IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v82y2019icp253-263.html
   My bibliography  Save this article

Are the crude oil markets really becoming more efficient over time? Some new evidence*

* This paper is a replication of an original study

Author

Listed:
  • Kristoufek, Ladislav

Abstract

We replicate the study of Tabak & Cajueiro (2007): “Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility” published in Energy Economics 29, pp. 28-36. The results have been mostly confirmed. Specifically, we have confirmed that the crude oil markets efficiency could be rejected up till approximately 1994 and this holds for both studied crude oil commodities — Brent and WTI. Analyzing an extended dataset up till June 2017, we find that the markets remained efficient (at least with respect to long-range dependence) until the outbreak of the Global Financial Crisis in 2008. This is confirmed by all three applied methods — the rescaled range analysis used in the original study, and the detrended fluctuation analysis and the Geweke-Porter-Hudak estimator which were added to strengthen validity of the results. The markets returned back to efficiency around 2012 and remained there until 2015 when the Hurst exponent started another rally and stayed rather high until the end of the examined sample. Comparing the two markets, the Brent crude oil shows stronger signs of inefficiency during the inefficient periods compared to the WTI crude oil. This is in hand with the results reported in the original study. Apart from rerunning the analysis on an extended dataset and using two additional methods, we also provide a firmer validity check using the moving-block bootstrap procedure, which outperforms the original shuffling procedure in the provided forecasting study.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:eneeco:v:82:y:2019:i:c:p:253-263
    DOI: 10.1016/j.eneco.2018.03.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988318301002
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2018.03.019?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. John Geweke & Susan Porter‐Hudak, 1983. "The Estimation And Application Of Long Memory Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 221-238, July.
    2. 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.
    3. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    4. Kristoufek, Ladislav & Lunackova, Petra, 2015. "Rockets and feathers meet Joseph: Reinvestigating the oil–gasoline asymmetry on the international markets," Energy Economics, Elsevier, vol. 49(C), pages 1-8.
    5. Sensoy, Ahmet & Hacihasanoglu, Erk, 2014. "Time-varying long range dependence in energy futures markets," Energy Economics, Elsevier, vol. 46(C), pages 318-327.
    6. Kristoufek, Ladislav & Vosvrda, Miloslav, 2014. "Commodity futures and market efficiency," Energy Economics, Elsevier, vol. 42(C), pages 50-57.
    7. Barunik, Jozef & Kristoufek, Ladislav, 2010. "On Hurst exponent estimation under heavy-tailed distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3844-3855.
    8. 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.
    9. 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.
    10. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    11. Alvarez-Ramirez, Jose & Rodriguez, Eduardo & Carlos Echeverría, Juan, 2005. "Detrending fluctuation analysis based on moving average filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 199-219.
    12. 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..
    13. Li, Daye & Nishimura, Yusaku & Men, Ming, 2016. "Why the long-term auto-correlation has not been eliminated by arbitragers: Evidences from NYMEX," Energy Economics, Elsevier, vol. 59(C), pages 167-178.
    14. Kristoufek, Ladislav, 2012. "How are rescaled range analyses affected by different memory and distributional properties? A Monte Carlo study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(17), pages 4252-4260.
    15. Adrangi, Bahram & Chatrath, Arjun & Dhanda, Kanwalroop Kathy & Raffiee, Kambiz, 2001. "Chaos in oil prices? Evidence from futures markets," Energy Economics, Elsevier, vol. 23(4), pages 405-425, July.
    16. Lubnau, Thorben & Todorova, Neda, 2015. "Trading on mean-reversion in energy futures markets," Energy Economics, Elsevier, vol. 51(C), pages 312-319.
    17. Couillard, Michel & Davison, Matt, 2005. "A comment on measuring the Hurst exponent of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 404-418.
    18. Lahiri, S. N., 1993. "On the moving block bootstrap under long range dependence," Statistics & Probability Letters, Elsevier, vol. 18(5), pages 405-413, December.
    19. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    20. Matos, José A.O. & Gama, Sílvio M.A. & Ruskin, Heather J. & Sharkasi, Adel Al & Crane, Martin, 2008. "Time and scale Hurst exponent analysis for financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3910-3915.
    21. Weron, Rafał, 2002. "Estimating long-range dependence: finite sample properties and confidence intervals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(1), pages 285-299.
    22. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 15-27.
    23. 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.
    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. 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.
    26. Aloui, Chaker & Mabrouk, Samir, 2010. "Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models," Energy Policy, Elsevier, vol. 38(5), pages 2326-2339, May.
    27. 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.
    28. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    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. Kang, Sang Hoon & Kang, Sang-Mok & Yoon, Seong-Min, 2009. "Forecasting volatility of crude oil markets," Energy Economics, Elsevier, vol. 31(1), pages 119-125, January.
    31. Panas, Epaminondas & Ninni, Vassilia, 2000. "Are oil markets chaotic? A non-linear dynamic analysis," Energy Economics, Elsevier, vol. 22(5), pages 549-568, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sattarhoff, Cristina & Gronwald, Marc, 2022. "Measuring informational efficiency of the European carbon market — A quantitative evaluation of higher order dependence," International Review of Financial Analysis, Elsevier, vol. 84(C).
    2. Valenti, Daniele & Bastianin, Andrea & Manera, Matteo, 2023. "A weekly structural VAR model of the US crude oil market," Energy Economics, Elsevier, vol. 121(C).
    3. 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.
    4. Li, Wenhui & Zhu, Qi & Wen, Fenghua & Nor, Normaziah Mohd, 2022. "The evolution of day-of-the-week and the implications in crude oil market," Energy Economics, Elsevier, vol. 106(C).
    5. Marc Gronwald & Sania Wadud & Kingsley Dogah, 2024. "Oil Market Efficiency, Quantity of Information, and Oil Market Turbulence," CESifo Working Paper Series 10995, CESifo.
    6. Corzo Santamaría, Teresa & Martin-Bujack, Karin & Portela, Jose & Sáenz-Diez, Rocio, 2022. "Early market efficiency testing among hydrogen players," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 723-742.
    7. Matthieu Garcin, 2018. "Hurst exponents and delampertized fractional Brownian motions," Working Papers hal-01919754, HAL.
    8. Cao, K.H. & Qi, H.S. & Tsai, C.H. & Woo, C.K. & Zarnikau, J., 2021. "Energy trading efficiency in the US Midcontinent electricity markets," Applied Energy, Elsevier, vol. 302(C).
    9. Wang, Xiaoyang, 2022. "Efficient markets are more connected: An entropy-based analysis of the energy, industrial metal and financial markets," Energy Economics, Elsevier, vol. 111(C).
    10. A. Gómez-Águila & J. E. Trinidad-Segovia & M. A. Sánchez-Granero, 2022. "Improvement in Hurst exponent estimation and its application to financial markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    11. 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).
    12. 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).

