Critical Edges in Financial Networks

In this study, we propose a method for the identification of influential edges in financial networks. In our approach, the critical edges are those whose removal would cause a large impact on the systemic risk of the financial network. We apply this framework to a thorough Brazilian data set to identify critical bank-firm edges. In our data set, banks and firms are connected through two financial networks: the interbank network and the bank-firm loan network. We found at least 18% of the edges are critical, in the sense they have a significant impact on the systemic risk of the network. We then employed machine learning (ML) techniques to predict the critical status and – for a large level of the initial shock – the sign of the impact of bank-firm edges on the systemic risk. The level of accuracy obtained in these prediction exercises was very high (above 90%). Posterior analysis through Shapley values shows: i) the PageRank of the edge’s destination node (the firm) is the main driver of the critical status of the edges; and ii) the sign of the edges’ impact depends on the degree of the edge’s origin node (the bank).