Transitions in the Cryptocurrency Market During the COVID-19 Pandemic: A Network Analysis

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Abstract

This study examines cryptocurrency market transitions during the COVID-19 pandemic using network analysis. Key findings reveal:

Keywords: Cryptocurrencies, COVID-19, Network Analysis, Market Synchronization, Herding, Diversification


1. Introduction

The COVID-19 pandemic disrupted global financial markets, including cryptocurrencies. While existing research focused on Bitcoin and small crypto subsets, this paper analyzes 69 long-lived cryptocurrencies to uncover systemic trends.

Key Research Objectives:

  1. Assess COVID-19’s impact on market synchronization.
  2. Track recovery phases via network centrality metrics.
  3. Link market transitions to financial theories (efficiency, herding).

2. Data & Methodology

2.1 Data Sources

2.2 Network Analysis Framework

Key Metrics:

| Metric | Financial Interpretation |
|----------------------|---------------------------------------------------|
| Degree Centrality | Measures interconnectedness; spikes indicate panic periods. |
| Betweenness Centrality | Identifies "bridge" assets; lows suggest uniform co-movement. |


3. Empirical Results

3.1 Market Phases Identified

  1. Pre-Shock (Aug 2019–Mar 2020): Stable core-periphery network.
  2. Panic Phase (Mar 12–Apr 1, 2020):

    • Average degree centrality surged to 40 (vs. pre-shock average 15).
    • Fully connected network topology.
  3. Recovery (Apr–Jun 2020): Gradual decline in synchronization.
  4. Post-July 2020: Pre-pandemic conditions restored.

3.2 Financial Implications


4. Conclusion

Key Takeaways:

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FAQs

Q1: How long did COVID-19 disrupt the cryptocurrency market?
A1: Intense synchronization lasted only 3 weeks (March 12–April 1, 2020). Recovery began by April and stabilized post-July.

Q2: Did Bitcoin behave as a safe haven during the pandemic?
A2: No—Bitcoin amplified contagion initially but rebounded faster than stocks (Caferra & Vidal-Tomás, 2020).

Q3: Why use network analysis over traditional methods?
A3: Networks capture systemic dynamics, revealing phases static models miss (e.g., panic synchronization).

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