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Complex Interbank Network Estimation: Sparsity-Clustering Threshold

  • Nils Bundi
  • Khaldoun Khashanah
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)

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 \(\alpha \) 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 \(\alpha \). In an outlook we discuss the possibility of developing an \(\alpha \)-based measurement for the complexity characteristics of observed interbank networks.

Keywords

Interbank networks Network estimation Sparse networks Node clustering 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Stevens Institute of TechnologyHobokenUSA
  2. 2.Zurich University of Applied SciencesWinterthurSwitzerland

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