Complex network analysis of cryptocurrency market during crashes
Kundan Mukhia, Anish Rai, SR Luwang, Md Nurujjaman, Sushovan Majhi,, Chittaranjan Hens

TL;DR
This study uses complex network analysis to examine cryptocurrency market crashes, revealing patterns of increased network density and rapid information flow during crashes, which can inform investor decision-making.
Contribution
It introduces a partial correlation based complex network approach to analyze market crashes and identifies distinct network behavior patterns during different crash periods.
Findings
Network density and clustering coefficient spike during crashes
Rapid information flow occurs during market crashes
Post-crash networks remain denser than pre-crash levels
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…
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Taxonomy
TopicsComplex Network Analysis Techniques
