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The impact of fundamental and financial traders on the term structure of oil

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  • Heidorn, Thomas
  • Mokinski, Frieder
  • Rühl, Christoph
  • Schmaltz, Christian

Abstract

We study how the exposure of fundamental and financial traders affects the futures curve of WTI oil and the market integration between WTI and Brent as measured by their price spread. To obtain a parsimonious representation of the futures curve, we decompose it into a level-, a slope- and a curvature factor. In a second step, we separately regress each extracted factor on measures of the market exposure of fundamental and financial traders revealing whether and how the exposure of the two trader groups affects the different dimensions of the futures curve. Spanning from 2006 until 2012, our dataset covers sub-periods of a sharp WTI-price rise as well as a diverging Brent–WTI-spread. Our contribution is threefold: First, we suggest that it is important to distinguish between level and slope as we find that fundamental traders have a measurable impact on the level of the futures curve, but do not play much of a role for its slope or curvature, whereas the exposure of financial traders mainly influences the slope of the futures curve. Despite allegations to the contrary, we find no evidence of a systematic impact of non-fundamental traders on the level of the futures curve, for example during the 2006–2008 oil price surge. Second, we suggest using relative short- and relative long positions for fundamental and financial traders instead of the net position as the former reflect better the overall economic positioning of each group. Third, we find that the exposure of financials is the key driver of the Brent–WTI spread. It confirms that financial rather than fundamental traders are responsible for integrating the two markets.

Suggested Citation

  • Heidorn, Thomas & Mokinski, Frieder & Rühl, Christoph & Schmaltz, Christian, 2015. "The impact of fundamental and financial traders on the term structure of oil," Energy Economics, Elsevier, vol. 48(C), pages 276-287.
  • Handle: RePEc:eee:eneeco:v:48:y:2015:i:c:p:276-287
    DOI: 10.1016/j.eneco.2015.01.001
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    References listed on IDEAS

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    1. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    2. Tokic, Damir, 2011. "Rational destabilizing speculation, positive feedback trading, and the oil bubble of 2008," Energy Policy, Elsevier, vol. 39(4), pages 2051-2061, April.
    3. Ing-Haw Cheng & Andrei Kirilenko & Wei Xiong, 2015. "Convective Risk Flows in Commodity Futures Markets," Review of Finance, European Finance Association, vol. 19(5), pages 1733-1781.
    4. Irwin, Scott H. & Sanders, Dwight R., 2012. "Testing the Masters Hypothesis in commodity futures markets," Energy Economics, Elsevier, vol. 34(1), pages 256-269.
    5. Jacks, David S., 2007. "Populists versus theorists: Futures markets and the volatility of prices," Explorations in Economic History, Elsevier, vol. 44(2), pages 342-362, April.
    6. Bahattin Buyuksahin & Jeffrey H. Harris, 2011. "Do Speculators Drive Crude Oil Futures Prices?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 167-202.
    7. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    8. Diebold, Francis X. & Rudebusch, Glenn D. & Borag[caron]an Aruoba, S., 2006. "The macroeconomy and the yield curve: a dynamic latent factor approach," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 309-338.
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    Cited by:

    1. van Huellen, Sophie, 2019. "Price discovery in commodity futures and cash markets with heterogeneous agents," Journal of International Money and Finance, Elsevier, vol. 95(C), pages 1-13.
    2. Liu, Li & Wang, Yudong & Wu, Chongfeng & Wu, Wenfeng, 2016. "Disentangling the determinants of real oil prices," Energy Economics, Elsevier, vol. 56(C), pages 363-373.
    3. Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.
    4. van Huellen, Sophie, 2020. "Too much of a good thing? Speculative effects on commodity futures curves," Journal of Financial Markets, Elsevier, vol. 47(C).

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    More about this item

    Keywords

    WTI; Price speculation; Oil price rise; Market integration;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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