Celeste, Valerio ORCID: 0000-0002-1809-7889, Corbet, Shaen ORCID: 0000-0001-7430-7417 and Gurdgiev, Constantin ORCID: 0000-0002-5501-7614 (2019) Fractal dynamics and wavelet analysis: deep volatility and return properties of Bitcoin, Ethereum and Ripple. Quarterly Review of Economics and Finance, 76 . pp. 310-324. ISSN 1062-9769
Abstract
The substantial volatility and growth in cryptocurrencies valuations between 2009 and the end
of 2017 strongly suggest that both long memory and price volatility and return spillovers should be
present in these assets’ dynamics. To date, literature on the major cryptocurrencies price processes
does not address jointly and comprehensively their fractal properties, long memory and wavelet
analysis, that could robustly confirm the presence of fractal dynamics in their prices, and confirm
or deny the validity of the Fractal Market Hypothesis as being applicable to the cryptocurrencies.
This research shows that Bitcoin prices exhibit long term memory, although its trend has been
reducing overtime. In fact, assessing Bitcoin, Ethereum and Ripple across the period between 2016
and 2017, focusing solely on the period prior to the crash of 2018, we can conclude that Bitcoin was
better described by a random walk, showing signs of markets maturity emerging, in contrast, other
cryptocurrencies such as Ethereum and Ripple present evidence of a growing underlying memory
behaviour.
Metadata
Item Type: | Article (Published) |
---|---|
Refereed: | Yes |
Uncontrolled Keywords: | Efficient Market Hypothesis; Fractal Market Hypothesis; Cryptocurrencies; Wavelet Coherence; Continuous Wavelet Transform; Hurst Exponent |
Subjects: | Business > Finance |
DCU Faculties and Centres: | DCU Faculties and Schools > DCU Business School |
Publisher: | Elsevier |
Official URL: | http://dx.doi.org/10.1016/j.qref.2019.09.011 |
Copyright Information: | © 2020 Elsevier. CC BY-NC-ND |
ID Code: | 25044 |
Deposited On: | 01 Oct 2020 11:48 by Thomas Murtagh . Last Modified 10 Sep 2021 03:30 |
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