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Which risk factors drive oil futures price curves?

Author

Listed:
  • Matthew Ames

    (Tokyo - The Institute of Statistical Mathematics)

  • Guillaume Bagnarosa

    (ESC [Rennes] - ESC Rennes School of Business, UCL - University College of London [London])

  • Tomoko Matsui

    (Tokyo - The Institute of Statistical Mathematics)

  • Gareth W. Peters

    (Department of Statistical Sciences - UCL - University College of London [London], University of Oxford, LSE - London School of Economics and Political Science, HWU - Heriot-Watt University [Edinburgh])

  • Pavel V. Shevchenko

    (Macquarie University)

Abstract

We develop extensions that introduce regression structure to the multi-factor stochastic models of commodity futures price term structure dynamics. We demonstrate the accuracy with which these models can be calibrated to oil futures data and how they improve on existing models both in model fit and in model interpretation. We found leading observable factors that contribute to explaining the term structure of oil futures, in the presence of long and short term stochastic factors, included the dollar index, inventories, commodity indices and risk aversion associated to financial intermediaries. Furthermore, we determine the time frame on which these factors are explanatory.

Suggested Citation

  • Matthew Ames & Guillaume Bagnarosa & Tomoko Matsui & Gareth W. Peters & Pavel V. Shevchenko, 2020. "Which risk factors drive oil futures price curves?," Post-Print hal-02779870, HAL.
    • Handle: RePEc:hal:journl:hal-02779870
    DOI: 10.1016/j.eneco.2020.104676
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    Citations

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    Cited by:

    1. Galán-Gutiérrez, Juan Antonio & Martín-García, Rodrigo, 2021. "Cointegration between the structure of copper futures prices and Brexit," Resources Policy, Elsevier, vol. 71(C).
    2. 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).
    3. Li, Jingyu & Liu, Ranran & Yao, Yanzhen & Xie, Qiwei, 2022. "Time-frequency volatility spillovers across the international crude oil market and Chinese major energy futures markets: Evidence from COVID-19," Resources Policy, Elsevier, vol. 77(C).
    4. Gong, Xu & Guan, Keqin & Chen, Liqing & Liu, Tangyong & Fu, Chengbo, 2021. "What drives oil prices? — A Markov switching VAR approach," Resources Policy, Elsevier, vol. 74(C).
    5. Juan Antonio Galán-Gutiérrez & Rodrigo Martín-García, 2022. "Fundamentals vs. Financialization during Extreme Events: From Backwardation to Contango, a Copper Market Analysis during the COVID-19 Pandemic," Mathematics, MDPI, vol. 10(4), pages 1-23, February.
    6. Galán-Gutiérrez, Juan Antonio & Labeaga, José M. & Martín-García, Rodrigo, 2023. "Cointegration between high base metals prices and backwardation: Getting ready for the metals super-cycle," Resources Policy, Elsevier, vol. 81(C).
    7. Han Jun S. & Kordzakhia Nino & Shevchenko Pavel V. & Trück Stefan, 2022. "On correlated measurement errors in the Schwartz–Smith two-factor model," Dependence Modeling, De Gruyter, vol. 10(1), pages 108-122, January.
    8. Li, Wenhui & Zhu, Qi & Wen, Fenghua & Nor, Normaziah Mohd, 2022. "The evolution of day-of-the-week and the implications in crude oil market," Energy Economics, Elsevier, vol. 106(C).

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