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Discovering SIFIs in Interbank Communities.

Nicolò Pecora1, Pablo Rovira Kaltwasser2, Alessandro Spelta3

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This study introduces a new network analysis method using non-negative matrix factorization to find central nodes and communities in directed networks. It identifies systemically important banks and potential contagion areas within the interbank market.

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Area of Science:

  • Network Science
  • Financial Network Analysis
  • Data Mining

Background:

  • Interbank networks are crucial for financial stability.
  • Understanding network structure reveals systemic risk.
  • Existing methods may not fully capture directed, weighted network dynamics.

Purpose of the Study:

  • To develop a novel methodology for community detection and central node identification in directed weighted networks.
  • To apply this method to the interbank market network.
  • To identify systemically important institutions and potential contagion pathways.

Main Methods:

  • Non-negative Matrix Factorization (NMF) for network analysis.
  • Application to directed weighted networks, specifically the e-MID interbank market data.
  • Identification of central nodes and community structures.

Main Results:

  • Successfully detected community structures within the interbank network.
  • Identified central nodes, distinguishing between systemically important borrowers and lenders.
  • Provided insights into potential areas of financial distress contagion.

Conclusions:

  • The proposed NMF-based methodology is effective for analyzing complex financial networks.
  • The findings enhance the understanding of interbank market structure and systemic risk.
  • The technique offers a valuable tool for financial regulators and market participants.