Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/303546 
Authors: 
Year of Publication: 
2022
Citation: 
[Journal:] Cogent Economics & Finance [ISSN:] 2332-2039 [Volume:] 10 [Issue:] 1 [Article No.:] 2013588 [Year:] 2022 [Pages:] 1-18
Publisher: 
Taylor & Francis, Abingdon
Abstract: 
Two distinct and non-redundant understandings of volatility, as deviation from consistency, exist for a time-series: (1) exhibiting high standard deviation and, closer to the dictionary definition of the term, (2) appearing highly irregular and unpredictable. We find that Bitcoin is a prime example of an asset for which the two concepts of volatility diverge. We show that, historically, Bitcoin combines high Standard Deviation and low Approximate Entropy, relative to Gold and S&P 500. Moreover, subsample analysis for different time-scales (daily, weekly, monthly) shows that lower sampling frequencies drastically reduce the Kurtosis of the distribution of log-returns of Bitcoin. The opposite effect is observed for Gold and S&P 500. These properties suggest that, contrary to the volatility of the two traditional assets, Bitcoin's high volatility is essentially an intra-day phenomenon that is strongly attenuated for a weekly or monthly time-preference.
Subjects: 
Time-Series Analysis
Information Theory
Approximate Entropy
Volatility
Extreme Value Theory
Cryptocurrency
Bitcoin
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.