Modeling and Mathematical Analysis of Liquidity Risk Contagion in the Banking System Using an Optimal Control Approach
The study of contagion dynamics is a well-established domain within epidemiology, where the spread of infectious diseases is modeled and analyzed. In recent years, similar methodologies have been applied to the financial sector to understand and predict the propagation of risks within banking system...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | AppliedMath |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-9909/5/1/20 |
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| Summary: | The study of contagion dynamics is a well-established domain within epidemiology, where the spread of infectious diseases is modeled and analyzed. In recent years, similar methodologies have been applied to the financial sector to understand and predict the propagation of risks within banking systems better. This paper examines the application of contagion models to assessing liquidity risk in the banking sector, leveraging optimal control theory to evaluate potential interventions by central banks. Using data from the largest European banks, we simulate the impact of central bank measures on liquidity risk. By employing optimal control techniques, we construct a model capable of simulating various scenarios to evaluate the effectiveness of policy interventions in mitigating financial contagion. Our approach provides a robust framework for analyzing the systemic risk propagation within the banking network, offering qualitative insights into the contagion mechanisms and their implications for the financial and macroeconomic landscape. The model simulates three distinct scenarios, with each representing varying levels of intervention and market conditions. The results demonstrate the model’s ability to capture the intricate interactions among major European banks, reflecting the complex realities of the financial system. These findings emphasize the critical role of central bank policies in maintaining financial stability and underscore the necessity of coordinated international efforts to manage systemic risks. This analysis contributes to a broader understanding of financial contagion, offering valuable insights for policymakers and financial institutions aiming to strengthen their resilience against future crises. The data used for the parameters are historical, which may not reflect recent changes in the banking system. The model could also be improved by incorporating non-financial factors, such as the behaviors of market actors. For future research, several improvements are possible. One improvement would be to make the bank interactions more dynamic to reflect rapid market changes better. It would also be interesting to add financial crisis scenarios to test the system’s resilience. Using more up-to-date data and incorporating new regulations would help refine the model. Finally, it would be relevant to examine the impact of external events, such as geopolitical crises, on the propagation of systemic risk. In conclusion, while the model is useful, there are several avenues for improving it and making it more suitable for our current realities. |
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| ISSN: | 2673-9909 |