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Fractal dynamics and wavelet analysis: deep volatility and return properties of Bitcoin, Ethereum and Ripple

Celeste, Valerio orcid logoORCID: 0000-0002-1809-7889, Corbet, Shaen orcid logoORCID: 0000-0001-7430-7417 and Gurdgiev, Constantin orcid logoORCID: 0000-0002-5501-7614 (2019) Fractal dynamics and wavelet analysis: deep volatility and return properties of Bitcoin, Ethereum and Ripple. The Quarterly Review of Economics & 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:https://dx.doi.org/10.1016/j.qref.2019.09.011
Copyright Information:© 2019 Board of Trustees of the University of Illinois.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:25992
Deposited On:10 Jun 2021 12:24 by Thomas Murtagh . Last Modified 10 Jun 2021 12:24
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