IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v82y2023ics0301420723001381.html
   My bibliography  Save this article

Measuring price efficiency in petroleum markets: New insights using various long-range dependence techniques

Author

Listed:
  • Tiwari, Aviral Kumar
  • Abakah, Emmanuel Joel Aikins
  • Mefteh-Wali, Salma
  • Owusu, Patrick

Abstract

This paper empirically measures pricing efficiency in the petroleum markets using a battery of long-range dependence techniques. We analyse the volatility (absolute returns) of fourteen petroleum products using daily prices from January 1990 to April 2020. We use bootstrapping techniques to estimate the confidence intervals of the long-range dependence parameters. Overall results obtained using CMA-MAF, CMA-MSF, GPH and periodogram techniques suggest that there is no evidence of long-range dependence in absolute returns of petroleum markets prices indices at least at the 10% level of significance. For robustness purposes, findings from the rolling windows estimates reveal that the results are not affected by the normality assumption under different rolling window sizes. Furthermore, we find that Gas oil, Biofuel and Heating oil are the most correctly priced while Crude Oil Dubai emerging is the least efficient. Comparing the efficiency index of the all six crude oil returns in our sample, WTI oil was seen to be less efficient than Brent crude oil. In all, our work uncovers crucial implications for investors and policymakers.

Suggested Citation

  • Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Mefteh-Wali, Salma & Owusu, Patrick, 2023. "Measuring price efficiency in petroleum markets: New insights using various long-range dependence techniques," Resources Policy, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:jrpoli:v:82:y:2023:i:c:s0301420723001381
    DOI: 10.1016/j.resourpol.2023.103430
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420723001381
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2023.103430?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Los, Cornelis A. & Yu, Bing, 2008. "Persistence characteristics of the Chinese stock markets," International Review of Financial Analysis, Elsevier, vol. 17(1), pages 64-82.
    2. Arouri, Mohamed El Hedi & Hammoudeh, Shawkat & Lahiani, Amine & Nguyen, Duc Khuong, 2013. "On the short- and long-run efficiency of energy and precious metal markets," Energy Economics, Elsevier, vol. 40(C), pages 832-844.
    3. Narayan, Paresh Kumar & Narayan, Seema & Zheng, Xinwei, 2010. "Gold and oil futures markets: Are markets efficient?," Applied Energy, Elsevier, vol. 87(10), pages 3299-3303, October.
    4. Levine, Ross & Zervos, Sara, 1998. "Stock Markets, Banks, and Economic Growth," American Economic Review, American Economic Association, vol. 88(3), pages 537-558, June.
    5. Emmanuel Joel Aikins Abakah & Paul Alagidede & Lord Mensah & Kwaku Ohene-Asare, 2018. "Non-linear approach to Random Walk Test in selected African countries," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 14(3), pages 362-376, April.
    6. Alvarez-Ramirez, Jose & Cisneros, Myriam & Ibarra-Valdez, Carlos & Soriano, Angel, 2002. "Multifractal Hurst analysis of crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 313(3), pages 651-670.
    7. Alvarez-Ramirez, Jose & Alvarez, Jesus & Solis, Ricardo, 2010. "Crude oil market efficiency and modeling: Insights from the multiscaling autocorrelation pattern," Energy Economics, Elsevier, vol. 32(5), pages 993-1000, September.
    8. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    9. Apostolos Serletis & Ioannis Andreadis, 2007. "Random Fractal Structures in North American Energy Markets," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 18, pages 245-255, World Scientific Publishing Co. Pte. Ltd..
    10. Kuruppuarachchi, Duminda & Lin, Hai & Premachandra, I.M., 2019. "Testing commodity futures market efficiency under time-varying risk premiums and heteroscedastic prices," Economic Modelling, Elsevier, vol. 77(C), pages 92-112.
    11. Kristoufek, Ladislav & Vosvrda, Miloslav, 2013. "Measuring capital market efficiency: Global and local correlations structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 184-193.
