Finding communities in credit networks

Leonardo Bargigli () and Mauro Gallegati

Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), 2013, vol. 7, No 2013-17, 39 pages

Abstract: In 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 verify the hypothesis that the network under study truly has communities. Secondly, they devise a reliable algorithm to find those communities. In order to be sure that a given algorithm works, they 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 network risk depends crucially on community structure, their results suggest that policy maker should identify systemically important communities, i.e. those able extend the initial shock to the entire system.

Keywords: credit networks; communities; contagion; systemic risk (search for similar items in EconPapers)
JEL-codes: C49 C63 D85 E51 G21 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://dx.doi.org/10.5018/economics-ejournal.ja.2013-17
https://www.econstor.eu/bitstream/10419/73462/1/742435172.pdf (application/pdf)

Related works:
Working Paper: Finding communities in credit networks (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:ifweej:201317

DOI: 10.5018/economics-ejournal.ja.2013-17

Access Statistics for this article

Economics - The Open-Access, Open-Assessment E-Journal (2007-2020) is currently edited by Dennis J. Snower

More articles in Economics - The Open-Access, Open-Assessment E-Journal (2007-2020) from Kiel Institute for the World Economy (IfW Kiel) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().