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Finding communities in credit networks

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  • Bargigli, Leonardo
  • Gallegati, Mauro

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.

Suggested Citation

  • Bargigli, Leonardo & Gallegati, Mauro, 2013. "Finding communities in credit networks," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 7, pages 1-39.
  • Handle: RePEc:zbw:ifweej:201317
    DOI: 10.5018/economics-ejournal.ja.2013-17
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    References listed on IDEAS

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    Cited by:

    1. Catullo, Ermanno & Gallegati, Mauro & Palestrini, Antonio, 2015. "Towards a credit network based early warning indicator for crises," Journal of Economic Dynamics and Control, Elsevier, vol. 50(C), pages 78-97.
    2. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
    3. Silva, Thiago Christiano & de Souza, Sergio Rubens Stancato & Tabak, Benjamin Miranda, 2016. "Network structure analysis of the Brazilian interbank market," Emerging Markets Review, Elsevier, vol. 26(C), pages 130-152.
    4. Casarin, Roberto & Costola, Michele & Yenerdag, Erdem, 2018. "Financial bridges and network communities," SAFE Working Paper Series 208, Leibniz Institute for Financial Research SAFE.
    5. Wäckerle, Manuel, 2013. "On the bottom-up foundations of the banking-macro nexus," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 7, pages 1-45.
    6. Dániel Kondor & Márton Pósfai & István Csabai & Gábor Vattay, 2014. "Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.

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    More about this item

    Keywords

    credit networks; communities; contagion; systemic risk;
    All these keywords.

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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