    12. Emmanuel Joel Aikins Abakah & Paul Alagidede & Lord Mensah & Kwaku Ohene-Asare, 2018. "Non-linear approach to Random Walk Test in selected African countries," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 14(3), pages 362-376, April.
    13. Wang, Yudong & Wu, Chongfeng, 2012. "Long memory in energy futures markets: Further evidence," Resources Policy, Elsevier, vol. 37(3), pages 261-272.
    14. 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.
    15. Satish Kumar & Rajesh Pathak & Aviral Kumar Tiwari & Seong-Min Yoon, 2017. "Are exchange rates interdependent? Evidence using wavelet analysis," Applied Economics, Taylor & Francis Journals, vol. 49(33), pages 3231-3245, July.
    16. Connor Jeff & Rossiter Rosemary, 2005. "Wavelet Transforms and Commodity Prices," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(1), pages 1-22, March.
    17. Maslyuk, Svetlana & Smyth, Russell, 2008. "Unit root properties of crude oil spot and futures prices," Energy Policy, Elsevier, vol. 36(7), pages 2591-2600, July.
    18. Zhang, Bing & Li, Xiao-Ming & He, Fei, 2014. "Testing the evolution of crude oil market efficiency: Data have the conn," Energy Policy, Elsevier, vol. 68(C), pages 39-52.
    19. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo, 2008. "Short-term predictability of crude oil markets: A detrended fluctuation analysis approach," Energy Economics, Elsevier, vol. 30(5), pages 2645-2656, September.
    20. Lorne N. Switzer & Mario El‐Khoury, 2007. "Extreme volatility, speculative efficiency, and the hedging effectiveness of the oil futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(1), pages 61-84, January.
    21. Fernandez, Viviana, 2010. "Commodity futures and market efficiency: A fractional integrated approach," Resources Policy, Elsevier, vol. 35(4), pages 276-282, December.
    22. Shahzad, Umer & Jena, Sangram Keshari & Tiwari, Aviral Kumar & Doğan, Buhari & Magazzino, Cosimo, 2022. "Time-frequency analysis between Bloomberg Commodity Index (BCOM) and WTI crude oil prices," Resources Policy, Elsevier, vol. 78(C).
    23. Tiwari, Aviral Kumar & Kumar, Satish & Pathak, Rajesh & Roubaud, David, 2019. "Testing the oil price efficiency using various measures of long-range dependence," Energy Economics, Elsevier, vol. 84(C).
    24. Juncal Cunado & Luis A. Gil‐Alana & Fernando Perez de Gracia, 2010. "Persistence in some energy futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(5), pages 490-507, May.
    25. Yudong Wang & Chongfeng Wu, 2013. "Efficiency of Crude Oil Futures Markets: New Evidence from Multifractal Detrending Moving Average Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 42(4), pages 393-414, December.
    26. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    27. repec:clg:wpaper:2007-02 is not listed on IDEAS
    28. Wang, Yudong & Liu, Li, 2010. "Is WTI crude oil market becoming weakly efficient over time?: New evidence from multiscale analysis based on detrended fluctuation analysis," Energy Economics, Elsevier, vol. 32(5), pages 987-992, September.
    29. Tabak, Benjamin M. & Cajueiro, Daniel O., 2007. "Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility," Energy Economics, Elsevier, vol. 29(1), pages 28-36, January.
    30. Caraiani, Petre, 2012. "Money and output: New evidence based on wavelet coherence," Economics Letters, Elsevier, vol. 116(3), pages 547-550.
    31. Zhang, Yue-Jun, 2013. "Speculative trading and WTI crude oil futures price movement: An empirical analysis," Applied Energy, Elsevier, vol. 107(C), pages 394-402.
    32. Kian‐Ping Lim & Robert Brooks, 2011. "The Evolution Of Stock Market Efficiency Over Time: A Survey Of The Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 69-108, February.
    33. Charles Adjasi & Nicholas Biekpe, 2006. "Stock Market Development and Economic Growth: The Case of Selected African Countries," African Development Review, African Development Bank, vol. 18(1), pages 144-161.
    34. Ghazani, Majid Mirzaee & Ebrahimi, Seyed Babak, 2019. "Testing the adaptive market hypothesis as an evolutionary perspective on market efficiency: Evidence from the crude oil prices," Finance Research Letters, Elsevier, vol. 30(C), pages 60-68.
