arXiv 2 Nov 2025 · Finance — Risk Management
arXiv:2511.08602 · PDF · DOI · OpenAlex · Extracted main text
The 2008 financial crisis exposed fundamental vulnerabilities in interconnected banking systems, yet existing frameworks fail to integrate spatial propagation with network contagion mechanisms. This paper develops a unified spatial-network framework to analyze systemic risk dynamics, revealing three critical findings that challenge conventional wisdom. First, banking consolidation paradoxically increased systemic fragility: while bank numbers declined 47.3% from 2007 to 2023, network fragility measured by algebraic connectivity rose 315.8%, demonstrating that interconnectedness intensity dominates institutional count. Second, financial contagion propagates globally with negligible spatial decay (boundary d* = 47,474 km), contrasting sharply with localized technology diffusion (d* = 69 km)--a scale difference of 688 times. Third, traditional difference-in-differences methods overestimate crisis impacts by 73.2% when ignoring network structure, producing severely biased policy assessments. Using bilateral exposure data from 156 institutions across 28 countries (2007-2023) and employing spectral analysis of network Laplacian operators combined with spatial difference-in-differences identification, we document that crisis effects amplified over time rather than dissipating, increasing fragility 68.4% above pre-crisis levels with persistent effects through 2023. The consolidation paradox exhibits near-perfect correlation (R = 0.97) between coupling strength and systemic vulnerability, validating theoretical predictions from continuous spatial dynamics. Policy simulations demonstrate network-targeted capital requirements achieve 11.3x amplification effects versus uniform regulations. These findings establish that accurate systemic risk assessment and macroprudential policy design require explicit incorporation of both spatial propagation and network topology.
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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 | Kikuchi, T (2024) Dynamic spatial treatment effect boundaries: A continuous functional framework from Navier-Stokes equations self | 1.000 | 9 | 4 | 100% |
| 2 | Kikuchi, T (2024) Spatial and temporal boundaries in difference-in-differences: A framework from Navier-Stokes equation self | 1.000 | 7 | 3 | 100% |
| 3 | Kikuchi, T (2024) Network contagion dynamics in European banking: A Navier-Stokes framework for systemic risk assessment self | 1.000 | 6 | 3 | 100% |
| 4 | Kikuchi, T (2024) Dual-Channel Technology Diffusion: Spatial Decay and Network Contagion in Supply Chain Networks self | 1.000 | 5 | 3 | 100% |
| 5 | Adrian, T., & Shin, H. S (2010) Liquidity and leverage | 0.644 | 2 | 2 | 100% |
| 6 | Bernanke, B. S (2010) Causes of the recent financial and economic crisis | 0.644 | 2 | 2 | 100% |
| 7 | Fiedler, M (1973) Algebraic connectivity of graphs | 0.644 | 2 | 2 | 100% |
| 8 | Garratt, R., Mahadeva, L., & Svirydzenka, K (2014) Mapping systemic risk in the international banking network | 0.644 | 2 | 2 | 100% |
| 9 | Upper, C., & Worms, A (2004) Estimating bilateral exposures in the German interbank market: Is there a danger of contagion? | 0.644 | 2 | 2 | 100% |
| 10 | Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J (2020) Sampling-based versus design-based uncertainty in regression analysis | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 30 scored citations.