    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. Sensoy, Ahmet & Hacihasanoglu, Erk, 2014. "Time-varying long range dependence in energy futures markets," Energy Economics, Elsevier, vol. 46(C), pages 318-327.
    2. 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.
    3. 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.
    4. Kristoufek, Ladislav & Vosvrda, Miloslav, 2014. "Commodity futures and market efficiency," Energy Economics, Elsevier, vol. 42(C), pages 50-57.
    5. Li, Daye & Nishimura, Yusaku & Men, Ming, 2016. "Why the long-term auto-correlation has not been eliminated by arbitragers: Evidences from NYMEX," Energy Economics, Elsevier, vol. 59(C), pages 167-178.
    6. Benjamin Rainer Auer, 2018. "Are standard asset pricing factors long-range dependent?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 42(1), pages 66-88, January.
    7. Li, Daye & Kou, Zhun & Sun, Qiankun, 2015. "The scale-dependent market trend: Empirical evidences using the lagged DFA method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 26-35.
    8. Tiwari, Aviral Kumar & Umar, Zaghum & Alqahtani, Faisal, 2021. "Existence of long memory in crude oil and petroleum products: Generalised Hurst exponent approach," Research in International Business and Finance, Elsevier, vol. 57(C).
    9. 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.
    10. 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.
    11. 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.
    12. Li, Daye & Nishimura, Yusaku & Men, Ming, 2016. "The long memory and the transaction cost in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 312-320.
    13. Corzo Santamaría, Teresa & Martin-Bujack, Karin & Portela, Jose & Sáenz-Diez, Rocio, 2022. "Early market efficiency testing among hydrogen players," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 723-742.
    14. Zunino, Luciano & Bariviera, Aurelio F. & Guercio, M. Belén & Martinez, Lisana B. & Rosso, Osvaldo A., 2016. "Monitoring the informational efficiency of European corporate bond markets with dynamical permutation min-entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 1-9.
    15. 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.
    16. George P. Papaioannou & Christos Dikaiakos & Akylas C. Stratigakos & Panos C. Papageorgiou & Konstantinos F. Krommydas, 2019. "Testing the Efficiency of Electricity Markets Using a New Composite Measure Based on Nonlinear TS Tools," Energies, MDPI, vol. 12(4), pages 1-30, February.
    17. 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).
    18. Wang, Yudong & Wu, Chongfeng & Wei, Yu, 2011. "Can GARCH-class models capture long memory in WTI crude oil markets?," Economic Modelling, Elsevier, vol. 28(3), pages 921-927, May.
    19. 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.
    20. A. Gómez-Águila & J. E. Trinidad-Segovia & M. A. Sánchez-Granero, 2022. "Improvement in Hurst exponent estimation and its application to financial markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.

    Replication

    This item is a replication of:

    More about this item

    Keywords

    Crude oil; Efficient market hypothesis; Forecasting; Long-range dependence; Replication;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

    Lists

    This item is featured on the following reading lists, Wikipedia, or ReplicationWiki pages:
    1. Are the crude oil markets really becoming more efficient over time? Some new evidence (Energy Economics 2019) in ReplicationWiki

    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:eneeco:v:82:y:2019:i:c:p:253-263. 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/eneco .

    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.