(6), pages 1-19, June.
    10. Nguyen, Bao H. & Okimoto, Tatsuyoshi, 2019. "Asymmetric reactions of the US natural gas market and economic activity," Energy Economics, Elsevier, vol. 80(C), pages 86-99.
    11. Rubaszek, Michał & Uddin, Gazi Salah, 2020. "The role of underground storage in the dynamics of the US natural gas market: A threshold model analysis," Energy Economics, Elsevier, vol. 87(C).
    12. Cross, Jamie L. & Hou, Chenghan & Nguyen, Bao H., 2021. "On the China factor in the world oil market: A regime switching approach11We thank Hilde Bjørnland, Tatsuyoshi Okimoto, Ippei Fujiwara, Knut Aastveit, Leif Anders Thorsrud, Francesco Ravazzolo, Renee ," Energy Economics, Elsevier, vol. 95(C).
    13. Ali Jadidzadeh & Mobin Mirzababaei & Apostolos Serletis, 2022. "Oil Prices and the Hydrocarbon Markets: A Review," Energies, MDPI, vol. 15(17), pages 1-9, August.
    14. Rubaszek, Michał & Szafranek, Karol & Uddin, Gazi Salah, 2021. "The dynamics and elasticities on the U.S. natural gas market. A Bayesian Structural VAR analysis," Energy Economics, Elsevier, vol. 103(C).
    15. Núñez, Héctor M. & Trujillo-Barrera, Andres & Etienne, Xiaoli, 2022. "Declining integration in the US natural gas market," Resources Policy, Elsevier, vol. 78(C).
    16. Nguyen, Bao H. & Okimoto, Tatsuyoshi & Tran, Trung Duc, 2022. "Uncertainty-dependent and sign-dependent effects of oil market shocks," Journal of Commodity Markets, Elsevier, vol. 26(C).
    17. Kole, Erik & van Dijk, Dick, 2023. "Moments, shocks and spillovers in Markov-switching VAR models," Journal of Econometrics, Elsevier, vol. 236(2).
    18. Michael Weylandt & Yu Han & Katherine B. Ensor, 2019. "Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility," Papers 1907.10152, arXiv.org.
    19. Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2020. "Forecasting natural gas prices using highly flexible time-varying parameter models," Working Papers 2020-01, University of Tasmania, Tasmanian School of Business and Economics.
    20. Michał Rubaszek & Karol Szafranek, 2022. "Have European natural gas prices decoupled from crude oil prices? Evidence from TVP-VAR analysis," KAE Working Papers 2022-078, Warsaw School of Economics, Collegium of Economic Analysis.
    21. Vatsa, Puneet & Basnet, Hem C., 2020. "The dynamics of energy prices and the Norwegian economy: A common trends and common cycles analysis," Resources Policy, Elsevier, vol. 68(C).

    More about this item

    Keywords

    Natural gas market; Bayesian model comparison; Markov Switching VAR model;
    All these keywords.

    JEL classification:

    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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