IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_11017.html
   My bibliography  Save this paper

Informational Efficiency of World Oil Markets: One Great Pool, but with Varying Depth

Author

Listed:
  • Marc Gronwald
  • Sania Wadud
  • Kingsley Dogah

Abstract

This paper investigates the informational efficiency of global crude oil markets using a recently introduced quantitative measure for market inefficiency. The methodology assesses the deviation of observed oil price behavior from the Random Walk benchmark, representing an efficient market. The main findings of the analysis are as follows: firstly, the degree of crude oil market inefficiency demonstrates temporal variations. Secondly, there are marked increases in the degree of inefficiency during extreme episodes, such as the price downturns experienced in 2008, 2014, and early 2020. Thirdly, the degree of inefficiency exhibits substantial variations across regional crude oil markets before 2006 but converges thereafter. Since this discovery is grounded in the observation of more similar price behavior across markets post-2006, the paper establishes a connection between the literature on oil market integration and that focusing on the informational efficiency of oil prices.

Suggested Citation

  • Marc Gronwald & Sania Wadud & Kingsley Dogah, 2024. "Informational Efficiency of World Oil Markets: One Great Pool, but with Varying Depth," CESifo Working Paper Series 11017, CESifo.
    • Handle: RePEc:ces:ceswps:_11017
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp11017.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Søren Johansen & Morten Ørregaard Nielsen, 2012. "Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model," Econometrica, Econometric Society, vol. 80(6), pages 2667-2732, November.
    2. Horta, Paulo & Lagoa, Sérgio & Martins, Luís, 2014. "The impact of the 2008 and 2010 financial crises on the Hurst exponents of international stock markets: Implications for efficiency and contagion," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 140-153.
    3. Abadir, Karim M. & Distaso, Walter & Giraitis, Liudas, 2007. "Nonstationarity-extended local Whittle estimation," Journal of Econometrics, Elsevier, vol. 141(2), pages 1353-1384, December.
    4. Kristoufek, Ladislav, 2019. "Are the crude oil markets really becoming more efficient over time? Some new evidence," Energy Economics, Elsevier, vol. 82(C), pages 253-263.
    5. Duan, Kun & Li, Zeming & Urquhart, Andrew & Ye, Jinqiang, 2021. "Dynamic efficiency and arbitrage potential in Bitcoin: A long-memory approach," International Review of Financial Analysis, Elsevier, vol. 75(C).
    6. Johansen, SØren, 2008. "A Representation Theory For A Class Of Vector Autoregressive Models For Fractional Processes," Econometric Theory, Cambridge University Press, vol. 24(3), pages 651-676, June.
    7. Michael Plante and Grant Strickler, 2021. "Closer to One Great Pool? Evidence from Structural Breaks in Oil Price Differentials," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-30.
    8. V Dimitrova & M Fernández-Martínez & M A Sánchez-Granero & J E Trinidad Segovia, 2019. "Some comments on Bitcoin market (in)efficiency," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-14, July.
    9. Shimotsu, Katsumi & Phillips, Peter C.B., 2006. "Local Whittle estimation of fractional integration and some of its variants," Journal of Econometrics, Elsevier, vol. 130(2), pages 209-233, February.
    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. Marc Gronwald & Sania Wadud & Kingsley Dogah, 2024. "Oil Market Efficiency, Quantity of Information, and Oil Market Turbulence," CESifo Working Paper Series 10995, CESifo.
    2. Marc Gronwald & Sania Wadud, 2024. "“My Name Is Bond. Green Bond.” Informational Efficiency of Climate Finance Markets," CESifo Working Paper Series 11029, CESifo.
    3. Zhong, Meirui & Zhang, Rui & Ren, Xiaohang, 2023. "The time-varying effects of liquidity and market efficiency of the European Union carbon market: Evidence from the TVP-SVAR-SV approach," Energy Economics, Elsevier, vol. 123(C).
