The inefficiency of Bitcoin revisited: A dynamic approach

This letter revisits the informational efficiency of the Bitcoin market. In particular we analyze the time-varying behavior of long memory of returns on Bitcoin and volatility 2011 until 2017, using the Hurst exponent. Our results are twofold. First, R/S method is prone to detect long memory, wherea...

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Bibliographic Details
Published in:Economics letters 2017-12, Vol.161, p.1-4
Main Author: Bariviera, Aurelio F.
Format: Article
Language:English
Subjects:
Behavior
Bitcoin
Digital currencies
Hurst exponent
Long range dependence
Market prices
Memory
Prices
Prone
Studies
Volatility
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Summary:This letter revisits the informational efficiency of the Bitcoin market. In particular we analyze the time-varying behavior of long memory of returns on Bitcoin and volatility 2011 until 2017, using the Hurst exponent. Our results are twofold. First, R/S method is prone to detect long memory, whereas DFA method can discriminate more precisely variations in informational efficiency across time. Second, daily returns exhibit persistent behavior in the first half of the period under study, whereas its behavior is more informational efficient since 2014. Finally, price volatility, measured as the logarithmic difference between intraday high and low prices exhibits long memory during all the period. This reflects a different underlying dynamic process generating the prices and volatility. •We study long-range dependence of Bitcoin return and volatility.•Hurst exponent is computed using overlapping sliding windows.•Daily return time series become more efficient across time.•Daily volatility exhibits long-range memory during all the period.
ISSN:0165-1765
1873-7374
DOI:10.1016/j.econlet.2017.09.013