Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1-4 |
| Number of pages | 4 |
| Journal | Economics Letters |
| Volume | 161 |
| DOIs | |
| State | Published - Dec 2017 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2017 Elsevier B.V.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure -
SDG 10 Reduced Inequalities -
SDG 17 Partnerships for the Goals
Keywords
- Bitcoin
- Hurst exponent
- Long range dependence
- Volatility
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