End-to-End Python implementation of Feng's (2026) scalable systemic-risk analytics engine. Implements: exact rank-K reduction of finite-N interbank networks to macroscopic feedback ODEs, directed graphon limits with Wasserstein stability bounds, and indicator-loss well-posedness theory for scalable, provably convergent, auditable risk analytics.
python reproducible-research jupyter-notebook network-science stress-testing monte-carlo-simulation dynamical-systems mathematical-finance numerical-analysis singular-value-decomposition low-rank-approximation financial-contagion systemic-risk early-warning-indicators wasserstein-distance measure-theoretic-probability financial-networks graphon-theory empirical-process-theory sovereign-exposure
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Updated
Aug 8, 2026 - Jupyter Notebook