Systemic centrality and systemic communities in financial networks

A systemically important firm could be too-connected-to-fail and/or too-important-to-fail,two properties which centrality measures and community detection methods can capture respectively.This paper examines the performance of these measures in a variance decomposition global financialnetwork. Too-connected-to-fail risk and vulnerability rankings are quite robust to the choice ofcentrality measure. The PageRank centrality measure, however, does not seem as suitable for assessingvulnerabilities. Two community identification methods, edge betweenness and the map equation(Infomap) were used to identify systemic communities, which in turn capture the too-important-tofaildimension of systemic risk. The first method appears more robust to di erent weighting schemesbut tends to isolate too many firms. The second method exhibits the opposite characteristics. Overall,the analysis suggests that centrality measures and community identification methods complement eachother for assessing systemic risk in financial networks.