IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v46y2014icp318-327.html
   My bibliography  Save this article

Time-varying long range dependence in energy futures markets

Author

Listed:
  • Sensoy, Ahmet
  • Hacihasanoglu, Erk

Abstract

This study aims to investigate the presence of long-range dependence in energy futures markets. Using a daily dataset covering from 1990 to 2013 (which includes crucial events for energy markets such as invasion of Iraq and global financial crisis of 2008), we estimate time-varying generalized Hurst exponents of several energy futures contracts with different times to maturity using a rolling window approach. Results reveal that efficiency of energy futures markets is clearly time-varying and changes drastically over the sample period. For futures contracts with 1–4months to maturities, crude oil and gasoline are found to be more efficient compared to others. On the other hand, for contracts with 5–9months to maturities, crude oil and natural gas futures are more efficient. For almost every different month to maturity, heating oil and gas oil futures are found to be the least efficient markets. Moreover in general, the efficiency of energy futures markets is found to be decreasing dramatically when time to maturity is increasing. Several implications are discussed.

Suggested Citation

  • Sensoy, Ahmet & Hacihasanoglu, Erk, 2014. "Time-varying long range dependence in energy futures markets," Energy Economics, Elsevier, vol. 46(C), pages 318-327.
  • Handle: RePEc:eee:eneeco:v:46:y:2014:i:c:p:318-327
    DOI: 10.1016/j.eneco.2014.09.023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988314002497
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2014.09.023?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. Cajueiro, Daniel O. & Tabak, Benjamin M., 2008. "Testing for long-range dependence in world stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 37(3), pages 918-927.
    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. Apostolos Serletis, 2007. "Unit Root Behavior in Energy Futures Prices," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 1, pages 7-14, World Scientific Publishing Co. Pte. Ltd..
    5. 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.
    6. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    7. Laurent Calvet & Adlai Fisher, 2002. "Multifractality In Asset Returns: Theory And Evidence," The Review of Economics and Statistics, MIT Press, vol. 84(3), pages 381-406, August.
    8. Mandelbrot, Benoit B, 1971. "When Can Price Be Arbitraged Efficiently? A Limit to the Validity of the Random Walk and Martingale Models," The Review of Economics and Statistics, MIT Press, vol. 53(3), pages 225-236, August.
    9. Hooi Hooi Lean & Michael McAleer & Wing-Keung Wong, 2010. "Market Efficiency of Oil Spot and Futures: A Stochastic Dominance Approach," CIRJE F-Series CIRJE-F-705, CIRJE, Faculty of Economics, University of Tokyo.
    10. Jamdee, Sutthisit & Los, Cornelis A., 2007. "Long memory options: LM evidence and simulations," Research in International Business and Finance, Elsevier, vol. 21(2), pages 260-280, June.
    11. Fama, Eugene F & French, Kenneth R, 1988. "Permanent and Temporary Components of Stock Prices," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 246-273, April.
    12. 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.
    13. John Elder & Apostolos Serletis, 2008. "Long memory in energy futures prices," Review of Financial Economics, John Wiley & Sons, vol. 17(2), pages 146-155.
    14. 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.
    15. 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.
    16. Morales, Raffaello & Di Matteo, T. & Aste, Tomaso, 2013. "Non-stationary multifractality in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6470-6483.
    17. 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.
    18. François Lescaroux & Valérie Mignon, 2008. "On the Influence of Oil Prices on Economic Activity and Other Macroeconomic and Financial Variables," Working Papers 2008-05, CEPII research center.
    19. Lean, Hooi Hooi & McAleer, Michael & Wong, Wing-Keung, 2010. "Market efficiency of oil spot and futures: A mean-variance and stochastic dominance approach," Energy Economics, Elsevier, vol. 32(5), pages 979-986, September.
    20. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    21. El Hedi Arouri, Mohamed & Huong Dinh, Thanh & Khuong Nguyen, Duc, 2010. "Time-varying predictability in crude-oil markets: the case of GCC countries," Energy Policy, Elsevier, vol. 38(8), pages 4371-4380, August.
    22. Matteo, T. Di & Aste, T. & Dacorogna, Michel M., 2005. "Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 827-851, April.
    23. 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.
    24. Dimitris Politis & Halbert White, 2004. "Automatic Block-Length Selection for the Dependent Bootstrap," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 53-70.
    25. Barunik, Jozef & Aste, Tomaso & Di Matteo, T. & Liu, Ruipeng, 2012. "Understanding the source of multifractality in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(17), pages 4234-4251.
    26. Morales, Raffaello & Di Matteo, T. & Gramatica, Ruggero & Aste, Tomaso, 2012. "Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3180-3189.
    27. 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.
    28. Wang, Yudong & Wu, Chongfeng, 2012. "Long memory in energy futures markets: Further evidence," Resources Policy, Elsevier, vol. 37(3), pages 261-272.
    29. Sensoy, A., 2013. "Generalized Hurst exponent approach to efficiency in MENA markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5019-5026.
    30. Cajueiro, Daniel O. & Tabak, Benjamin M., 2010. "Fluctuation dynamics in US interest rates and the role of monetary policy," Finance Research Letters, Elsevier, vol. 7(3), pages 163-169, September.
    31. 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.
    32. 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.
    33. Pilar Grau-Carles, 2005. "Tests of Long Memory: A Bootstrap Approach," Computational Economics, Springer;Society for Computational Economics, vol. 25(1), pages 103-113, February.
