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Disentangling bipartite and core-periphery structure in financial networks

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  • Paolo Barucca
  • Fabrizio Lillo

Abstract

A growing number of systems are represented as networks whose architecture conveys significant information and determines many of their properties. Examples of network architecture include modular, bipartite, and core-periphery structures. However inferring the network structure is a non trivial task and can depend sometimes on the chosen null model. Here we propose a method for classifying network structures and ranking its nodes in a statistically well-grounded fashion. The method is based on the use of Belief Propagation for learning through Entropy Maximization on both the Stochastic Block Model (SBM) and the degree-corrected Stochastic Block Model (dcSBM). As a specific application we show how the combined use of the two ensembles -SBM and dcSBM- allows to disentangle the bipartite and the core-periphery structure in the case of the e-MID interbank network. Specifically we find that, taking into account the degree, this interbank network is better described by a bipartite structure, while using the SBM the core-periphery structure emerges only when data are aggregated for more than a week.

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  • Paolo Barucca & Fabrizio Lillo, 2015. "Disentangling bipartite and core-periphery structure in financial networks," Papers 1511.08830, arXiv.org.
    • Handle: RePEc:arx:papers:1511.08830
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    References listed on IDEAS

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    1. de Masi, G. & Iori, G. & Caldarelli, G., 2006. "A fitness model for the Italian interbank money market," Working Papers 06/08, Department of Economics, City University London.
    2. Iori, Giulia & Mantegna, Rosario N. & Marotta, Luca & Miccichè, Salvatore & Porter, James & Tumminello, Michele, 2015. "Networked relationships in the e-MID interbank market: A trading model with memory," Journal of Economic Dynamics and Control, Elsevier, vol. 50(C), pages 98-116.
    3. Paolo Barucca & Fabrizio Lillo, 2015. "The organization of the interbank network and how ECB unconventional measures affected the e-MID overnight market," Papers 1511.08068, arXiv.org, revised Sep 2017.
    4. in ’t Veld, Daan & van Lelyveld, Iman, 2014. "Finding the core: Network structure in interbank markets," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 27-40.
    5. Iori, G. & Masi, G. D. & Precup, O. V. & Gabbi, G. & Caldarelli, G., 2005. "A network analysis of the Italian overnight money market," Working Papers 05/05, Department of Economics, City University London.
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    7. Fricke, Daniel & Lux, Thomas, 2012. "Core-periphery structure in the overnight money market: Evidence from the e-MID trading platform," Kiel Working Papers 1759, Kiel Institute for the World Economy (IfW Kiel).
    8. Michael Boss & Helmut Elsinger & Martin Summer & Stefan Thurner, 2004. "Network topology of the interbank market," Quantitative Finance, Taylor & Francis Journals, vol. 4(6), pages 677-684.
    9. Fricke, Daniel & Finger, Karl & Lux, Thomas, 2013. "On assortative and disassortative mixing in scale-free networks: The case of interbank credit networks," Kiel Working Papers 1830, Kiel Institute for the World Economy (IfW Kiel).
    10. Iori, Giulia & De Masi, Giulia & Precup, Ovidiu Vasile & Gabbi, Giampaolo & Caldarelli, Guido, 2008. "A network analysis of the Italian overnight money market," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 259-278, January.
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    1. Paolo Barucca & Fabrizio Lillo, 2015. "The organization of the interbank network and how ECB unconventional measures affected the e-MID overnight market," Papers 1511.08068, arXiv.org, revised Sep 2017.

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