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Multifractal Detrended Fluctuation Analysis of Return on Bitcoin *
Author(s) -
Shrestha Keshab
Publication year - 2021
Publication title -
international review of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 18
eISSN - 1468-2443
pISSN - 1369-412X
DOI - 10.1111/irfi.12256
Subject(s) - detrended fluctuation analysis , multifractal system , inefficiency , econometrics , autocorrelation , efficient market hypothesis , hurst exponent , cryptocurrency , variation (astronomy) , computer science , economics , statistical physics , mathematics , statistics , fractal , stock market , physics , mathematical analysis , paleontology , geometry , computer security , horse , scaling , astrophysics , microeconomics , biology
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.