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It Takes Two to Tango, but More to Assess Systemic Risk: Credit Networks Through the Lens of Hypergraphs

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  • Federico D. Forte

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.

Suggested Citation

  • Federico D. Forte, 2026. "It Takes Two to Tango, but More to Assess Systemic Risk: Credit Networks Through the Lens of Hypergraphs," Papers 2607.10943, arXiv.org.
    • Handle: RePEc:arx:papers:2607.10943
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    References listed on IDEAS

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    1. Marco Bardoscia & Stefano Battiston & Fabio Caccioli & Guido Caldarelli, 2015. "DebtRank: A Microscopic Foundation for Shock Propagation," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-13, June.
    2. Silva, Thiago Christiano & Alexandre, Michel da Silva & Tabak, Benjamin Miranda, 2018. "Bank lending and systemic risk: A financial-real sector network approach with feedback," Journal of Financial Stability, Elsevier, vol. 38(C), pages 98-118.
    3. G. De Masi & M. Gallegati, 2012. "Bank–firms topology in Italy," Empirical Economics, Springer, vol. 43(2), pages 851-866, October.
    4. Elosegui, Pedro & Forte, Federico D. & Montes-Rojas, Gabriel, 2022. "Network structure and fragmentation of the Argentinean interbank markets," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(3).
    5. Martinez-Jaramillo, Serafin & Carmona, Christian U. & Kenett, Dror Y., 2019. "Interconnectedness and financial stability," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 12(2), pages 168-183, March.
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    More about this item

    JEL classification:

    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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