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Systemic cascades on inhomogeneous random financial networks.
1Mathematics and Statistics, McMaster University, 1280 Main St. West, Hamilton, ON L8S 4L8 Canada.
Mathematics and Financial Economics
|February 27, 2023
Summary
This study models financial systems as networks to analyze systemic crises. It introduces a cascade mechanism to understand shock propagation and equilibrium in financial networks.
Area of Science:
- Financial mathematics
- Network science
- Systemic risk analysis
Background:
- Financial systems are complex networks with interconnected institutions.
- Systemic crises can arise from exogenous shocks to financial institutions.
- Existing models often simplify the dynamics of crisis propagation.
Purpose of the Study:
- To present a novel model of the financial system as an inhomogeneous random financial network (IRFN).
- To analyze the propagation and amplification of financial crises using a cascade mechanism.
- To investigate the mathematical properties of a generalized solvency cascade mechanism.
Main Methods:
- Modeling the financial system as an inhomogeneous random financial network (IRFN) with N nodes and directed weighted edges.
- Utilizing a cascade mechanism to track shock propagation and amplification.
- Generalizing the Eisenberg-Noe solvency cascade mechanism to include fractional bankruptcy charges.
Main Results:
- Verification of a "tree independent cascade property" in the solvency cascade mechanism.
- Development of an explicit recursive stochastic solvency cascade mapping for a large number of banks (N).
- Numerical computation of the cascade mapping to visualize systemic crisis evolution.
Conclusions:
- The proposed model provides a detailed framework for understanding systemic crises in financial networks.
- The generalized cascade mechanism offers new insights into shock propagation and financial stability.
- Numerical computation of the cascade mapping allows for the study of crisis dynamics towards equilibrium.
Keywords:
Banking networkCascadeFunding liquidityInsolvencyInterbank exposureLocally tree-likeRandom financial networkSystemic riskMore Related Videos
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