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Efficiency or speculation? A dynamic analysis of the Bitcoin market

Author

Listed:
  • Refk Selmi

    (IRMAPE-ESC Pau Business School; CATT University of Pau)

  • Aviral Kumar Tiwari

    (Montpellier Business School; Rajagiri Business School)

  • Shawkat Hammoudeh

    (Lebow College of Business, Drexel University; Montpellier Business School)

Abstract

Bitcoin has recently been labelled as a “dangerous speculative bubble†by Nobel Prize-winning economists Joseph Stiglitz and Robert Shiller, as the Bitcoin's market value now exceeds the GDP of over 130 countries. In this study, the multifractality and efficiency of the Bitcoin price index are tested, using a nonlinear data analysis technique called the multifractal detrended fluctuation analysis (MF-DFA). In addition, we assess the time-variations in the market efficiency level through using a rolling-window framework. Our evidence shows that the efficiency of the Bitcoin market changes over time and this market seems to be more efficient during downward than upward periods. We also find that Bitcoin is marked by a persistent long memory phenomenon in its short- term components, which could be interpreted as a possible speculation by investors.

Suggested Citation

  • Refk Selmi & Aviral Kumar Tiwari & Shawkat Hammoudeh, 2018. "Efficiency or speculation? A dynamic analysis of the Bitcoin market," Economics Bulletin, AccessEcon, vol. 38(4), pages 2037-2046.
    • Handle: RePEc:ebl:ecbull:eb-18-00395
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    References listed on IDEAS

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    Cited by:

    1. Park, Sangjin & Jang, Kwahngsoo & Yang, Jae-Suk, 2021. "Information flow between bitcoin and other financial assets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    2. Arshad, Shaista & Rizvi, Syed Aun R. & Haroon, Omair & Mehmood, Fahad & Gong, Qiang, 2021. "Are oil prices efficient?," Economic Modelling, Elsevier, vol. 96(C), pages 362-370.
    3. Blau, Benjamin M. & Griffith, Todd G. & Whitby, Ryan J., 2021. "Inflation and Bitcoin: A descriptive time-series analysis," Economics Letters, Elsevier, vol. 203(C).
    4. Cesario Mateus & Bao Trung Hoang, 2021. "Frontier Markets, Liberalization and Informational Efficiency: Evidence from Vietnam," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(4), pages 499-526, December.
    5. Konstantin Gorgen & Jonas Meirer & Melanie Schienle, 2022. "Predicting Value at Risk for Cryptocurrencies With Generalized Random Forests," Papers 2203.08224, arXiv.org, revised Jun 2022.
    6. Elie Bouri & Rangan Gupta & Chi keung marco Lau & David Roubaud, 2021. "Risk aversion and Bitcoin returns in extreme quantiles," Economics Bulletin, AccessEcon, vol. 41(3), pages 1374-1386.
    7. Jamal Bouoiyour & Refk Selmi, 2019. "Beyond the Big Challenges facing Facebook's Libra," Working Papers hal-02309316, HAL.
    8. Costantini, Mauro & Maaitah, Ahmad & Mishra, Tapas & Sousa, Ricardo M., 2023. "Bitcoin market networks and cyberattacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    9. Huynh, Toan Luu Duc, 2021. "Does Bitcoin React to Trump’s Tweets?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 31(C).

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    More about this item

    Keywords

    Bitcoin; efficiency; speculation; multifractal detrended fluctuation analysis.;
    All these keywords.

    JEL classification:

    • F3 - International Economics - - International Finance
    • G1 - Financial Economics - - General Financial Markets

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