🤖 AI Summary
Traditional binary networks struggle to capture the multilateral credit linkages arising when multiple financial institutions are simultaneously exposed to the same firm, thereby limiting the accurate assessment of systemic risk. This study addresses this gap by introducing a hypergraph framework into financial credit networks, constructing a bank–firm multilateral credit relationship model using loan registry data from the Central Bank of Argentina. The authors propose a nonlinearly weighted H-eigenvector centrality measure that integrates both neighboring institution centrality and loan exposure amounts, and combine it with an enhanced DebtRank algorithm to evaluate shock propagation effects. Empirical results demonstrate that institutions identified as systemically important under this approach exhibit significantly stronger risk amplification during adverse shocks, offering financial regulators a more precise and complementary analytical tool for systemic risk monitoring.
📝 Abstract
This paper provides the first analysis of credit relationships between financial institutions and firms through the lens of hypergraphs. Unlike traditional network approaches, which rely on pairwise connections, this framework explicitly represents the shared exposure of multiple financial institutions to the same firm as a simultaneous multilateral relationship. The approach is applied empirically to Credit Registry data from the Central Bank of Argentina, covering the period from August 2023 to December 2025 and focusing on commercial loans between banks and firms. Traditional centrality metrics are compared with hypergraph-specific measures to identify systemically relevant institutions. The paper also proposes an adjusted version of H-eigenvector centrality that nonlinearly weights both the centrality of neighboring institutions and each creditor's lending amount, in order to assess the relevance of a bank within the network. The systemic impact of shocking the top-ranked institutions according to each centrality metric is then estimated through an adaptation of the DebtRank algorithm. The results show that the proposed framework identifies institutions with greater shock-amplification capacity, providing a complementary tool for financial supervision and regulation.