    34. Sensoy, A., 2013. "Effects of monetary policy on the long memory in interest rates: Evidence from an emerging market," Chaos, Solitons & Fractals, Elsevier, vol. 57(C), pages 85-88.
    35. 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.
    36. 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.
    37. Raffaello Morales & T. Di Matteo & Tomaso Aste, 2012. "Non stationary multifractality in stock returns," Papers 1212.3195, arXiv.org, revised May 2013.
    38. 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.
    39. Fernandez, Viviana, 2010. "Commodity futures and market efficiency: A fractional integrated approach," Resources Policy, Elsevier, vol. 35(4), pages 276-282, December.
    40. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    41. Cajueiro, Daniel O. & Tabak, Benjamin M., 2005. "Testing for time-varying long-range dependence in volatility for emerging markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(3), pages 577-588.
    42. Cajueiro, Daniel O. & Tabak, Benjamin M., 2009. "Testing for long-range dependence in the Brazilian term structure of interest rates," Chaos, Solitons & Fractals, Elsevier, vol. 40(4), pages 1559-1573.
    43. 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.
    44. 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.
    45. 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.
    46. François Lescaroux & Valérie Mignon, 2008. "On the influence of oil prices on economic activity and other macroeconomic and financial variables ," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 32(4), pages 343-380, December.
    47. Narayan, Paresh Kumar & Narayan, Seema & Popp, Stephan, 2011. "Investigating price clustering in the oil futures market," Applied Energy, Elsevier, vol. 88(1), pages 397-402, January.
    48. 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.
    49. repec:clg:wpaper:2007-02 is not listed on IDEAS
    50. 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.
    51. 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.
    52. Lim, Kian-Ping & Brooks, Robert D. & Kim, Jae H., 2008. "Financial crisis and stock market efficiency: Empirical evidence from Asian countries," International Review of Financial Analysis, Elsevier, vol. 17(3), pages 571-591, June.
    53. Lee, Chien-Chiang & Lee, Jun-De, 2009. "Energy prices, multiple structural breaks, and efficient market hypothesis," Applied Energy, Elsevier, vol. 86(4), pages 466-479, April.
    54. 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.
    55. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    56. Lim, Kian-Ping, 2007. "Ranking market efficiency for stock markets: A nonlinear perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 445-454.
    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. 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).
    2. 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).
    3. Jiang, Yonghong & Nie, He & Ruan, Weihua, 2018. "Time-varying long-term memory in Bitcoin market," Finance Research Letters, Elsevier, vol. 25(C), pages 280-284.
    4. Al-Yahyaee, Khamis Hamed & Mensi, Walid & Yoon, Seong-Min, 2018. "Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets," Finance Research Letters, Elsevier, vol. 27(C), pages 228-234.
    5. Guo, Yaoqi & Yao, Shanshan & Cheng, Hui & Zhu, Wensong, 2020. "China's copper futures market efficiency analysis: Based on nonlinear Granger causality and multifractal methods," Resources Policy, Elsevier, vol. 68(C).
    6. Agnello, Luca & Castro, Vítor & Hammoudeh, Shawkat & Sousa, Ricardo M., 2020. "Global factors, uncertainty, weather conditions and energy prices: On the drivers of the duration of commodity price cycle phases," Energy Economics, Elsevier, vol. 90(C).
    7. Indranil SenGupta & William Nganje & Erik Hanson, 2021. "Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning," Annals of Data Science, Springer, vol. 8(1), pages 39-55, March.
    8. Ding, Ashley, 2021. "A state-preference volatility index for the natural gas market," Energy Economics, Elsevier, vol. 104(C).
    9. 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.
    10. Vogl, Markus, 2023. "Hurst exponent dynamics of S&P 500 returns: Implications for market efficiency, long memory, multifractality and financial crises predictability by application of a nonlinear dynamics analysis framewo," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    11. Humayra Shoshi & Indranil SenGupta, 2020. "Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model," Papers 2004.14862, arXiv.org, revised Feb 2021.
    12. Sensoy, Ahmet & Tabak, Benjamin M., 2015. "Time-varying long term memory in the European Union stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 147-158.
    13. 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.
    14. 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.
    15. 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.
    16. Al-Yahyaee, Khamis Hamed & Rehman, Mobeen Ur & Mensi, Walid & Al-Jarrah, Idries Mohammad Wanas, 2019. "Can uncertainty indices predict Bitcoin prices? A revisited analysis using partial and multivariate wavelet approaches," The North American Journal of Economics and Finance, Elsevier, vol. 49(C), pages 47-56.
    17. 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.
    18. Nils Bundi & Marc Wildi, 2019. "Bitcoin and market-(in)efficiency: a systematic time series approach," Digital Finance, Springer, vol. 1(1), pages 47-65, November.
    19. Herrera, Gabriel Paes & Constantino, Michel & Tabak, Benjamin Miranda & Pistori, Hemerson & Su, Jen-Je & Naranpanawa, Athula, 2019. "Long-term forecast of energy commodities price using machine learning," Energy, Elsevier, vol. 179(C), pages 214-221.
    20. Charfeddine, Lanouar & Khediri, Karim Ben & Mrabet, Zouhair, 2019. "The forward premium anomaly in the energy futures markets: A time-varying approach," Research in International Business and Finance, Elsevier, vol. 47(C), pages 600-615.
    21. Corbet, Shaen & Katsiampa, Paraskevi, 2020. "Asymmetric mean reversion of Bitcoin price returns," International Review of Financial Analysis, Elsevier, vol. 71(C).
    22. A. Sensoy & Benjamin M. Tabak, 2013. "How much random does European Union walk? A time-varying long memory analysis," Working Papers Series 342, Central Bank of Brazil, Research Department.

