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Volatility and returns connectedness in cryptocurrency markets: insights from graph-based methods

Nguyen, An Pham Ngoc, Mai, Tai Tan orcid logoORCID: 0000-0001-6657-0872, Bezbradica, Marija orcid logoORCID: 0000-0001-9366-5113 and Crane, Martin orcid logoORCID: 0000-0001-9366-5113 (2023) Volatility and returns connectedness in cryptocurrency markets: insights from graph-based methods. Physica A: Statistical Mechanics and its Applications, 632 . ISSN 0378-4371

Abstract
We employ graph-based methods to examine the connectedness between cryptocurrencies of different market caps over time. By applying denoising and detrending techniques inherited from Random Matrix Theory and the concept of the so-called Market Component, we are able to extract new insights from historical return and volatility time series. Notably, our analysis reveals that changes in volatility-based network structure can be used to identify major events that have, in turn, impacted the cryptocurrency market. Additionally, we find that these structures reflect investors’ sentiments, including emotions like fear and greed. Using metrics such as PageRank, we discover that certain minor coins unexpectedly exert a disproportionate influence on the market, while the largest cryptocurrencies such as BTC and ETH seem less influential. We suggest that our findings have practical implications for investors in different ways: Firstly, helping them to avoid major market disruptions such as crashes, to safeguard their investments, and to capitalize on opportunities for high returns; Secondly, sharpening and optimizing the portfolios thanks to the understanding of cryptocurrencies’ connectedness.
Metadata
Item Type:Article (Published)
Refereed:Yes
Additional Information:Article number: 129349
Uncontrolled Keywords:Cryptocurrencies; Volatility; Correlation-based network; Graph-based metrics; Influential cryptocurrencies
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
Physical Sciences > Statistical physics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Publisher:Elsevier
Official URL:https://doi.org/10.1016/j.physa.2023.129349
Copyright Information:© 2023 The Authors
Funders:Science Foundation Ireland Centre for Research Training in Artificial Intelligence (CRT-AI) grant number 18/CRT/6223 (APN Nguyen)., Science Foundation Ireland ADAPT Research Centre Grant Agreement 13/RC/2106_P2 (MC, MB)
ID Code:29235
Deposited On:23 Nov 2023 13:07 by Martin Crane . Last Modified 23 Nov 2023 13:07
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