Universal Cybernetic Risk Framework: Closed-Loop Dynamic Stabilization of Systemic Contagion in Interconnected Financial Networks

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Title

Universal Cybernetic Risk Framework: Closed-Loop Dynamic Stabilization of Systemic Contagion in Interconnected Financial Networks

Authors

LANZ CWJ CHAN
Finamatrix Quant-Lab, Singapore
Shin-Hung Pan
Department of Information Management,
Chaoyang University of Technology, Taichung, Taiwan
WING-KEUNG WONG
Department of Finance, Quantum AI Research Center, Fintech & Blockchain Research Center, and Big Data Research Center, Asia University, Taiwan
Department of Medical Research, China Medical University Hospital, Taiwan
Business, Economic and Public Policy Research Centre, Hong Kong Shue Yan University
The Economic Growth Centre, Nanyang Technological University, Singapore

Abstract

Modern financial markets operate as hyper-connected, non-linear dynamical networks where micro-level autonomous de-risking and algorithmic liquidation cascades generate catastrophic macro-prudential instabilities. Prevailing systemic risk frameworks—such as Value-at-Risk (VaR), Expected Shortfall (ES), and static DebtRank algorithms—function as open-loop, passive observational diagnostics that exhibit severe phase-lag errors during non-equilibrium liquidation spirals. This paper introduces the Universal Cybernetic Risk Framework (UCRF), transforming systemic risk mitigation from an observational metric into an active, closed-loop control architecture tailored for Central Counterparty Clearinghouses (CCPs) and central bank emergency liquidity facilities. We formulate a fully coupled 3N-dimensional state-space dynamic model capturing the non-linear interplay among balance-sheet equity, unencumbered funding liquidity, and endogenous fire-sale price impacts. By treating bilateral default cascades as sector-bounded non-linearities within a Lur’e system formulation, we establish rigorous network stabilization criteria using Lur’e-Postnikov Lyapunov functions and Linear Matrix Inequalities (LMIs). Furthermore, we prove formal Input-to-State Stability (ISS), analytically deriving the compact invariant attractor bounds that guarantee bounded systemic drawdowns under worst-case exogenous shocks. Crucially, UCRF incorporates actuator saturation and finite interventional budget constraints, resolving the unrealistic assumption of unconstrained liquidity bailouts. Multi-node stress-testing under a dual-time-scale simulation framework demonstrates that UCRF dynamically arrests contagion propagation and curtails systemic drawdowns substantially more effectively than conventional macroprudential overlays and equal-budget uncoordinated interventions, establishing an operational, mathematically grounded paradigm for safeguarding interconnected financial systems.

Keywords

Systemic Risk; Closed-Loop Control Systems; Financial Networks; Lur’e Systems; Linear Matrix Inequalities (LMI); Input-to-State Stability (ISS); Liquidity Contagion; Fire Sales

Classification-JEL

C61, C62, D85, G17, G28

Pages

39-61

How to Cite

Chan, L. C., Pan, S. H., & Wong, W.-K. (2026). Universal Cybernetic Risk Framework: Closed-Loop Dynamic Stabilization of Systemic Contagion in Interconnected Financial Networks. International Journal of Wealth Management, 1, 39-61.