'Too central to fail' systemic risk measure adopting PageRank algorithm

Abstract

"Too central to fail" has been on the rise after 'too big to fail'. We suggested a novel systemic risk measure, Rank, adopting the PageRank algorithm. The measure effectively captures network relationships between financial institutions from centrality perspective. We compared the measure with other well-known systemic risk measures, conditional value at risk (CoVaR) and marginal expected shortfall (MES). First, we used simulation that generated bilateral connections between financial institutions. Second, we used real market data of the sample United States financial institutions. We found that Rank can well capture network structure between financial institutions than CoVaR and MES. Additionally, Rank does not have procyclical properties, which means that Rank is independent on market conditions. This paper contributes to developing timely measure using publicly available market data. The measure overcomes balance sheet-based approach that balance sheet has time lags as financial institutions release balance sheet quarterly. We also include both equity-type and liability-type assets in that systemic risk mainly propagates through intricately connected liability obligations. This work helps regulators and policy makers understand the full implications of monitoring systemic risk from network perspective.