    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. Tiwari, Aviral Kumar & Kumar, Satish & Pathak, Rajesh & Roubaud, David, 2019. "Testing the oil price efficiency using various measures of long-range dependence," Energy Economics, Elsevier, vol. 84(C).
    2. Sensoy, Ahmet & Hacihasanoglu, Erk, 2014. "Time-varying long range dependence in energy futures markets," Energy Economics, Elsevier, vol. 46(C), pages 318-327.
    3. Ortiz-Cruz, Alejandro & Rodriguez, Eduardo & Ibarra-Valdez, Carlos & Alvarez-Ramirez, Jose, 2012. "Efficiency of crude oil markets: Evidences from informational entropy analysis," Energy Policy, Elsevier, vol. 41(C), pages 365-373.
    4. Yudong Wang & Chongfeng Wu, 2013. "Efficiency of Crude Oil Futures Markets: New Evidence from Multifractal Detrending Moving Average Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 42(4), pages 393-414, December.
    5. Zhang, Bing, 2013. "Are the crude oil markets becoming more efficient over time? New evidence from a generalized spectral test," Energy Economics, Elsevier, vol. 40(C), pages 875-881.
    6. 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.
    7. Wang, Yudong & Wu, Chongfeng, 2012. "What can we learn from the history of gasoline crack spreads?: Long memory, structural breaks and modeling implications," Economic Modelling, Elsevier, vol. 29(2), pages 349-360.
    8. Yuan, Ying & Zhuang, Xin-tian & Jin, Xiu & Huang, Wei-qiang, 2014. "Stable distribution and long-range correlation of Brent crude oil market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 173-179.
    9. Jiang, Zhi-Qiang & Xie, Wen-Jie & Zhou, Wei-Xing, 2014. "Testing the weak-form efficiency of the WTI crude oil futures market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 235-244.
    10. Kristoufek, Ladislav & Vosvrda, Miloslav, 2014. "Commodity futures and market efficiency," Energy Economics, Elsevier, vol. 42(C), pages 50-57.
    11. Alvarez-Ramirez, Jose & Alvarez, Jesus & Solis, Ricardo, 2010. "Crude oil market efficiency and modeling: Insights from the multiscaling autocorrelation pattern," Energy Economics, Elsevier, vol. 32(5), pages 993-1000, September.
    12. Wang, Yudong & Wu, Chongfeng, 2012. "Long memory in energy futures markets: Further evidence," Resources Policy, Elsevier, vol. 37(3), pages 261-272.
    13. Arouri, Mohamed El Hedi & Hammoudeh, Shawkat & Lahiani, Amine & Nguyen, Duc Khuong, 2013. "On the short- and long-run efficiency of energy and precious metal markets," Energy Economics, Elsevier, vol. 40(C), pages 832-844.
    14. Ibarra-Valdez, C. & Alvarez, J. & Alvarez-Ramirez, J., 2016. "Randomness confidence bands of fractal scaling exponents for financial price returns," Chaos, Solitons & Fractals, Elsevier, vol. 83(C), pages 119-124.
    15. Martina, Esteban & Rodriguez, Eduardo & Escarela-Perez, Rafael & Alvarez-Ramirez, Jose, 2011. "Multiscale entropy analysis of crude oil price dynamics," Energy Economics, Elsevier, vol. 33(5), pages 936-947, September.
    16. Alvarez-Ramirez, J. & Alvarez, J. & Rodríguez, E., 2015. "Asymmetric long-term autocorrelations in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 330-341.
    17. Charles, Amélie & Darné, Olivier, 2009. "The efficiency of the crude oil markets: Evidence from variance ratio tests," Energy Policy, Elsevier, vol. 37(11), pages 4267-4272, November.
    18. Jebabli, Ikram & Roubaud, David, 2018. "Time-varying efficiency in food and energy markets: Evidence and implications," Economic Modelling, Elsevier, vol. 70(C), pages 97-114.
    19. Gu, Rongbao & Zhang, Bing, 2016. "Is efficiency of crude oil market affected by multifractality? Evidence from the WTI crude oil market," Energy Economics, Elsevier, vol. 53(C), pages 151-158.
    20. Espinosa-Paredes, G. & Rodriguez, E. & Alvarez-Ramirez, J., 2022. "A singular value decomposition entropy approach to assess the impact of Covid-19 on the informational efficiency of the WTI crude oil market," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

    More about this item

    Keywords

    Long-range persistence; Petroleum price efficiency; Petroleum markets;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

    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:jrpoli:v:82:y:2023:i:c:s0301420723001381. 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/inca/30467 .

    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.