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Multifractal Detrended Fluctuation Analysis of Return on Bitcoin

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  • Keshab Shrestha

Abstract

We revisit the issue of market efficiency of Bitcoin, which is an important part of the new financial technology (FinTech), by analyzing the Bitcoin returns using two recently developed analytical techniques called bipower variation method and Multifractal Detrended Fluctuation Analysis (MF‐DFA). MF‐DFA allows us to analyze the return series in ways not possible using a monofractal analytical techniques such as detrended fluctuation analysis (DFA) and R/S method. The bipower variation method suggests that the Bitcoin returns are efficient and contain some large finite jumps. Using MF‐DFA, we find that the Bitcoin returns are multifractal and, therefore, the Bitcoin market is not efficient. By carrying out further analysis, we also find that the multifractility and inefficiency are caused by the autocorrelated returns as well as extreme returns.

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  • Keshab Shrestha, 2021. "Multifractal Detrended Fluctuation Analysis of Return on Bitcoin," International Review of Finance, International Review of Finance Ltd., vol. 21(1), pages 312-323, March.
    • Handle: RePEc:bla:irvfin:v:21:y:2021:i:1:p:312-323
    DOI: 10.1111/irfi.12256
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    References listed on IDEAS

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    Cited by:

    1. Shrestha, Keshab & Naysary, Babak & Philip, Sheena Sara Suresh, 2023. "Fintech market efficiency: A multifractal detrended fluctuation analysis," Finance Research Letters, Elsevier, vol. 54(C).
    2. Chowdhury, Mohammad Ashraful Ferdous & Abdullah, Mohammad & Masih, Mansur, 2022. "COVID-19 government interventions and cryptocurrency market: Is there any optimum portfolio diversification?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    3. Emmanuel Joel Aikins Abakah & Aviral Kumar Tiwari & Chi‐Chuan Lee & Matthew Ntow‐Gyamfi, 2023. "Quantile price convergence and spillover effects among Bitcoin, Fintech, and artificial intelligence stocks," International Review of Finance, International Review of Finance Ltd., vol. 23(1), pages 187-205, March.
    4. Foued Sa^adaoui, 2023. "Structured Multifractal Scaling of the Principal Cryptocurrencies: Examination using a Self-Explainable Machine Learning," Papers 2304.08440, arXiv.org.

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