Integrating granular data into a multilayer network: an interbank model of the euro area for systemic risk assessment

2026-02-11

Advances in Data Analysis and Classification

AI Summary

Existing systemic risk models struggle to integrate multi-source, heterogeneous, fine-grained banking data and often rely on oversimplified or single-channel network representations that fail to capture the complexity of multi-channel risk contagion mechanisms. This study proposes a novel multilayer network model of systemically important banks in the euro area, constructed from real regulatory and statistical data, where each layer corresponds to a distinct risk transmission channel—such as interbank lending, securities holdings, and short-term funding—and features consistent exposure matrices across layers over a unified set of nodes. Through multilayer network modeling, entity alignment, and microsimulation, the analysis reveals significant heterogeneity across layers in connectivity and centrality, demonstrating that aggregated networks can misidentify systemically important institutions. This work establishes a new paradigm for layered, data-driven systemic risk assessment.

Technology Category

• Multiagent Systems: Multiagent Systems under Uncertainty
• Machine Learning: Multi-instance/Multi-view Learning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Micro-structural models of contagion and systemic risk emphasize that shock propagation is inherently multi-channel, spanning counterparty exposures, short-term funding and roll-over risk, securities cross-holdings, and common-asset (fire-sale) spillovers. Empirical implementations, however, often rely on stylized or simulated networks, or focus on a single exposure dimension, reflecting the practical difficulty of reconciling heterogeneous granular collections into a coherent representation with consistent identifiers and consolidation rules. We close part of this gap by constructing an empirically grounded multilayer network for euro area significant banking groups that integrates several supervisory and statistical datasets into layer-consistent exposure matrices defined on a common node set. Each layer corresponds to a distinct transmission channel, long- and short-term credit, securities cross-holdings, short-term secured funding, and overlapping external portfolios, and nodes are enriched with balance-sheet information to support model calibration. We document pronounced cross-layer heterogeneity in connectivity and centrality, and show that an aggregated (flattened) representation can mask economically relevant structure and misidentify the institutions that are systemically important in specific markets. We then illustrate how the resulting network disciplines standard systemic-risk analytics by implementing a centrality-based propagation measure and a micro-structural agent-based framework on real exposures. The approach provides a data-grounded basis for layer-aware systemic-risk assessment and stress testing across multiple dimensions of the banking network.

Problem

• systemic risk
• multilayer network
• granular data
• interbank exposures
• financial contagion

Innovation

• multilayer network
• systemic risk
• granular data integration
• contagion channels
• empirical banking network