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Can the dynamics of the term structure of petroleum futures be forecasted? Evidence from major markets

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  • Chantziara, Thalia
  • Skiadopoulos, George
Abstract
We investigate whether the daily evolution of the term structure of petroleum futures can be forecasted. To this end, the principal components analysis is employed. The retained principal components describe the dynamics of the term structure of futures prices parsimoniously and are used to forecast the subsequent daily changes of futures prices. Data on the New York Mercantile Exchange (NYMEX) crude oil, heating oil, gasoline, and the International Petroleum Exchange (IPE) crude oil futures are used. We find that the retained principal components have small forecasting power both in-sample and out-of-sample. Similar results are obtained from standard univariate and vector autoregression models. Spillover effects between the four petroleum futures markets are also detected.

Suggested Citation

  • Chantziara, Thalia & Skiadopoulos, George, 2008. "Can the dynamics of the term structure of petroleum futures be forecasted? Evidence from major markets," Energy Economics, Elsevier, vol. 30(3), pages 962-985, May.
    • Handle: RePEc:eee:eneeco:v:30:y:2008:i:3:p:962-985
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    Cited by:

    1. Dennis Philip & Chihwa Kao & Giovanni Urga, 2007. "Testing for Instability in Factor Structure of Yield Curves," Center for Policy Research Working Papers 96, Center for Policy Research, Maxwell School, Syracuse University.
    2. Michael Ye & John Zyren & Joanne Shore & Thomas Lee, 2010. "Crude Oil Futures as an Indicator of Market Changes: A Graphical Analysis," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 16(3), pages 257-268, August.
    3. Yue-Hua Dai & Wen-Jie Xie & Zhi-Qiang Jiang & George J. Jiang & Wei-Xing Zhou, 2016. "Correlation structure and principal components in the global crude oil market," Empirical Economics, Springer, vol. 51(4), pages 1501-1519, December.
    4. Horváth, Lajos & Liu, Zhenya & Rice, Gregory & Wang, Shixuan, 2020. "A functional time series analysis of forward curves derived from commodity futures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 646-665.
    5. Charlot, Philippe & Darné, Olivier & Moussa, Zakaria, 2016. "Commodity returns co-movements: Fundamentals or “style” effect?," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 130-160.
    6. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    7. Zolotko, Mikhail & Okhrin, Ostap, 2014. "Modelling the general dependence between commodity forward curves," Energy Economics, Elsevier, vol. 43(C), pages 284-296.
    8. Cummins, Mark & Dowling, Michael & Kearney, Fearghal, 2016. "Oil market modelling: A comparative analysis of fundamental and latent factor approaches," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 211-218.
    9. Eirini Konstantinidi & Gkaren Papazian & George Skiadopoulos, 2015. "Modeling the Dynamics of Temperature with a View to Weather Derivatives," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 17, pages 511-544, World Scientific Publishing Co. Pte. Ltd..
    10. Atkins, Philip J. & Cummins, Mark, 2023. "Improved scalability and risk factor proxying with a two-step principal component analysis for multi-curve modelling," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1331-1348.
    11. Cavalcante, Mileno, 2010. "An Analysis of the relationship between WTI term structure and oil market fundamentals in 2002-2009," MPRA Paper 24263, University Library of Munich, Germany.
    12. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo, 2008. "Short-term predictability of crude oil markets: A detrended fluctuation analysis approach," Energy Economics, Elsevier, vol. 30(5), pages 2645-2656, September.
    13. Goulas, Lambros & Skiadopoulos, George, 2012. "Are freight futures markets efficient? Evidence from IMAREX," International Journal of Forecasting, Elsevier, vol. 28(3), pages 644-659.
    14. Butler, Sunil & Kokoszka, Piotr & Miao, Hong & Shang, Han Lin, 2021. "Neural network prediction of crude oil futures using B-splines," Energy Economics, Elsevier, vol. 94(C).
    15. Gogolin, Fabian & Kearney, Fearghal, 2016. "Does speculation impact what factors determine oil futures prices?," Economics Letters, Elsevier, vol. 144(C), pages 119-122.
    16. repec:kap:iaecre:v:16:y:2010:i:3:p:257-268 is not listed on IDEAS
    17. Bonnier, Jean-Baptiste, 2022. "Forecasting crude oil volatility with exogenous predictors: As good as it GETS?," Energy Economics, Elsevier, vol. 111(C).
    18. Daskalaki, Charoula & Skiadopoulos, George, 2011. "Should investors include commodities in their portfolios after all? New evidence," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2606-2626, October.
    19. Ruano, Fábio & Barros, Victor, 2022. "Commodities and portfolio diversification: Myth or fact?," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 281-295.

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