Community-level Contagion among Diverse Financial Assets

ArXiv ID: 2509.15232 “View on arXiv”

Authors: An Pham Ngoc Nguyen, Marija Bezbradica, Martin Crane

Abstract

As global financial markets become increasingly interconnected, financial contagion has developed into a major influencer of asset price dynamics. Motivated by this context, our study explores financial contagion both within and between asset communities. We contribute to the literature by examining the contagion phenomenon at the community level rather than among individual assets. Our experiments rely on high-frequency data comprising cryptocurrencies, stocks and US ETFs over the 4-year period from April 2019 to May 2023. Using the Louvain community detection algorithm, Vector Autoregression contagion detection model and Tracy-Widom random matrix theory for noise removal from financial assets, we present three main findings. Firstly, while the magnitude of contagion remains relatively stable over time, contagion density (the percentage of asset pairs exhibiting contagion within a financial system) increases. This suggests that market uncertainty is better characterized by the transmission of shocks more broadly than by the strength of any single spillover. Secondly, there is no significant difference between intra- and inter-community contagion, indicating that contagion is a system-wide phenomenon rather than being confined to specific asset groups. Lastly, certain communities themselves, especially those dominated by Information Technology assets, tend to act as major contagion transmitters in the financial network over the examined period, spreading shocks with high densities to many other communities. Our findings suggest that traditional risk management strategies such as portfolio diversification through investing in low-correlated assets or different types of investment vehicle might be insufficient due to widespread contagion.

Keywords: Financial Contagion, Community Detection (Louvain), Vector Autoregression (VAR), High-Frequency Data, Random Matrix Theory

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical techniques like Vector Autoregression and Tracy-Widom random matrix theory, and its empirical rigor is high due to the use of a specific 4-year high-frequency dataset, detailed methodology, and robustness checks.

  flowchart TD
    A["Research Goal:<br>Explore financial contagion<br>within/between asset communities"] --> B["Data: High-frequency data<br>2019-2023: Crypto, Stocks, ETFs"]
    B --> C["Noise Reduction:<br>Tracy-Widom Random Matrix Theory"]
    C --> D["Community Detection:<br>Louvain Algorithm"]
    C --> E["Contagion Detection:<br>Vector Autoregression VAR"]
    D --> F{"Final Analysis & Findings"}
    E --> F
    F --> G["1. Contagion Density ↑<br>Strength stable, breadth increases"]
    F --> H["2. No Intra/Inter difference<br>System-wide phenomenon"]
    F --> I["3. Key Transmitters:<br>Info Tech communities dominate"]