IDEAS home Printed from https://ideas.repec.org/a/sae/enejou/v27y2006i2p55-71.html
   My bibliography  Save this article

Testing for Market Integration Crude Oil, Coal, and Natural Gas

Author

Listed:
  • Lance J. Bachmeier
  • James M. Griffin

Abstract

Prompted by the contemporaneous spike in coal, oil, and natural gas prices, this paper evaluates the degree of market integration both within and between crude oil, coal, and natural gas markets. Our approach yields parameters that can be readily tested against a priori conjectures. Using daily price data for five very different crude oils, we conclude that the world oil market is a single, highly integrated economic market. On the other hand, coal prices at five trading locations across the United States are cointegrated, but the degree of market integration is much weaker, particularly between Western and Eastern coals. Finally, we show that crude oil, coal, and natural gas markets are only very weakly integrated. Our results indicate that there is not a primary energy market. Despite current price peaks, it is not useful to think of a primary energy market, except in a very long run context.

Suggested Citation

  • Lance J. Bachmeier & James M. Griffin, 2006. "Testing for Market Integration Crude Oil, Coal, and Natural Gas," The Energy Journal, , vol. 27(2), pages 55-71, April.
  • Handle: RePEc:sae:enejou:v:27:y:2006:i:2:p:55-71
    DOI: 10.5547/ISSN0195-6574-EJ-Vol27-No2-4
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.5547/ISSN0195-6574-EJ-Vol27-No2-4
    Download Restriction: no

    File URL: https://libkey.io/10.5547/ISSN0195-6574-EJ-Vol27-No2-4?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
    ---><---

    References listed on IDEAS

    as
    1. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    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. Takahashi, Makoto & Watanabe, Toshiaki & Omori, Yasuhiro, 2016. "Volatility and quantile forecasts by realized stochastic volatility models with generalized hyperbolic distribution," International Journal of Forecasting, Elsevier, vol. 32(2), pages 437-457.
    2. Hallin, Marc & La Vecchia, Davide, 2020. "A Simple R-estimation method for semiparametric duration models," Journal of Econometrics, Elsevier, vol. 218(2), pages 736-749.
    3. repec:kap:iaecre:v:14:y:2008:i:1:p:112-124 is not listed on IDEAS
    4. Bauwens, L. & Galli, F., 2009. "Efficient importance sampling for ML estimation of SCD models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1974-1992, April.
    5. Saulo, Helton & Balakrishnan, Narayanaswamy & Vila, Roberto, 2023. "On a quantile autoregressive conditional duration model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 425-448.
    6. Delis, Manthos D. & Hasan, Iftekhar & Tsionas, Efthymios G., 2015. "Firms' risk endogenous to strategic management choices," Bank of Finland Research Discussion Papers 16/2015, Bank of Finland.
    7. Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.
    8. Oscar Jorda & Holly Liu & Jeffrey Williams, 2003. "Non-Institutional Market Making Behavior: The Dalian Futures Exchange," Working Papers 41, University of California, Davis, Department of Economics.
    9. Maneesoonthorn, Worapree & Martin, Gael M. & Forbes, Catherine S. & Grose, Simone D., 2012. "Probabilistic forecasts of volatility and its risk premia," Journal of Econometrics, Elsevier, vol. 171(2), pages 217-236.
    10. Dimitrakopoulos, Stefanos & Tsionas, Mike, 2019. "Ordinal-response GARCH models for transaction data: A forecasting exercise," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1273-1287.
    11. Allen, David & Lazarov, Zdravetz & McAleer, Michael & Peiris, Shelton, 2009. "Comparison of alternative ACD models via density and interval forecasts: Evidence from the Australian stock market," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2535-2555.
    12. Nicholas Taylor, 2011. "Time-varying price discovery in fragmented markets," Applied Financial Economics, Taylor & Francis Journals, vol. 21(10), pages 717-734.
    13. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    14. Fernandes, Marcelo & Grammig, Joachim, 2005. "Nonparametric specification tests for conditional duration models," Journal of Econometrics, Elsevier, vol. 127(1), pages 35-68, July.
    15. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    16. Li, W. K. & Yu, Philip L. H., 2003. "On the residual autocorrelation of the autoregressive conditional duration model," Economics Letters, Elsevier, vol. 79(2), pages 169-175, May.
    17. Bill M. Cai & Charlie X. Cai & Kevin Keasey, 2007. "Influence of cultural factors on price clustering and price resistance in China's stock markets," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 47(4), pages 623-641, December.
    18. Pipat Wongsaart & Jiti Gao, 2011. "Nonparametric Kernel Testing in Semiparametric Autoregressive Conditional Duration Model," Monash Econometrics and Business Statistics Working Papers 18/11, Monash University, Department of Econometrics and Business Statistics.
    19. Misaki, Hiroumi & Kunitomo, Naoto, 2015. "On robust properties of the SIML estimation of volatility under micro-market noise and random sampling," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 265-281.
    20. Fu, Qi & So, Jacky Yuk-Chow & Li, Xiaotong, 2024. "Stable paretian distribution, return generating processes and habit formation—The implication for equity premium puzzle," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    21. Gurgul Henryk & Machno Artur, 2017. "Trade Pattern on Warsaw Stock Exchange and Prediction of Number of Trades," Statistics in Transition New Series, Polish Statistical Association, vol. 18(1), pages 91-114, March.

    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:sae:enejou:v:27:y:2006:i:2:p:55-71. 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: SAGE Publications (email available below). General contact details of provider: .

    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.