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The Inefficiency of Litecoin: A Dynamic Analysis

Author

Listed:
  • R. K. Jana

    (Indian Institute of Management Raipur)

  • Aviral Kumar Tiwari

    (Montpellier Business School)

  • Shawkat Hammoudeh

    (Montpellier Business School
    Drexel University)

Abstract

We analyze the informational efficiency of Litecoin using computationally efficient and robust estimators of long-range dependence for a sample period spanning over April 28, 2013 to November 27, 2017. We show evidence of market inefficiency. However, some short periods with negligible inefficiency are also observed. We also find evidence of multifractality of Litecoin returns.

Suggested Citation

  • R. K. Jana & Aviral Kumar Tiwari & Shawkat Hammoudeh, 2019. "The Inefficiency of Litecoin: A Dynamic Analysis," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 447-457, June.
    • Handle: RePEc:spr:jqecon:v:17:y:2019:i:2:d:10.1007_s40953-018-0149-0
    DOI: 10.1007/s40953-018-0149-0
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    References listed on IDEAS

    as
    1. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    2. Urquhart, Andrew, 2017. "Price clustering in Bitcoin," Economics Letters, Elsevier, vol. 159(C), pages 145-148.
    3. Bariviera, Aurelio F., 2017. "The inefficiency of Bitcoin revisited: A dynamic approach," Economics Letters, Elsevier, vol. 161(C), pages 1-4.
    4. 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.
    5. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    6. 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.
    7. Vadim Teverovsky & Murad Taqqu, 1997. "Testing for long‐range dependence in the presence of shifting means or a slowly declining trend, using a variance‐type estimator," Journal of Time Series Analysis, Wiley Blackwell, vol. 18(3), pages 279-304, May.
    8. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    9. Aurelio F. Bariviera & Luciano Zunino & Osvaldo A. Rosso, 2018. "An analysis of high-frequency cryptocurrencies prices dynamics using permutation-information-theory quantifiers," Papers 1808.01926, arXiv.org.
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    Cited by:

    1. R. K. Jana & Indranil Ghosh & Debojyoti Das, 2021. "A differential evolution-based regression framework for forecasting Bitcoin price," Annals of Operations Research, Springer, vol. 306(1), pages 295-320, November.

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