摘要：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 financial network. Too-connected-to-fail risk and vulnerability rankings are quite robust to the choice of centrality measure. The PageRank centrality measure, however, does not seem as suitable for assessing vulnerabilities. Two community identification methods, edge betweenness and the map equation (Infomap) were used to identify systemic communities, which in turn capture the too-important-tofail dimension of systemic risk. The first method appears more robust to di erent weighting schemes but 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 each other for assessing systemic risk in financial networks.
