Ilias Aarab, Thomas Gottron, Andrea Colombo, Jörg Reddig, Annalauro Ianiro
arXiv 11 Feb 2026 · Finance — Statistical Finance · publishedAdvances in Data Analysis and Classification (2026)
arXiv:2602.10960 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bargigli, Leonardo and Di Iasio, Giovanni and Infante, Luigi and Lil… (2015) The multiplex structure of interbank networks | 1.000 | 5 | 3 | 100% |
| 2 | Montagna, Mattia and Kok, Christoffer Multi-layered interbank model for assessing systemic risk | 0.941 | 6 | 5 | 83% |
| 3 | Battiston, Federico and Nicosia, Vincenzo and Latora, Vito (2014) Structural measures for multiplex networks | 0.843 | 3 | 3 | 100% |
| 4 | Boss, Michael and Elsinger, Helmut and Summer, Martin and Thurner 4,… (2004) Network topology of the interbank market | 0.843 | 3 | 3 | 100% |
| 5 | Glasserman, Paul and Young, H Peyton (2015) How likely is contagion in financial networks? | 0.811 | 4 | 2 | 100% |
| 6 | Battiston, Stefano and Puliga, Michelangelo and Kaushik, Rahul and T… (2012) DebtRank: Too central to fail? financial networks, the FED and systemic risk | 0.737 | 3 | 2 | 100% |
| 7 | Elliott, Matthew and Golub, Benjamin and Jackson, Matthew O (2014) Financial networks and contagion | 0.737 | 3 | 2 | 100% |
| 8 | Aarab, Ilias and Gottron, Thomas (2024) Network Topology of the Euro Area Interbank Market self | 0.644 | 2 | 2 | 100% |
| 9 | Allen, Franklin and Gale, Douglas (2000) Financial contagion | 0.644 | 2 | 2 | 100% |
| 10 | Cifuentes, Rodrigo and Ferrucci, Gianluigi and Shin, Hyun Song (2005) Liquidity risk and contagion | 0.644 | 2 | 2 | 100% |
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