IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v405y2014icp235-244.html
   My bibliography  Save this article

Testing the weak-form efficiency of the WTI crude oil futures market

Author

Listed:
  • Jiang, Zhi-Qiang
  • Xie, Wen-Jie
  • Zhou, Wei-Xing

Abstract

The weak-form efficiency of energy futures markets has long been studied and empirical evidence suggests controversial conclusions. In this work, nonparametric methods are adopted to estimate the Hurst indexes of the WTI crude oil futures prices (1983–2012) and a strict statistical test in the spirit of bootstrapping is put forward to verify the weak-form market efficiency hypothesis. The results show that the crude oil futures market is efficient when the whole period is considered. When the whole series is divided into three sub-series separated by the outbreaks of the Gulf War and the Iraq War, it is found that the Gulf War reduced the efficiency of the market. If the sample is split into two sub-series based on the signing date of the North American Free Trade Agreement, the market is found to be inefficient in the sub-periods during which the Gulf War broke out. The same analysis on short-time series in moving windows shows that the market is inefficient only when some turbulent events occur, such as the oil price crash in 1985, the Gulf war, and the oil price crash in 2008.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:phsmap:v:405:y:2014:i:c:p:235-244
    DOI: 10.1016/j.physa.2014.02.042
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114001514
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    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. Apostolos Serletis & Ricardo Rangel-Ruiz, 2007. "Testing for Common Features in North American Energy Markets," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 14, pages 172-187, World Scientific Publishing Co. Pte. Ltd..
    2. 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.
    3. 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.
    4. Serletis, Apostolos & Rosenberg, Aryeh Adam, 2007. "The Hurst exponent in energy futures prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 325-332.
    5. Shambora, William E. & Rossiter, Rosemary, 2007. "Are there exploitable inefficiencies in the futures market for oil?," Energy Economics, Elsevier, vol. 29(1), pages 18-27, January.
    6. He, Ling-Yun & Qian, Wen-Bin, 2012. "A Monte Carlo simulation to the performance of the R/S and V/S methods—Statistical revisit and real world application," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(14), pages 3770-3782.
    7. Green, Steven L & Mork, Knut Anton, 1991. "Toward Efficiency in the Crude-Oil Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(1), pages 45-66, Jan.-Marc.
    8. D. Sornette, 2003. "Critical Market Crashes," Papers cond-mat/0301543, arXiv.org.
    9. Gu, Rongbao & Chen, Hongtao & Wang, Yudong, 2010. "Multifractal analysis on international crude oil markets based on the multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2805-2815.
    10. 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.
    11. Cajueiro, Daniel O. & Tabak, Benjamin M., 2005. "The rescaled variance statistic and the determination of the Hurst exponent," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 70(3), pages 172-179.
    12. Elder, John & Serletis, Apostolos, 2008. "Long memory in energy futures prices," Review of Financial Economics, Elsevier, vol. 17(2), pages 146-155.
    13. 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.
    14. 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.
    15. Carbone, A. & Castelli, G. & Stanley, H.E., 2004. "Time-dependent Hurst exponent in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 267-271.
    16. Amir Bashan & Ronny Bartsch & Jan W. Kantelhardt & Shlomo Havlin, 2008. "Comparison of detrending methods for fluctuation analysis," Papers 0804.4081, arXiv.org.
    17. Arianos, Sergio & Carbone, Anna, 2007. "Detrending moving average algorithm: A closed-form approximation of the scaling law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 9-15.
    18. Cajueiro, Daniel O & Tabak, Benjamin M, 2004. "The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 521-537.
    19. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    20. Wang, Tao & Yang, Jian, 2010. "Nonlinearity and intraday efficiency tests on energy futures markets," Energy Economics, Elsevier, vol. 32(2), pages 496-503, March.
    21. Giraitis, Liudas & Kokoszka, Piotr & Leipus, Remigijus & Teyssiere, Gilles, 2003. "Rescaled variance and related tests for long memory in volatility and levels," Journal of Econometrics, Elsevier, vol. 112(2), pages 265-294, February.
    22. 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..
    23. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    24. Carbone, Anna & Stanley, H.Eugene, 2004. "Directed self-organized critical patterns emerging from fractional Brownian paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 340(4), pages 544-551.
    25. repec:clg:wpaper:2007-02 is not listed on IDEAS
    26. Castro e Silva, A. & Moreira, J.G., 1997. "Roughness exponents to calculate multi-affine fractal exponents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 235(3), pages 327-333.
    27. Stanislaw Drozdz & Jaroslaw Kwapien & Pawel Oswiecimka, 2008. "Criticality Characteristics of Current Oil Price Dynamics," Papers 0808.3360, arXiv.org.
    28. Shang, Pengjian & Lin, Aijing & Liu, Liang, 2009. "Chaotic SVD method for minimizing the effect of exponential trends in detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(5), pages 720-726.
