arXiv 22 Oct 2025 · Econometrics
arXiv:2510.19630 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a continuous functional framework for analyzing contagion dynamics in financial networks, extending the Navier-Stokes-based approach to network-structured spatial processes. We model financial distress propagation as a diffusion process on weighted networks, deriving a network diffusion equation from first principles that predicts contagion decay depends on the network's algebraic connectivity through the relation $κ= \sqrt{λ_2/D}$, where $λ_2$ is the second-smallest eigenvalue of the graph Laplacian and $D$ is the diffusion coefficient. Applying this framework to European banking data from the EBA stress tests (2018, 2021, 2023), we estimate interbank exposure networks using maximum entropy methods and track the evolution of systemic risk through the COVID-19 crisis. Our key finding is that network connectivity declined by 45% from 2018 to 2023, implying a 26% reduction in the contagion decay parameter. Difference-in-differences analysis reveals this structural change was driven by regulatory-induced deleveraging of systemically important banks, which experienced differential asset reductions of 17% relative to smaller institutions. The networks exhibit lognormal rather than scale-free degree distributions, suggesting greater resilience than previously assumed in the literature. Extensive robustness checks across parametric and non-parametric estimation methods confirm declining systemic risk, with cross-method correlations exceeding 0.95. These findings demonstrate that post-COVID-19 regulatory reforms effectively reduced network interconnectedness and systemic vulnerability in the European banking system.
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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 | Upper, C (2011) Simulation methods to assess the danger of contagion in interbank markets | 1.000 | 6 | 5 | 100% |
| 2 | Kikuchi, T (2024) Dynamic spatial treatment effect boundaries: A continuous functional framework from Navier-Stokes equations self | 1.000 | 6 | 3 | 100% |
| 3 | Anand, K., Craig, B., and Von Peter, G (2018) Filling in the blanks: Network structure and interbank contagion | 1.000 | 5 | 4 | 100% |
| 4 | Boss, M., Elsinger, H., Summer, M., and Thurner, S (2004) Network topology of the interbank market | 0.928 | 4 | 4 | 100% |
| 5 | Acemoglu, D., Ozdaglar, A., and Tahbaz-Salehi, A (2015) Systemic risk and stability in financial networks | 0.843 | 3 | 3 | 100% |
| 6 | Gai, P. and Kapadia, S (2010) Contagion in financial networks | 0.843 | 3 | 3 | 100% |
| 7 | Kikuchi, T (2024) Emergent dynamical spatial boundaries in emergency medical services: A Navier-Stokes framework from first principles self | 0.737 | 3 | 2 | 100% |
| 8 | Kikuchi, T (2024) A unified framework for spatial and temporal treatment effect boundaries: Theory and identification self | 0.737 | 3 | 2 | 100% |
| 9 | Allen, F. and Gale, D (2000) Financial contagion | 0.644 | 2 | 2 | 100% |
| 10 | Barabási, A.-L. and Albert, R (1999) Emergence of scaling in random networks | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 30 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Dynamic Spatial Treatment Effects and Network Fragility: Theory and Evidence from the 2008 Financial Crisis | 1.000 | 6 | 3 |
| 2 | Dual-Channel Technology Diffusion: Spatial Decay and Network Contagion in Supply Chain Networks | 0.874 | 16 | 2 |