Network, Market, and Book-Based Systemic Risk Rankings

We investigate the information content of stock correlation based network measures for systemic risk rankings, such as SIFIRank (based on Google's PageRank). Using European banking data, we first show that SIFIRank is empirically equivalent to a ranking based on average pairwise stock correlations. Next, we find that correlation based network measures still appear to complement currently available systemic risk ranking methods based on book or market values. A further analytical investigation, however, shows that the value-added appears to be mainly attributable to pairwise cross-sectional heterogeneity rather than to more subtle network relations and feedback loops.