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dc.contributor.authorSoramäki, Kimmoen
dc.contributor.authorCook, Samanthaen
dc.date.accessioned2012-09-04-
dc.date.accessioned2012-09-04T15:18:43Z-
dc.date.available2012-09-04T15:18:43Z-
dc.date.issued2012-
dc.identifier.urihttp://hdl.handle.net/10419/62120-
dc.description.abstractThe ability to accurately estimate the extent to which the failure of a bank disrupts the financial system is very valuable for regulators of the financial system. One important part of the financial system is the interbank payment system. This paper develops a robust measure, SinkRank, that accurately predicts the magnitude of disruption caused by the failure of a bank in a payment system and identifies banks most affected by the failure. SinkRank is based on absorbing Markov chains, which are well-suited to model liquidity dynamics in payment systems. Because actual bank failures are rare and the data is not generally publicly available, the authors test the metric by simulating payment networks and inducing failures in them. The authors use two metrics to evaluate the magnitude of the disruption: the duration of delays in the system (Congestion) aggregated over all banks and the average reduction in available funds of the other banks due to the failing bank (Liquidity dislocation). The authors test SinkRank on Barabasi-Albert types of scale-free networks modeled on the Fedwire system and find that the failing bank's SinkRank is highly correlated with the resulting disruption in the system overall; moreover, the SinkRank technology can identify which individual banks would be most disrupted by a given failure.en
dc.language.isoengen
dc.publisher|aKiel Institute for the World Economy (IfW) |cKielen
dc.relation.ispartofseries|aEconomics Discussion Papers |x2012-43en
dc.subject.jelC63en
dc.subject.jelE58en
dc.subject.jelG28en
dc.subject.ddc330en
dc.subject.keywordSystemic risken
dc.subject.keywordinterbank payment systemen
dc.subject.keywordliquidityen
dc.subject.keywordMarkov chainsen
dc.subject.keywordsimulationen
dc.titleAlgorithm for identifying systemically important banks in payment systems-
dc.typeWorking Paperen
dc.identifier.ppn723776571en
dc.rights.licensehttp://creativecommons.org/licenses/by-nc/2.0/de/deed.enen
dc.identifier.repecRePEc:zbw:ifwedp:201243en

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