    29. 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.
    30. 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.
    31. 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.
    32. Gao-Feng Gu & Wei-Xing Zhou, 2010. "Detrending moving average algorithm for multifractals," Papers 1005.0877, arXiv.org, revised Jun 2010.
    33. Matsushita, Raul & Gleria, Iram & Figueiredo, Annibal & Da Silva, Sergio, 2007. "Are Pound and Euro the Same Currency? - Updated," MPRA Paper 1981, University Library of Munich, Germany.
    34. Serinaldi, Francesco, 2010. "Use and misuse of some Hurst parameter estimators applied to stationary and non-stationary financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2770-2781.
    35. Zhi-Qiang Jiang & Wei-Xing Zhou, 2011. "Multifractal detrending moving average cross-correlation analysis," Papers 1103.2577, arXiv.org, revised Mar 2011.
    36. Sornette, Didier & Woodard, Ryan & Zhou, Wei-Xing, 2009. "The 2006–2008 oil bubble: Evidence of speculation, and prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1571-1576.
    37. Wang, Yudong & Wei, Yu & Wu, Chongfeng, 2011. "Detrended fluctuation analysis on spot and futures markets of West Texas Intermediate crude oil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(5), pages 864-875.
    38. Ying-Hui Shao & Gao Feng Gu & Zhi-Qiang Jiang & Wei-Xing Zhou & Didier Sornette, 2012. "Comparing the performance of FA, DFA and DMA using different synthetic long-range correlated time series," Papers 1208.4158, arXiv.org.
    39. Bashan, Amir & Bartsch, Ronny & Kantelhardt, Jan W. & Havlin, Shlomo, 2008. "Comparison of detrending methods for fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5080-5090.
    40. Wei-Xing Zhou, 2008. "Multifractal detrended cross-correlation analysis for two nonstationary signals," Papers 0803.2773, arXiv.org.
    41. Didier SORNETTE, 2009. "Dragon-Kings, Black Swans and the Prediction of Crises," Swiss Finance Institute Research Paper Series 09-36, Swiss Finance Institute.
    42. 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.
    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. Kristoufek, Ladislav, 2014. "Leverage effect in energy futures," Energy Economics, Elsevier, vol. 45(C), pages 1-9.
    2. Zhuang, Xiaoyang & Wei, Yu & Ma, Feng, 2015. "Multifractality, efficiency analysis of Chinese stock market and its cross-correlation with WTI crude oil price," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 101-113.
    3. repec:eee:phsmap:v:509:y:2018:i:c:p:578-587 is not listed on IDEAS
    4. Josselin Garnier & Knut Solna, 2018. "Emergence of Turbulent Epochs in Oil Prices," Papers 1808.09382, arXiv.org, revised Apr 2019.
    5. 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.
    6. Wang, Lijun & An, Haizhong & Liu, Xiaojia & Huang, Xuan, 2016. "Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach," Applied Energy, Elsevier, vol. 162(C), pages 1608-1618.
    7. Josselin Garnier & Knut Solna, 2018. "Chaos and Order in the Bitcoin Market," Papers 1809.08403, arXiv.org, revised Apr 2019.
    8. Lahmiri, Salim, 2017. "A study on chaos in crude oil markets before and after 2008 international financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 389-395.
    9. repec:eee:phsmap:v:509:y:2018:i:c:p:657-672 is not listed on IDEAS
    10. Hooi Hooi Lean & Russell Smyth, 2015. "Testing for weak-form efficiency of crude palm oil spot and future markets: new evidence from a GARCH unit root test with multiple structural breaks," Applied Economics, Taylor & Francis Journals, vol. 47(16), pages 1710-1721, April.
    11. Jin, Xiaoye, 2016. "The impact of 2008 financial crisis on the efficiency and contagion of Asian stock markets: A Hurst exponent approach," Finance Research Letters, Elsevier, vol. 17(C), pages 167-175.
    12. García-Carranco, Sergio M. & Bory-Reyes, Juan & Balankin, Alexander S., 2016. "The crude oil price bubbling and universal scaling dynamics of price volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 60-68.
    13. Huai-Long Shi & Zhi-Qiang Jiang & Wei-Xing Zhou, 2016. "Time-varying return predictability in the Chinese stock market," Papers 1611.04090, arXiv.org.
    14. Jian Zhou & Gao-Feng Gu & Zhi-Qiang Jiang & Xiong Xiong & Wei Chen & Wei Zhang & Wei-Xing Zhou, 2017. "Computational Experiments Successfully Predict the Emergence of Autocorrelations in Ultra-High-Frequency Stock Returns," Computational Economics, Springer;Society for Computational Economics, vol. 50(4), pages 579-594, December.
    15. Liu, Xiaojia & An, Haizhong & Wang, Lijun & Jia, Xiaoliang, 2017. "An integrated approach to optimize moving average rules in the EUA futures market based on particle swarm optimization and genetic algorithms," Applied Energy, Elsevier, vol. 185(P2), pages 1778-1787.
    16. Ma, Pengcheng & Li, Daye & Li, Shuo, 2016. "Efficiency and cross-correlation in equity market during global financial crisis: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 163-176.
    17. Gao-Feng Gu & Xiong Xiong & Yong-Jie Zhang & Wei Chen & Wei Zhang & Wei-Xing Zhou, 2014. "Stylized facts of price gaps in limit order books: Evidence from Chinese stocks," Papers 1405.1247, arXiv.org.
    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. repec:eee:phsmap:v:523:y:2019:i:c:p:734-746 is not listed on IDEAS
    20. repec:eee:phsmap:v:510:y:2018:i:c:p:658-670 is not listed on IDEAS

    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:phsmap:v:405:y:2014:i:c:p:235-244. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.