    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 & Tabak, Benjamin M., 2016. "Dynamic efficiency of stock markets and exchange rates," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 353-371.
    2. Sensoy, Ahmet & Tabak, Benjamin M., 2015. "Time-varying long term memory in the European Union stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 147-158.
    3. 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).
    4. A. Sensoy & Benjamin M. Tabak, 2013. "How much random does European Union walk? A time-varying long memory analysis," Working Papers Series 342, Central Bank of Brazil, Research Department.
    5. 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.
    6. Kristoufek, Ladislav & Vosvrda, Miloslav, 2014. "Commodity futures and market efficiency," Energy Economics, Elsevier, vol. 42(C), pages 50-57.
    7. 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).
    8. 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).
    9. 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.
    10. Wang, Yudong & Wu, Chongfeng, 2012. "Long memory in energy futures markets: Further evidence," Resources Policy, Elsevier, vol. 37(3), pages 261-272.
    11. 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.
    12. Jiang, Yonghong & Nie, He & Ruan, Weihua, 2018. "Time-varying long-term memory in Bitcoin market," Finance Research Letters, Elsevier, vol. 25(C), pages 280-284.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    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. 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.
    20. Ladislav Kristoufek & Miloslav Vosvrda, 2014. "Measuring capital market efficiency: long-term memory, fractal dimension and approximate entropy," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(7), pages 1-9, July.

    More about this item

    Keywords

    Energy markets; Futures markets; Market efficiency; Generalized Hurst exponent;
    All these keywords.

    JEL classification:

    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • 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

    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:46:y:2014:i:c:p:318-327. 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.