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dc.contributor.authorSoramäki, Kimmoen_US
dc.contributor.authorCook, Samanthaen_US
dc.date.accessioned2012-09-04en_US
dc.date.accessioned2012-09-04T15:18:43Z-
dc.date.available2012-09-04T15:18:43Z-
dc.date.issued2012en_US
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_US
dc.language.isoengen_US
dc.publisherKiel Institute for the World Economy (IfW) Kielen_US
dc.relation.ispartofseriesEconomics Discussion Papers 2012-43en_US
dc.subject.jelC63en_US
dc.subject.jelE58en_US
dc.subject.jelG28en_US
dc.subject.ddc330en_US
dc.subject.keywordSystemic risken_US
dc.subject.keywordinterbank payment systemen_US
dc.subject.keywordliquidityen_US
dc.subject.keywordMarkov chainsen_US
dc.subject.keywordsimulationen_US
dc.titleAlgorithm for identifying systemically important banks in payment systemsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn723776571en_US
dc.rights.licensehttp://creativecommons.org/licenses/by-nc/2.0/de/deed.enen_US
dc.identifier.repecRePEc:zbw:ifwedp:201243-
Appears in Collections:Economics: The Open-Access, Open-Assessment E-Journal - Discussion Papers

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