![]() In particular, after Mantegna’s groundbreaking research, complex network theory has been successfully implemented in the financial market, and various empirical analyzes of the stock market have been conducted based on this theory. Various studies about complex networks have been performed in social interactions, communication networks, biological networks, transportation infrastructures, and so on. Therefore, these components describe complicated real-world systems from different and complementary perspectives and these are a new and rich source of domain-specific information. Fundamentally, a complex network is composed of two basic components: nodes that are elements of the system and edges that represent the pairwise relationships between those elements. Thus, complex network analysis has become a powerful method, and it provides a useful map that describes a wide range of systems of high technological and intellectual importance. Recent advances in science and technology have created large data sets in a variety of fields, and we live in a complex world where they are interconnected. All remaining relevant data are in the Supporting information files.įunding: The author(s) received no specific funding for this work.Ĭompeting interests: The authors have declared that no competing interests exist. Others would be able to access these data in the same manner as the authors. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The data underlying the results presented in the paper are publicly available from. ![]() Received: AugAccepted: OctoPublished: February 18, 2022Ĭopyright: © 2022 Hong, Yoon. Our results provide insight and expectations for investors in terms of sharing information about cryptocurrencies amid the uncertainty posed by the COVID-19 pandemic.Ĭitation: Hong MY, Yoon JW (2022) The impact of COVID-19 on cryptocurrency markets: A network analysis based on mutual information. In particular, during the post-COVID-19 period, it can be seen that Ethereum and Qtum are the most influential cryptocurrencies in both methods. Moreover, the results of graphs constructed by each method are different in topological and statistical properties and the network’s behavior. ![]() Numerical results demonstrate that the degree distribution follows the power-law and the graphs after the COVID-19 outbreak have noticeable differences in network measurements compared to before. Furthermore, we study the statistical and topological properties of these networks. Based on these two methods, we construct networks by applying the minimum spanning tree and the planar maximally filtered graph. To construct a cryptocurrency network, we first apply a mutual information method to the daily log return values of 102 digital currencies from January 1, 2019, to December 31, 2020, and also apply a correlation coefficient method for comparison. The purpose of our study is to figure out the transitions of the cryptocurrency market due to the outbreak of COVID-19 through network analysis, and we studied the complexity of the market from different perspectives. ![]()
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