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March 1, 2026Axioms1 citationsOpen Access

A Nonlinear Dynamic Model of Risk Propagation and Optimal Control Strategy in Multilayer Financial Networks

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YDYi DingShandong University of Science and TechnologyYYYue YinPeople’s Hospital of RizhaoCYChun YanShandong University of Science and Technology

Key Points

  • To develop a model for understanding risk propagation and to create an optimal control strategy for financial stability.
  • Proposed a continuous-time dynamic clearing model on multilayer financial networks.
  • Incorporated factors like interbank credit and overlapping portfolios into the model.
  • Developed a model predictive control (MPC) framework for resource allocation.
  • MPC strategy reduces the number of defaulting banks by approximately 56%.
  • MPC outperforms no-intervention and rule-based policies in maintaining financial stability.
  • Rule-based interventions achieve a 48.83% reduction in defaults and a 28.57% improvement in rescue efficiency.

Abstract

This paper proposes a continuous-time dynamic clearing model on a multilayer financial network to study systemic risk propagation and optimal intervention. The model incorporates interbank credit, equity crossholdings, and overlapping portfolios, and models bankruptcy as a jump event triggered by insolvency or illiquidity. Based on the system’s dynamic structure, we develop a model predictive control (MPC) framework that enables forward-looking and flexible allocation of limited bailout resources between debt relief and capital injection. Numerical results show that the proposed MPC strategy substantially outperforms both no-intervention and rule-based policies in terms of financial stability and resource efficiency. Compared with no intervention, the MPC strategy reduces the number of defaulting banks by approximately 56%. In contrast, the simple rule-based intervention achieves a reduction of about 48.83%, while improving rescue efficiency by approximately 28.57%. Overall, the framework provides a unified and effective approach to systemic risk control in financial networks.

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Cite This Study

Ding et al. (2026) studied this question.

synapsesocial.com/papers/69a3d7baec16d51705d2e0dchttps://doi.org/10.3390/axioms15030166
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