📝 SummarXiv

Abstract

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.