{"title":"Complex network analysis of cryptocurrency market during crashes","authors":"","doi":"10.1016/j.physa.2024.130095","DOIUrl":null,"url":null,"abstract":"<div><p>This paper identifies the cryptocurrency market crashes and analyses its dynamics using the complex network. We identify three distinct crashes during 2017–20, and the analysis is carried out by dividing the time series into pre-crash, crash, and post-crash periods. Partial correlation based complex network analysis is carried out to study the crashes. Degree density (<span><math><msub><mrow><mi>ρ</mi></mrow><mrow><mi>D</mi></mrow></msub></math></span>), average path length (<span><math><mover><mrow><mi>l</mi></mrow><mrow><mo>̄</mo></mrow></mover></math></span>), and average clustering coefficient (<span><math><mover><mrow><mi>c</mi><mi>c</mi></mrow><mo>¯</mo></mover></math></span>) are estimated from these networks. We find that both <span><math><msub><mrow><mi>ρ</mi></mrow><mrow><mi>D</mi></mrow></msub></math></span> and <span><math><mover><mrow><mi>c</mi><mi>c</mi></mrow><mo>¯</mo></mover></math></span> are smallest during the pre-crash period, and spike during the crash suggesting the network is dense during a crash. Although <span><math><msub><mrow><mi>ρ</mi></mrow><mrow><mi>D</mi></mrow></msub></math></span> and <span><math><mover><mrow><mi>c</mi><mi>c</mi></mrow><mo>¯</mo></mover></math></span> decrease in the post-crash period, they remain higher than pre-crash levels for the 2017–18 and 2018–19 crashes suggesting a market attempt to return to normalcy. We get <span><math><mover><mrow><mi>l</mi></mrow><mrow><mo>̄</mo></mrow></mover></math></span> is minimal during the crash period, suggesting a rapid flow of information. A dense network and rapid information flow suggest that during a crash uninformed synchronized panic sell-off happens. However, during the 2019–20 crash, the values of <span><math><msub><mrow><mi>ρ</mi></mrow><mrow><mi>D</mi></mrow></msub></math></span>, <span><math><mover><mrow><mi>c</mi><mi>c</mi></mrow><mo>¯</mo></mover></math></span>, and <span><math><mover><mrow><mi>l</mi></mrow><mrow><mo>̄</mo></mrow></mover></math></span> did not vary significantly, indicating minimal change in dynamics compared to other crashes. The findings of this study may guide investors in making decisions during market crashes.</p></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica A: Statistical Mechanics and its Applications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378437124006046","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
This paper identifies the cryptocurrency market crashes and analyses its dynamics using the complex network. We identify three distinct crashes during 2017–20, and the analysis is carried out by dividing the time series into pre-crash, crash, and post-crash periods. Partial correlation based complex network analysis is carried out to study the crashes. Degree density (), average path length (), and average clustering coefficient () are estimated from these networks. We find that both and are smallest during the pre-crash period, and spike during the crash suggesting the network is dense during a crash. Although and decrease in the post-crash period, they remain higher than pre-crash levels for the 2017–18 and 2018–19 crashes suggesting a market attempt to return to normalcy. We get is minimal during the crash period, suggesting a rapid flow of information. A dense network and rapid information flow suggest that during a crash uninformed synchronized panic sell-off happens. However, during the 2019–20 crash, the values of , , and did not vary significantly, indicating minimal change in dynamics compared to other crashes. The findings of this study may guide investors in making decisions during market crashes.
期刊介绍:
Physica A: Statistical Mechanics and its Applications
Recognized by the European Physical Society
Physica A publishes research in the field of statistical mechanics and its applications.
Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents.
Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.