IDEAS home Printed from https://ideas.repec.org/a/eee/jocoma/v6y2017icp16-31.html

A Markov regime-switching model of crude oil market integration

Author

Listed:
  • Kuck, Konstantin
  • Schweikert, Karsten

Abstract

This paper revisits the globalization-regionalization hypothesis for the world crude oil market. We examine long-run equilibrium relationships between major crude oil prices–WTI, Brent, Bonny Light, Dubai and Tapis–and focus on the adjustment behaviour following disequilibrium states. We account for a changing adjustment behaviour over time by using a Markov-switching vector error correction model. Our overall findings suggest that the crude oil market is globalized. Dubai turned out to be the only weakly exogenous price in all regimes, indicating its important role as a benchmark price. Furthermore, an interesting finding of our study is that the degree of market integration seems to be connected to global economic uncertainty.

Suggested Citation

  • Kuck, Konstantin & Schweikert, Karsten, 2017. "A Markov regime-switching model of crude oil market integration," Journal of Commodity Markets, Elsevier, vol. 6(C), pages 16-31.
    • Handle: RePEc:eee:jocoma:v:6:y:2017:i:c:p:16-31
    DOI: 10.1016/j.jcomm.2017.03.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2405851316301155
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jcomm.2017.03.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Reboredo, Juan C., 2011. "How do crude oil prices co-move?: A copula approach," Energy Economics, Elsevier, vol. 33(5), pages 948-955, September.
    2. repec:aen:journl:1984v05-03-a01 is not listed on IDEAS
    3. repec:aen:journl:2011v32-02-a07 is not listed on IDEAS
    4. repec:aen:journl:2008v29-04-a04 is not listed on IDEAS
    5. Zhang, Yue-Jun & Zhang, Lu, 2015. "Interpreting the crude oil price movements: Evidence from the Markov regime switching model," Applied Energy, Elsevier, vol. 143(C), pages 96-109.
    6. Liu, Li & Chen, Ching-Cheng & Wan, Jieqiu, 2013. "Is world oil market “one great pool”?: An example from China's and international oil markets," Economic Modelling, Elsevier, vol. 35(C), pages 364-373.
    7. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    8. Höök, Mikael & Hirsch, Robert & Aleklett, Kjell, 2009. "Giant oil field decline rates and their influence on world oil production," Energy Policy, Elsevier, vol. 37(6), pages 2262-2272, June.
    9. Candelon, Bertrand & Joëts, Marc & Tokpavi, Sessi, 2013. "Testing for Granger causality in distribution tails: An application to oil markets integration," Economic Modelling, Elsevier, vol. 31(C), pages 276-285.
    10. Weiner, R.J., 1991. "Is the World Oil Market "One Great Pool?"," Papers 9120, Laval - Recherche en Energie.
    11. Fattouh, Bassam, 2010. "The dynamics of crude oil price differentials," Energy Economics, Elsevier, vol. 32(2), pages 334-342, March.
    12. Szymon Wlazlowski & Bjorn Hagstromer & Monica Giulietti, 2011. "Causality in crude oil prices," Applied Economics, Taylor & Francis Journals, vol. 43(24), pages 3337-3347.
    13. repec:aen:journl:1991v12-03-a07 is not listed on IDEAS
    14. Mann, Janelle & Sephton, Peter, 2016. "Global relationships across crude oil benchmarks," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 1-5.
    15. Lu, Feng-bin & Hong, Yong-miao & Wang, Shou-yang & Lai, Kin-keung & Liu, John, 2014. "Time-varying Granger causality tests for applications in global crude oil markets," Energy Economics, Elsevier, vol. 42(C), pages 289-298.
    16. Jan Bentzen, 2007. "Does OPEC influence crude oil prices? Testing for co-movements and causality between regional crude oil prices," Applied Economics, Taylor & Francis Journals, vol. 39(11), pages 1375-1385.
    17. Osterwald-Lenum, Michael, 1992. "A Note with Quantiles of the Asymptotic Distribution of the Maximum Likelihood Cointegration Rank Test Statistics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 461-472, August.
    18. Zacharias Psaradakis & Nicola Spagnolo, 2006. "Joint Determination of the State Dimension and Autoregressive Order for Models with Markov Regime Switching," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 753-766, September.
    19. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    20. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    21. Junsoo Lee & Mark C. Strazicich, 2003. "Minimum Lagrange Multiplier Unit Root Test with Two Structural Breaks," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 1082-1089, November.
