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On Assortative and Disassortative Mixing Scale-Free Networks: The Case of Interbank Credit Networks

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  • Daniel Fricke
  • Karl Finger
  • Thomas Lux

Abstract

Networks constructed from credit relationships in the interbank market have been found to exhibit disassortative mixing together with a scale-free degree distribution, in contrast to most social networks that are assortative and not necessarily scale-free. This provokes the question whether generating mechanisms for scale-free networks have enough flexibility to generate both assortative and disassortative structures depending on their parametrization. Using Monte-Carlo simulations, we show that scale-free networks with a small tail exponent tend to be disassortative. However, the simulations indicate also that the level of disassortativity is sensitive to changes in the scaling exponent and the density. A given combination of disassortativity, scaling of the degree distribution, and density in an empirical data set, might be hard or impossible to obtain from any of the known generating mechanisms for scale-free networks

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File URL: http://www.ifw-members.ifw-kiel.de/publications/on-assortative-and-disassortative-mixing-scale-free-networks-the-case-of-interbank-credit-networks/13OnAssortative_final.pdf
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Bibliographic Info

Paper provided by Kiel Institute for the World Economy in its series Kiel Working Papers with number 1830.

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Length: 18 pages
Date of creation: Feb 2013
Date of revision:
Handle: RePEc:kie:kieliw:1830

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Related research

Keywords: Interbank Market; Network Models; Scale-Free Networks; Powerlaw;

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References

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  1. Kimmo Soramaki & Morten L. Bech & Jeffrey Arnold & Robert J. Glass & Walter Beyeler, 2006. "The topology of interbank payment flows," Staff Reports 243, Federal Reserve Bank of New York.
  2. Michael D. König & Claudio J. Tessone & Yves Zenou, 2010. "From Assortative To Dissortative Networks: The Role Of Capacity Constraints," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 13(04), pages 483-499.
  3. Karl Finger & Daniel Fricke & Thomas Lux, 2013. "Network analysis of the e-MID overnight money market: the informational value of different aggregation levels for intrinsic dynamic processes," Computational Management Science, Springer, vol. 10(2), pages 187-211, June.
  4. G. De Masi & G. Iori & G. Caldarelli, 2006. "A fitness model for the Italian Interbank Money Market," Papers physics/0610108, arXiv.org.
  5. Beaupain, Renaud & Durré, Alain, 2012. "Nonlinear liquidity adjustments in the euro area overnight money market," Working Paper Series 1500, European Central Bank.
  6. Thomas Lux, Daniel Fricke, 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.
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Cited by:
  1. Leonardo Bargigli & Giovanni Di Iasio & Luigi Infante & Fabrizio Lillo & Federico Pierobon, 2013. "The Multiplex Structure of Interbank Networks," Working Papers - Economics wp2013_26.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
  2. Schrader, Klaus & Bencek, David & Laaser, Claus-Friedrich, 2013. "IfW-Krisencheck: Alles wieder gut in Griechenland?," Kiel Discussion Papers 522/523, Kiel Institute for the World Economy (IfW).

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