"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.
"Too-central-to-fail"은 금융 분야에서 새로운 흐름으로 주목 받고 있다. 본 연구에서는 집중도 측면에서의 새로운 체계적 위험 측정 방법, Rank를 제안하였다. 제안된 측정 방법을 기존의 대표적인 체계적 위험 측정 방식들과 비교하였다. 시뮬레이션을 통해 생성된 가상의 시장 데이터와 실제 미국 시장 데이터를 이용해, Rank가 효과적으로 금융기관들간의 구조를 반영하는 것을 보였다. 또한 비-경기순응적인 측정 방식이라는 점도 확인하였다.
