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Pipeline capacity and the dynamics of Alberta crude oil price spreads

Author

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  • Gregory Galay

    () (Department of Economics and Finance, University of Guelph, Guelph ON Canada)

  • Henry Thille

    () (Department of Economics and Finance, University of Guelph, Guelph ON Canada)

Abstract

From 2011 until the end of 2014, a larger than normal price spread emerged between West Texas Intermediate (WTI) and Western Canadian Select (WCS). This led many participants in Canada’s energy sector to advocate for the expansion of Canada’s crude oil pipeline system as they believed that excess supply could not be moved from production regions in Northern Alberta to those markets that would yield the highest return. This article considers the impact constrained transportation capacity has on the price spread between WCS and other world prices such as WTI. A Markov-switching model is used to identify regimes associated with binding/non-binding pipeline capacity. Our results confirm the predictions of models of spatial arbitrage under capacity constraints. When there is sufficient transportation capacity the price spreads reflect transport costs (includes fees, insurance, etc.) plus any premium for the quality difference between the crude oils compared. However, during periods of tight capacity the spread becomes more volatile and on average exceeds transport costs plus the quality premium. We compare our results to newly available pipeline data and find that periods of tight capacity as identified through the price data are substantially fewer than that suggested by the pipeline capacity data.

Suggested Citation

  • Gregory Galay & Henry Thille, 2018. "Pipeline capacity and the dynamics of Alberta crude oil price spreads," Working Papers 1804, University of Guelph, Department of Economics and Finance.
  • Handle: RePEc:gue:guelph:2018-04
    as

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    File URL: http://www.uoguelph.ca/economics/repec/workingpapers/2018/2018-04.pdf
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    References listed on IDEAS

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    Keywords

    Crude oil prices; spatial pricing; pipeline congestion; Markov-switching autoregression;
    All these keywords.

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