IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v313y2022i2d10.1007_s10479-020-03652-2.html
   My bibliography  Save this article

On the relationship between oil and gas markets: a new forecasting framework based on a machine learning approach

Author

Listed:
  • Zied Ftiti

    (EDC Paris Business School
    High Business Institute of Management, University of Tunis)

  • Kais Tissaoui

    (University of Ha’il
    University Tunis ElManar)

  • Sahbi Boubaker

    (University of Jeddah
    University of Monastir)

Abstract

Owing to the uncertainty around the coupling and decoupling of oil and gas prices, this study re-examines the relationship between oil and gas markets by modeling the price of one energy source based on the price of the other, both linearly and nonlinearly. We present an autoregressive exogenous model and three nonlinear frameworks with different patterns of asymmetry. Based on daily data from January 7, 1997, to December 29, 2017, our analysis reaches two main findings. First, the nonlinear frameworks outperform the linear model (i.e., the autoregressive exogenous model) in modeling the relationship between oil and gas prices. Second, the nature of asymmetry varies based on market direction. We show that when oil prices exhibit an extreme movement (i.e., beyond a threshold value in absolute value), gas prices react nonlinearly, and that there is no relationship otherwise. Our results are robust for other frequencies, mainly weekly and monthly. These findings explain the conflicting results in the literature on the complex relationship between these markets. The results might serve investors in term of hedging, portfolio diversification, and asset allocation as we show that in the calm period, there is no relationship between oil and gas prices; however, the interaction between markets is more pronounced during periods of extreme movement. Similarly, policymakers’ awareness of the nonlinear dynamic under extreme movements could inform the regulation policy and/or adjustment in case oil (gas) prices increase or decrease.

