The chaotic, self-similar and hierarchical patterns in Bitcoin and Ethereum price series
Chaos, Solitons & Fractals, ISSN: 0960-0779, Vol: 165, Page: 112806
2022
- 11Citations
- 12Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Bitcoin (BTC) and Ethereum (ETH), pioneering public blockchains implementations, are two fundamental levers to register and transfer digital value. This article studies the structure of their daily price volatility time series following a multifaceted approach: first, it examines the existence of chaoticity and fractality in the time series. Obtained results confirm that the BTC and ETH price volatility series present signs of chaoticity, persistence of a long-term correlation and multifractality. Second, it analyses the corresponding visibility graphs associated with these time series using complex network theory. The undirected and connected complex networks, spawned by their natural visibility graphs (VGs) and horizontal visibility graphs (HVGs), present a hierarchical structure. These networks, especially the HVGs, confirm the fractality of the originating time series. The study of HVGs also confirms a lack of uncorrelated randomness in the originating BTC and ETH price series. This paper validates the value of visibility graphs as useful proxies to better understand complex time series, in this case, related to public blockchain implementations.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0960077922009857; http://dx.doi.org/10.1016/j.chaos.2022.112806; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85141421837&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0960077922009857; https://dx.doi.org/10.1016/j.chaos.2022.112806
Elsevier BV
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