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Reconstructing the topology of financial networks from degree distributions and reciprocity

Author

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  • Engel, Janina
  • Pagano, Andrea
  • Scherer, Matthias

Abstract

A flexible probabilistic approach for the constructing of realistic topologies of interbank networks is presented. This constitutes a challenging task, since information on bilateral inter-banking activities is classified confidential and the number of banks in most European countries is substantial. First, we analyze what information on European inter-banking liabilities is publicly available. Second, we present an approach for the reconstruction of network topologies satisfying known characteristics through an exponential random graph model (ERGM), which incorporates the available information as side conditions. Third, we conduct a case study calibrating the model to the Italian and the German interbank market. Samples of both models are then analyzed with respect to different network statistics. The relevance of the presented results stems from the urgent need of having realistic instances of possible adjacency matrices as input in technical studies on the stability of inter-banking networks. Various such studies exist, however, most of them rely on toy models for the analyzed adjacency matrices.

Suggested Citation

  • Engel, Janina & Pagano, Andrea & Scherer, Matthias, 2019. "Reconstructing the topology of financial networks from degree distributions and reciprocity," Journal of Multivariate Analysis, Elsevier, vol. 172(C), pages 210-222.
    • Handle: RePEc:eee:jmvana:v:172:y:2019:i:c:p:210-222
    DOI: 10.1016/j.jmva.2019.01.008
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    Citations

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    Cited by:

    1. Valentina Macchiati & Piero Mazzarisi & Diego Garlaschelli, 2024. "Interbank network reconstruction enforcing density and reciprocity," Papers 2402.11136, arXiv.org.
    2. Liu, Siyuan & Zhang, Chunyan & Li, Kun & Zhang, Jianlei, 2022. "Exploring the inducement for social dilemma and cooperation promotion mechanisms in structured populations," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    3. Ma, Ding & Yu, Qian & Li, Jing & Ge, Mengni, 2021. "Innovation diffusion enabler or barrier: An investigation of international patenting based on temporal exponential random graph models," Technology in Society, Elsevier, vol. 64(C).

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