Systemic risk of financial institutions and sectoral companies relies on their inter-dependencies. The inter-connectivity of the financial networks has proven to be crucial to understand the propagation of default, as it plays a central role to assess the impact of single default events in the full system. Here, we take advantage of complex network theory to shed light on the mechanisms behind default propagation. Using real data from the BBVA, the second largest bank in Spain, we extract a financial network from customer-supplier transactions among more than \(140\text{,}000\) companies, and their economic flows. Then, we introduce a computational model, inspired by the probabilities of default contagion, that allow us to obtain the main statistics of default diffusion given the network structure at individual and system levels. Our results show the exposure of different sectors to default cascades, therefore allowing for a quantification and ranking of sectors accordingly. This information is relevant to propose countermeasures to default propagation in specific scenarios.

中文翻译：

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评估相互联系的部门金融网络中违约传播的风险

金融机构和部门公司的系统性风险依赖于它们之间的相互依赖关系。事实证明，金融网络的相互联系对于理解违约的传播至关重要，因为它在评估单个违约事件在整个系统中的影响方面起着核心作用。在这里，我们利用复杂的网络理论来阐明默认传播背后的机制。利用西班牙第二大银行BBVA的真实数据，我们从超过\（140 \ text {，} 000 \）之间的客户-供应商交易中提取了一个财务网络公司及其经济流量。然后，我们引入一个计算模型，该模型受默认蔓延的可能性的启发，使我们可以在给定网络结构在个人和系统级别的情况下，获得默认扩散的主要统计信息。我们的结果表明，不同的部门暴露于默认级联，因此可以相应地对部门进行量化和排名。此信息与在特定情况下建议针对默认传播的对策有关。