Suggested Citation

  • Zied Ftiti & Kais Tissaoui & Sahbi Boubaker, 2022. "On the relationship between oil and gas markets: a new forecasting framework based on a machine learning approach," Annals of Operations Research, Springer, vol. 313(2), pages 915-943, June.
    • Handle: RePEc:spr:annopr:v:313:y:2022:i:2:d:10.1007_s10479-020-03652-2
    DOI: 10.1007/s10479-020-03652-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-020-03652-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-020-03652-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hillard G. Huntington, 1994. "Oil Price Forecasting in the 1980s: What Went Wrong?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-22.
    2. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    3. Stephen P. A. Brown & Mine K. Yucel, 2008. "What Drives Natural Gas Prices?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 45-60.
    4. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 427-507, Elsevier.
    5. Batten, Jonathan A. & Ciner, Cetin & Lucey, Brian M., 2017. "The dynamic linkages between crude oil and natural gas markets," Energy Economics, Elsevier, vol. 62(C), pages 155-170.
    6. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    7. repec:bla:opecrv:v:33:y:2009:i:2:p:111-124 is not listed on IDEAS
    8. Lai, YiHao & Chen, Cathy W.S. & Gerlach, Richard, 2009. "Optimal dynamic hedging via copula-threshold-GARCH models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2609-2624.
    9. Bernard, Jean-Thomas & Khalaf, Lynda & Kichian, Maral & Yelou, Clement, 2018. "Oil Price Forecasts For The Long Term: Expert Outlooks, Models, Or Both?," Macroeconomic Dynamics, Cambridge University Press, vol. 22(3), pages 581-599, April.
    10. Mohammadi, Hassan & Su, Lixian, 2010. "International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models," Energy Economics, Elsevier, vol. 32(5), pages 1001-1008, September.
    11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    12. repec:clg:wpaper:1999-04 is not listed on IDEAS
    13. David J. Ramberg and John E. Parsons, 2012. "The Weak Tie Between Natural Gas and Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    14. Christiane Baumeister & Lutz Kilian, 2014. "What Central Bankers Need To Know About Forecasting Oil Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 869-889, August.
    15. Xun Lu & Kin Lai & Liang Liang, 2014. "Portfolio value-at-risk estimation in energy futures markets with time-varying copula-GARCH model," Annals of Operations Research, Springer, vol. 219(1), pages 333-357, August.
    16. Apostolos Serletis & John Herbert, 2007. "The Message in North American Energy Prices," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 13, pages 156-171, World Scientific Publishing Co. Pte. Ltd..
    17. Nick, Sebastian & Thoenes, Stefan, 2014. "What drives natural gas prices? — A structural VAR approach," Energy Economics, Elsevier, vol. 45(C), pages 517-527.
    18. repec:ipg:wpaper:2014-569 is not listed on IDEAS
    19. Anna Creti & Duc Khuong Nguyen, 2015. "Energy markets׳ financialization, risk spillovers, and pricing models," Post-Print hal-01517413, HAL.
    20. Brigida, Matthew, 2014. "The switching relationship between natural gas and crude oil prices," Energy Economics, Elsevier, vol. 43(C), pages 48-55.
    21. Hooker, Mark A, 2002. "Are Oil Shocks Inflationary? Asymmetric and Nonlinear Specifications versus Changes in Regime," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(2), pages 540-561, May.
    22. repec:dau:papers:123456789/14774 is not listed on IDEAS
    23. Zhang, Jin-Liang & Zhang, Yue-Jun & Zhang, Lu, 2015. "A novel hybrid method for crude oil price forecasting," Energy Economics, Elsevier, vol. 49(C), pages 649-659.
    24. Tissaoui, Kais, 2019. "Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 232-249.
    25. Aloui, Riadh & Ben Aïssa, Mohamed Safouane & Nguyen, Duc Khuong, 2013. "Conditional dependence structure between oil prices and exchange rates: A copula-GARCH approach," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 719-738.
    26. Baumeister, Christiane & Kilian, Lutz & Lee, Thomas K., 2014. "Are there gains from pooling real-time oil price forecasts?," Energy Economics, Elsevier, vol. 46(S1), pages 33-43.
    27. Atil, Ahmed & Lahiani, Amine & Nguyen, Duc Khuong, 2014. "Asymmetric and nonlinear pass-through of crude oil prices to gasoline and natural gas prices," Energy Policy, Elsevier, vol. 65(C), pages 567-573.
    28. Onour, Ibrahim, 2009. "Natural Gas markets:How Sensitive to Crude Oil Price Changes?," MPRA Paper 14937, University Library of Munich, Germany.
    29. Lance J. Bachmeier & James M. Griffin, 2006. "Testing for Market Integration: Crude Oil, Coal, and Natural Gas," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 55-72.
    30. Frank Asche & Petter Osmundsen & Maria Sandsmark, 2006. "The UK Market for Natural Gas, Oil and Electricity: Are the Prices Decoupled?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 27-40.
