AI 中文总结
研究通过超图视角分析金融机构与企业信贷关系，提出新中心性指标，比较传统与超图特定指标识别系统相关机构，改编算法估计机构受冲击的系统影响，为金融监管提供补充工具。
AI 中文摘要
本文首次通过超图视角分析金融机构与企业之间的信贷关系。与传统依赖双边连接的网络方法不同，该框架将多个金融机构对同一企业的共同风险敞口明确表示为同步多边关系。实证应用于阿根廷中央银行2023年8月至2025年12月的信贷登记数据，聚焦银行与企业间商业贷款。比较传统中心性指标与超图特定指标以识别系统相关机构。还提出调整后的H特征向量中心性，非线性加权相邻机构中心性及各债权人贷款额。通过改编DebtRank算法估计按各中心性指标排名靠前机构受冲击的系统影响。结果表明该框架能识别冲击放大能力更强的机构，为金融监管提供补充工具。
英文摘要
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.
Comments26 pages, 11 figures, 2 tables
