🤖 AI Summary
This study addresses the challenge of identifying systemic risk in the Eurozone interbank market. Methodologically, it constructs, for the first time, a dynamic weighted directed network model grounded in high-frequency transaction data, integrating complex network analysis, multidimensional centrality metrics (betweenness, in-degree, out-degree), robustness simulations, and community detection algorithms to capture the market’s empirical topology. Results reveal that the network exhibits weak connectivity, high clustering, short average path length, and scale-free properties. A small set of core hub banks dominates interbank funding flows; their failure could disrupt over 30% of transaction volume. These findings provide quantifiable, dynamic, network-theoretic foundations for understanding systemic risk propagation pathways, pinpointing structural vulnerabilities, and informing macroprudential regulatory design—thereby advancing both theoretical modeling and policy-relevant systemic risk assessment in financial networks.
