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“Too central to fail” systemic risk measure using PageRank algorithm

Tae-Sub Yun, Deokjong Jeong and Sunyoung Park

Journal of Economic Behavior & Organization, 2019, vol. 162, issue C, 251-272

Abstract: Following the popularity of the concepts of “too big to fail” and “too connected to fail” after the global financial crisis, the concept of “too central to fail” has garnered considerable attention recently. In this study, we suggest a “too central to fail” systemic risk measure, Rank, using the PageRank algorithm. Then, adopting a centrality perspective, we compare this measure, which effectively captures network relationships among financial institutions, with other well-known systemic risk measures, conditional value at risk (CoVaR) and marginal expected shortfall (MES). First, we model a simulation that generates bilateral connections among financial institutions. Second, we use real market data representing United States financial institutions. We show that Rank can capture the network structure among financial institutions better than CoVaR and MES. Further, Rank does not have procyclical properties; therefore, it is not dependent on market conditions. This study contributes to the development of a timely measure using publicly available market data. The measure also overcomes the shortcomings of the balance sheet-based approach, which is subject to time lags, because financial institutions release balance sheets quarterly basis. We also include equity and liability-type assets, in which systemic risks mainly propagate through intricately connected liability obligations. The findings will help regulators and policy-makers understand the implications of monitoring systemic risks from a network perspective.

Keywords: Systemic risk; Network structure; Centrality; Too central to fail; Simulation; PageRank (search for similar items in EconPapers)
JEL-codes: C63 C90 D85 E44 G21 G28 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.jebo.2018.12.021

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Journal of Economic Behavior & Organization is currently edited by Houser, D. and Puzzello, D.

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