Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/62119 
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dc.contributor.authorBargigli, Leonardoen
dc.contributor.authorGallegati, Mauroen
dc.date.accessioned2012-09-04-
dc.date.accessioned2012-09-04T15:18:42Z-
dc.date.available2012-09-04T15:18:42Z-
dc.date.issued2012-
dc.identifier.urihttp://hdl.handle.net/10419/62119-
dc.description.abstractIn this paper the authors focus on credit connections as a potential source of systemic risk. In particular, they seek to answer the following question: how do we find densely connected subsets of nodes within a credit network? The question is relevant for policy, since these subsets are likely to channel any shock affecting the network. As it turns out, a reliable answer can be obtained with the aid of complex network theory. In particular, the authors show how it is possible to take advantage of the community detection network literature. The proposed answer entails two subsequent steps. Firstly, the authors need to verify the hypothesis that the network under study truly has communities. Secondly, they need to devise a reliable algorithm to find those communities. In order to be sure that a given algorithm works, they need to test it over a sample of random benchmark networks with known communities. To overcome the limitation of existing benchmarks, the authors introduce a new model and test alternative algorithms, obtaining very good results with an adapted spectral decomposition method. To illustrate this method they provide a community description of the Japanese bank-firm credit network, getting evidence of a strengthening of communities over time and finding support for the well-known Japanese main bank system. Thus, the authors find comfort both from simulations and from real data on the possibility to apply community detection methods to credit markets. They believe that this method can fruitfully complement the study of contagious defaults, since the likelihood of intracommunity default contagion is expected to be high.en
dc.language.isoengen
dc.publisher|aKiel Institute for the World Economy (IfW) |cKielen
dc.relation.ispartofseries|aEconomics Discussion Papers |x2012-41en
dc.subject.jelC49en
dc.subject.jelC63en
dc.subject.jelD85en
dc.subject.jelE51en
dc.subject.jelG21en
dc.subject.ddc330en
dc.subject.keywordCredit networksen
dc.subject.keywordcommunitiesen
dc.subject.keywordcontagionen
dc.subject.keywordsystemic risken
dc.titleFinding communities in credit networks-
dc.typeWorking Paperen
dc.identifier.ppn723775443en
dc.rights.licensehttp://creativecommons.org/licenses/by-nc/2.0/de/deed.enen
dc.identifier.repecRePEc:zbw:ifwedp:201241en

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