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

TL;DR

This paper constructs a detailed multilayer network model of euro area banks, integrating diverse data sources to improve systemic risk assessment by capturing multiple contagion channels and revealing the importance of layer-specific analysis.

Contribution

It develops an empirically grounded multilayer network model that combines various exposure types and balance-sheet data, enhancing the understanding of systemic risk in banking networks.

Findings

Cross-layer heterogeneity affects systemic importance

Aggregated networks can misidentify key institutions

Layer-specific analysis improves risk assessment

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.