    4. Alia Afzal & Philipp Sibbertsen, 2021. "Modeling fractional cointegration between high and low stock prices in Asian countries," Empirical Economics, Springer, vol. 60(2), pages 661-682, February.
    5. Adebola, Solarin Sakiru & Gil-Alana, Luis A. & Madigu, Godfrey, 2019. "Gold prices and the cryptocurrencies: Evidence of convergence and cointegration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1227-1236.
    6. Oloko, Tirimisiyu F. & Ogbonna, Ahamuefula E. & Adedeji, Abdulfatai A. & Lakhani, Noman, 2021. "Oil price shocks and inflation rate persistence: A Fractional Cointegration VAR approach," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 259-275.
    7. Mustanen, Dmitri & Maaitah, Ahmad & Mishra, Tapas & Parhi, Mamata, 2022. "The power of investors’ optimism and pessimism in oil market forecasting," Energy Economics, Elsevier, vol. 114(C).
    8. Gil-Alana, Luis A. & Carcel, Hector, 2020. "A fractional cointegration var analysis of exchange rate dynamics," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    9. Ataei, Masoud & Chen, Shengyuan & Yang, Zijiang & Peyghami, M. Reza, 2021. "Theory and applications of financial chaos index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    10. Javier Hualde & Morten {O}rregaard Nielsen, 2022. "Fractional integration and cointegration," Papers 2211.10235, arXiv.org.
    11. Dechert, Andreas, 2012. "Variance Ratio Testing for Fractional Cointegration in Presence of Trends and Trend Breaks," MPRA Paper 41044, University Library of Munich, Germany.
    12. Yaya, OlaOluwa S & Gil-Alana, Luis A., 2018. "High and Low Intraday Commodity Prices: A Fractional Integration and Cointegration Approach," MPRA Paper 90518, University Library of Munich, Germany.
    13. Gil-Alana, Luis A. & Yaya, OlaOluwa S. & Awe, Olushina O., 2017. "Time series analysis of co-movements in the prices of gold and oil: Fractional cointegration approach," Resources Policy, Elsevier, vol. 53(C), pages 117-124.
    14. Dechert, Andreas, 2014. "Fraktionale Kointegrationsbeziehungen zwischen Euribor-Zinssätzen," W.E.P. - Würzburg Economic Papers 93, University of Würzburg, Department of Economics.
    15. Luis Alberiko Gil-Alaña & Borja Balprad & Guglielmo Maria Caporale & Hector Carcel, 2015. "Exchange Rate Dynamics and Monetary Unions in Africa: A Fractional Integration and Cointegration Analysis," NCID Working Papers 11/2015, Navarra Center for International Development, University of Navarra.
    16. Søren Johansen & Morten Ørregaard Nielsen, 2012. "The role of initial values in nonstationary fractional time series models," Discussion Papers 12-18, University of Copenhagen. Department of Economics.
    17. Shimotsu, Katsumi, 2012. "Exact local Whittle estimation of fractionally cointegrated systems," Journal of Econometrics, Elsevier, vol. 169(2), pages 266-278.
    18. Stoupos, Nikolaos & Kiohos, Apostolos, 2022. "Euro area stock markets integration: Empirical evidence after the end of 2010 debt crisis," Finance Research Letters, Elsevier, vol. 46(PB).
    19. Yuliya Lovcha & Alejandro Perez-Laborda, 2017. "Structural shocks and dynamic elasticities in a long memory model of the US gasoline retail market," Empirical Economics, Springer, vol. 53(2), pages 405-422, September.
    20. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2020. "Modelling Loans to Non-Financial Corporations within the Eurozone: A Long-Memory Approach," CESifo Working Paper Series 8674, CESifo.

    More about this item

    Keywords

    world oil markets; efficient market hypothesis; market integration; fractional integration;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q31 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - 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:ces:ceswps:_11017. 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: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.html .

    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.