Complex interbank network estimation: sparsity-clustering threshold

Nils Bundi, Khaldoun Khashanah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In the “too-interconnected-to-fail” discussion network theory has emerged as an important tool to identify risk concentrations in interbank networks. Therefore, however, data on bilateral bank exposures, i.e. the edges in such a network, is not available but has to be estimated. In this work we report on the possibility of enhancing existing inference techniques with prior knowledge on network topology in order to preserve complex interbank network characteristics. A convenient feature of our technique is that a single parameter α governs the characteristics of the resulting network. In an empirical study we reconstruct the network of about 2100 US commercial banks and show that complex network characteristics can indeed be preserved and, moreover, controlled by α. In an outlook we discuss the possibility of developing an α -based measurement for the complexity characteristics of observed interbank networks.

Original languageEnglish
Title of host publicationComplex Networks and Their Applications VII - Volume 2 Proceedings The 7th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2018
EditorsLuca Maria Aiello, Hocine Cherifi, Pietro Lió, Luis M. Rocha, Chantal Cherifi, Renaud Lambiotte
Pages487-498
Number of pages12
DOIs
StatePublished - 2019
Event7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018 - Cambridge, United Kingdom
Duration: 11 Dec 201813 Dec 2018

Publication series

NameStudies in Computational Intelligence
Volume813
ISSN (Print)1860-949X

Conference

Conference7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018
Country/TerritoryUnited Kingdom
CityCambridge
Period11/12/1813/12/18

Keywords

  • Interbank networks
  • Network estimation
  • Node clustering
  • Sparse networks

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