    22. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    23. Neil A. Wilmot, 2013. "Cointegration in the Oil Market among Regional Blends," International Journal of Energy Economics and Policy, Econjournals, vol. 3(4), pages 424-433.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Niyati Bhanja & Samia Nasreen & Arif Billah Dar & Aviral Kumar Tiwari, 2022. "Connectedness in International Crude Oil Markets," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 227-262, January.
    2. Zhang, Qi & Di, Peng & Farnoosh, Arash, 2021. "Study on the impacts of Shanghai crude oil futures on global oil market and oil industry based on VECM and DAG models," Energy, Elsevier, vol. 223(C).
    3. Jia, Xiaoliang & An, Haizhong & Sun, Xiaoqi & Huang, Xuan & Wang, Lijun, 2017. "Evolution of world crude oil market integration and diversification: A wavelet-based complex network perspective," Applied Energy, Elsevier, vol. 185(P2), pages 1788-1798.
    4. 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.
    5. Apostolakis, George N. & Floros, Christos & Gkillas, Konstantinos & Wohar, Mark, 2024. "Volatility spillovers across the spot and futures oil markets after news announcements," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    6. Stavros Stavroyiannis, 2022. "Cointegration and ARDL specification between the Dubai crude oil and the US natural gas market," Papers 2206.03278, arXiv.org.
    7. Caporin, Massimiliano & Fontini, Fulvio & Talebbeydokhti, Elham, 2019. "Testing persistence of WTI and Brent long-run relationship after the shale oil supply shock," Energy Economics, Elsevier, vol. 79(C), pages 21-31.
    8. Luong, Phat V., 2023. "Crude oil pipeline constraints: A tale of two shales," Resources Policy, Elsevier, vol. 80(C).
    9. Gregory Galay & Henry Thille, 2021. "Pipeline capacity and the dynamics of Alberta crude oil price spreads," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 54(3), pages 1072-1102, November.
    10. Jena, Sangram Keshari & Tiwari, Aviral Kumar & Aikins Abakah, Emmanuel Joel & Hammoudeh, Shawkat, 2022. "The connectedness in the world petroleum futures markets using a Quantile VAR approach," Journal of Commodity Markets, Elsevier, vol. 27(C).
    11. Niyati Bhanja & Arif Billah Dar & Aviral Kumar Tiwari, 2018. "Do Global Crude Oil Markets Behave as One Great Pool? A Cyclical Analysis," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(2), pages 219-241, November.
    12. Batten, Jonathan A. & Kinateder, Harald & Szilagyi, Peter G. & Wagner, Niklas F., 2019. "Time-varying energy and stock market integration in Asia," Energy Economics, Elsevier, vol. 80(C), pages 777-792.
    13. Cui, Jinxin & Alshater, Muneer M. & Mensi, Walid, 2023. "Higher-order moment risk spillovers and optimal portfolio strategies in global oil markets," Resources Policy, Elsevier, vol. 86(PA).
    14. Beckmann, Joscha & Czudaj, Robert, 2013. "Oil prices and effective dollar exchange rates," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 621-636.
    15. Samih Antoine Azar & Angelic Salha, 2017. "The Bias in the Long Run Relation between the Prices of BRENT and West Texas Intermediate Crude Oils," International Journal of Energy Economics and Policy, Econjournals, vol. 7(1), pages 44-54.
    16. Xiaoyong Xiao & Jing Huang, 2018. "Dynamic Connectedness of International Crude Oil Prices: The Diebold–Yilmaz Approach," Sustainability, MDPI, vol. 10(9), pages 1-16, September.
    17. Ji, Qiang & Fan, Ying, 2016. "Evolution of the world crude oil market integration: A graph theory analysis," Energy Economics, Elsevier, vol. 53(C), pages 90-100.
    18. Bravo Caro, José Manuel & Golpe, Antonio A. & Iglesias, Jesús & Vides, José Carlos, 2020. "A new way of measuring the WTI – Brent spread. Globalization, shock persistence and common trends," Energy Economics, Elsevier, vol. 85(C).
    19. 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.
    20. Monge, Manuel & Gil-Alana, Luis Alberiko, 2021. "Spatial crude oil production divergence and crude oil price behaviour in the United States," Energy, Elsevier, vol. 232(C).

    More about this item

    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
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jocoma:v:6:y:2017:i:c:p:16-31. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jcomm .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.