    31. de Souza e Silva, Edmundo G. & Legey, Luiz F.L. & de Souza e Silva, Edmundo A., 2010. "Forecasting oil price trends using wavelets and hidden Markov models," Energy Economics, Elsevier, vol. 32(6), pages 1507-1519, November.
    32. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    33. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    34. Wang, Jie & Wang, Jun, 2016. "Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations," Energy, Elsevier, vol. 102(C), pages 365-374.
    35. Taiyong Li & Min Zhou & Chaoqi Guo & Min Luo & Jiang Wu & Fan Pan & Quanyi Tao & Ting He, 2016. "Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels," Energies, MDPI, vol. 9(12), pages 1, December.
    36. Zhang, Dayong & Ji, Qiang, 2018. "Further evidence on the debate of oil-gas price decoupling: A long memory approach," Energy Policy, Elsevier, vol. 113(C), pages 68-75.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Praveen Puram & Soumya Roy & Deepak Srivastav & Anand Gurumurthy, 2023. "Understanding the effect of contextual factors and decision making on team performance in Twenty20 cricket: an interpretable machine learning approach," Annals of Operations Research, Springer, vol. 325(1), pages 261-288, June.
    2. Naeem, Muhammad Abubakr & Iqbal, Najaf & Karim, Sitara & Lucey, Brian M., 2023. "From forests to faucets to fuel: Investigating the domino effect of extreme risk in timber, water, and energy markets," Finance Research Letters, Elsevier, vol. 55(PB).
    3. Tiwari, Aviral Kumar & Sharma, Gagan Deep & Rao, Amar & Hossain, Mohammad Razib & Dev, Dhairya, 2024. "Unraveling the crystal ball: Machine learning models for crude oil and natural gas volatility forecasting," Energy Economics, Elsevier, vol. 134(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
    2. Batten, Jonathan A. & Ciner, Cetin & Lucey, Brian M., 2017. "The dynamic linkages between crude oil and natural gas markets," Energy Economics, Elsevier, vol. 62(C), pages 155-170.
    3. Gatfaoui, Hayette, 2016. "Linking the gas and oil markets with the stock market: Investigating the U.S. relationship," Energy Economics, Elsevier, vol. 53(C), pages 5-16.
    4. Palma, Alessia & Paltrinieri, Andrea & Goodell, John W. & Oriani, Marco Ercole, 2024. "The black box of natural gas market: Past, present, and future," International Review of Financial Analysis, Elsevier, vol. 94(C).
    5. Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Mariem Nsaibi, 2023. "Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 663-687, August.
    6. Ji, Qiang & Zhang, Hai-Ying & Geng, Jiang-Bo, 2018. "What drives natural gas prices in the United States? – A directed acyclic graph approach," Energy Economics, Elsevier, vol. 69(C), pages 79-88.
    7. Wang, TianTian & Zhang, Dayong & Clive Broadstock, David, 2019. "Financialization, fundamentals, and the time-varying determinants of US natural gas prices," Energy Economics, Elsevier, vol. 80(C), pages 707-719.
    8. Zhang, Dayong & Shi, Min & Shi, Xunpeng, 2018. "Oil indexation, market fundamentals, and natural gas prices: An investigation of the Asian premium in natural gas trade," Energy Economics, Elsevier, vol. 69(C), pages 33-41.
    9. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
    10. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    11. Ramberg, David J. & Henry Chen, Y.H. & Paltsev, Sergey & Parsons, John E., 2017. "The economic viability of gas-to-liquids technology and the crude oil–natural gas price relationship," Energy Economics, Elsevier, vol. 63(C), pages 13-21.
    12. Scarcioffolo, Alexandre R. & Etienne, Xiaoli, 2021. "Testing directional predictability between energy prices: A quantile-based analysis," Resources Policy, Elsevier, vol. 74(C).
    13. Degiannakis, Stavros & Filis, George, 2023. "Oil price assumptions for macroeconomic policy," Energy Economics, Elsevier, vol. 117(C).
    14. Wang, Tiantian & Qu, Wan & Zhang, Dayong & Ji, Qiang & Wu, Fei, 2022. "Time-varying determinants of China's liquefied natural gas import price: A dynamic model averaging approach," Energy, Elsevier, vol. 259(C).
    15. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    16. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
    17. Ali Jadidzadeh & Mobin Mirzababaei & Apostolos Serletis, 2022. "Oil Prices and the Hydrocarbon Markets: A Review," Energies, MDPI, vol. 15(17), pages 1-9, August.
    18. Zhang, Dayong & Wang, Tiantian & Shi, Xunpeng & Liu, Jia, 2018. "Is hub-based pricing a better choice than oil indexation for natural gas? Evidence from a multiple bubble test," Energy Economics, Elsevier, vol. 76(C), pages 495-503.
    19. Lahiani, Amine & Miloudi, Anthony & Benkraiem, Ramzi & Shahbaz, Muhammad, 2017. "Another look on the relationships between oil prices and energy prices," Energy Policy, Elsevier, vol. 102(C), pages 318-331.
    20. Ji, Qiang & Geng, Jiang-Bo & Tiwari, Aviral Kumar, 2018. "Information spillovers and connectedness networks in the oil and gas markets," Energy Economics, Elsevier, vol. 75(C), pages 71-84.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:313:y:2022:i:2:d:10.1007_s10479-020-03652-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.