标题： 金融网络中边的系统性影响 显示英文标题

标题： The systemic impact of edges in financial networks

摘要： 在本文中，我们评估了金融网络的稳定性如何受到相互关联性的影响，考虑其最小的变化：边。我们计算边的影响作为包含该边导致整个网络系统性风险（SR）的百分比差异。我们将此框架应用于一个详尽的巴西数据集，以计算银行-企业边的影响。在观察到（i）边对SR的影响是异质的，以及（ii）对SR影响非正的边的比例随着初始冲击水平的增加而增加后，我们使用机器学习技术尝试预测两个变量：边的关键性（定义为对SR的影响明显大于其他边的边）和边影响的符号。这些预测练习获得的准确性水平非常高。这些结果对制定旨在金融稳定的宏观审慎政策具有重要意义。我们的框架可以根据边的起始节点和目标节点的相关特征（即贷款银行和借款企业）来确定，额外的贷款是否会对SR产生显著且积极的影响。 显示英文摘要

摘要： In this paper, we assess how the stability of financial networks is affected by interconnectedness considering its tiniest variation: the edge. We compute the impact of edges as the percentage difference in the systemic risk (SR) of the whole network caused by the inclusion of that edge. We apply this framework to a thorough Brazilian dataset to compute the impact of bank-firm edges. After observing that (i) edges are heterogeneous regarding their impact on the SR, and (ii) the fraction of edges whose impact on the SR is non-positive increases with the level of the initial shock, we use machine learning techniques to try to predict two variables: the criticality of the edges (defining as critical an edge whose impact on SR is significantly greater than that of the others) and the sign of the edge impact. The level of accuracy obtained in these prediction exercises was very high. These results have important implications for the development macroprudential policies aimed at financial stability. Our framework allows to identify, based on features related to the origin and destination nodes of the edge (i.e., the lending bank and the borrowing firm), whether an additional loan will have a significant and positive impact on the SR.