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A unified world oil market: Regions in physical, economic, geographic, and political space

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  • Kaufmann, Robert K.
  • Banerjee, Shayan

Abstract

Although there is a general consensus that the market is unified, here we quantify the factors that create regions by analyzing the price relation between 33 crude oils. ADF statistics indicate that 447 of the 528 crude oil pairings cointegrate; 81 do not. The presence/absence of cointegration is analyzed using a logit model. The likelihood that the prices for two crude oils cointegrate depends on their physical characteristics (density and sulfur content), economic factors (country risk for the nation of origin), their geographic location (distance between supply ports), and political factors (OPEC membership). Over the sample period, the technology to refine heavy crude oils penetrates the market, and this reduces the price difference between heavy and light crude oils. The effect of country risk implies that crude oils from high risk nations are not perfect substitutes for crude oils of similar quality from low risk nations. Finally, crude oils from widely separated suppliers are more likely to cointegrate than crude oils from near-by nations, which suggests consumers diversify supply across transportation chokepoints. For this sample, these sources of regionalization add $0.20 per barrel to the $2.86 average price difference between crude oils in the same market. Together, these factors have important implications for the efficacy of policy aimed at reducing dependence on unreliable suppliers and the spill-over effects of holding inventories.

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  • Kaufmann, Robert K. & Banerjee, Shayan, 2014. "A unified world oil market: Regions in physical, economic, geographic, and political space," Energy Policy, Elsevier, vol. 74(C), pages 235-242.
    • Handle: RePEc:eee:enepol:v:74:y:2014:i:c:p:235-242
    DOI: 10.1016/j.enpol.2014.08.028
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    3. Luo, Jiawen & Klein, Tony & Walther, Thomas & Ji, Qiang, 2021. "Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning," QBS Working Paper Series 2021/04, Queen's University Belfast, Queen's Business School.
    4. Kaufmann, Robert K., 2016. "Price differences among crude oils: The private costs of supply disruptions," Energy Economics, Elsevier, vol. 56(C), pages 1-8.
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    6. Luo, Jiawen & Ji, Qiang & Klein, Tony & Todorova, Neda & Zhang, Dayong, 2020. "On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks," Energy Economics, Elsevier, vol. 89(C).
    7. Yuksel Haliloglu, Ebru & Sahin, Serkan & Berument, M. Hakan, 2021. "Brent–Dubai oil spread: Basic drivers," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 492-505.
    8. Klein, Tony, 2018. "Trends and contagion in WTI and Brent crude oil spot and futures markets - The role of OPEC in the last decade," Energy Economics, Elsevier, vol. 75(C), pages 636-646.
    9. Klein, Tony, 2018. "Trends and Contagion in WTI and Brent Crude Oil Spot and Futures Markets - The Role of OPEC in the last Decade," QBS Working Paper Series 2018/05, Queen's University Belfast, Queen's Business School.
    10. Zhang, Dayong & Ji, Qiang & Kutan, Ali M., 2019. "Dynamic transmission mechanisms in global crude oil prices: Estimation and implications," Energy, Elsevier, vol. 175(C), pages 1181-1193.

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