Technology Adoption and Network Externalities in Financial Systems: A Spatial-Network Approach

Battle Count: 79

The paper focuses on technology adoption dynamics and policy design in financial infrastructure rather than trading strategies or market microstructure. While the network centrality and amplification factor concepts could inform understanding of how fintech adoption affects market structure, there is no direct application to trading signals, portfolio construction, or execution strategies. The Lévy jump-diffusion framework shares mathematical structure with jump-diffusion models used in derivatives pricing, but the application domain is fundamentally different.

The theoretical framework requires expertise in PDEs, stochastic processes (Feynman-Kac, Lévy processes), network theory, and variational calculus. The Lévy jump-diffusion extension with state-dependent intensity is mathematically sophisticated. Empirical implementation requires network data (BIS exposures), geographic coordinates, and adoption timing data. Monte Carlo simulation of the discrete network formulation is tractable but the full continuous PDE solution requires numerical methods. The nesting relationships with canonical models add conceptual complexity.

The paper provides detailed mathematical derivations, Monte Carlo simulation parameters (N=30-40, specific ν_s, ν_n, λ, κ, τ* values), and empirical specification. However, the SWIFT gpi adoption data and BIS bilateral exposure data are not publicly available. No code or repository is provided. The theoretical framework is fully specified but empirical replication requires proprietary data.