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Price relationships in crude oil futures: new evidence from CFTC disaggregated data

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  • Julien Chevallier

Abstract

This paper attempts to reconcile two strands of literature on oil and speculation: one that posits the predominance of supply/demand fundamentals, and one that investigates the hypothesis of speculative trading. To do so, we develop a Markov switching analysis based on the WTI crude oil futures price, CFTC disaggregated data, and fundamentals of the oil price. The benefits of this approach are twofold: (1) the model is able to track changes in the underlying business cycle, and (2) the model explicitly incorporates data on the net positions of money managers as a proxy for speculative activity. After verifying the sensitivity of our results to the inclusion of supply and demand factors on the oil market, we cannot eliminate statistically the possibility of speculation among the main reasons behind the 2008 oil price swing. We also explicitly recognize the influence of many other economic variables during that specific time period. Copyright Springer 2013

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  • Julien Chevallier, 2013. "Price relationships in crude oil futures: new evidence from CFTC disaggregated data," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 15(2), pages 133-170, April.
    • Handle: RePEc:spr:envpol:v:15:y:2013:i:2:p:133-170
    DOI: 10.1007/s10018-012-0045-3
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    Cited by:

    1. Julien, Chevallier & Sévi, Benoît, 2013. "A Fear Index to Predict Oil Futures Returns," Energy: Resources and Markets 156489, Fondazione Eni Enrico Mattei (FEEM).
    2. Lubnau, Thorben & Todorova, Neda, 2015. "Trading on mean-reversion in energy futures markets," Energy Economics, Elsevier, vol. 51(C), pages 312-319.
    3. repec:dau:papers:123456789/11714 is not listed on IDEAS
    4. Frank Venmans, 2015. "Capital market response to emission allowance prices: a multivariate GARCH approach," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 17(4), pages 577-620, October.
    5. Yan, Lei & Irwin, Scott H. & Sanders, Dwight R., 2018. "Mapping algorithms, agricultural futures, and the relationship between commodity investment flows and crude oil futures prices," Energy Economics, Elsevier, vol. 72(C), pages 486-504.
    6. Ayben Koy, 2017. "Modelling Nonlinear Dynamics of Oil Futures Market," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 2(1), pages 23-42, June.

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    More about this item

    Keywords

    Crude oil futures; Speculation; CFTC disaggregated data; Markov-switching model; C32; G12; G15; Q43;
    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
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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