We propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic...

In a banking network model, the ranking consistency of various popular systemic risk measures (SRMs) is analyzed. In contrast to previous studies, this model-based analysis offers the advantage that the sensitivity of the ranking consistency with respect to bank and network characteristics can...

We propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic...

We propose a simple network–based methodology for ranking systemically important financial institutions. We view the risks of firms –including both the financial sector and the real economy– as a network with nodes representing the volatility shocks. The metric for the connections of the...

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...