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Oil market modelling: A comparative analysis of fundamental and latent factor approaches

Author

Listed:
  • Mark Cummins

    (DCU - Dublin City University [Dublin])

  • Michael Dowling

    (ESC Rennes School of Business - ESC [Rennes] - ESC Rennes School of Business)

  • Fearghal Kearney

    (QUB - Queen's University [Belfast])

Abstract

We formally compare fundamental factor and latent factor approaches to oil price modelling. Fundamental modelling has a long history in seeking to understand oil price movements, while latent factor modelling has a more recent and limited history, but has gained popularity in other financial markets. The two approaches, though competing, have not formally been compared as to effectiveness. For a range of short-medium-and long-dated WTI oil futures we test a recently proposed five-factor fundamental model and a Principal Component Analysis latent factor model. Our findings demonstrate that there is no discernible difference between the two techniques in a dynamic setting. We conclude that this infers some advantages in adopting the latent factor approach due to the difficulty in determining a well specified fundamental model.

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  • Mark Cummins & Michael Dowling & Fearghal Kearney, 2016. "Oil market modelling: A comparative analysis of fundamental and latent factor approaches," Post-Print hal-01387596, HAL.
    • Handle: RePEc:hal:journl:hal-01387596
    DOI: 10.1016/j.irfa.2016.05.010
    Note: View the original document on HAL open archive server: https://rennes-sb.hal.science/hal-01387596
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    Cited by:

    1. Ames, Matthew & Bagnarosa, Guillaume & Matsui, Tomoko & Peters, Gareth W. & Shevchenko, Pavel V., 2020. "Which risk factors drive oil futures price curves?," Energy Economics, Elsevier, vol. 87(C).
    2. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).
    3. Muhammad Shahbaz & Arshian Sharif & Fateh Belaid & Xuan Vinh Vo, 2023. "Long‐run co‐variability between oil prices and economic policy uncertainty," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1308-1326, April.
    4. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2022. "Common factors and the dynamics of cereal prices. A forecasting perspective," Journal of Commodity Markets, Elsevier, vol. 28(C).
    5. Su, Zhi & Lu, Man & Yin, Libo, 2018. "Oil prices and news-based uncertainty: Novel evidence," Energy Economics, Elsevier, vol. 72(C), pages 331-340.
    6. Christina Anderl & Guglielmo Maria Caporale, 2024. "Functional Oil Price Expectations Shocks and Inflation," CESifo Working Paper Series 10998, CESifo.

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    More about this item

    Keywords

    Oil futures; Fundamental models; Latent factors; Vuong model comparison;
    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
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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