Modeling and stability analysis of liquidity risk contagion in the banking system with time delay

Abstract

The modeling of contagious spread is a well-established concept in the field of infectious diseases. However, its recent application to the banking system has opened up new avenues for analysis. The theory of delayed differential systems constitutes an essential mathematical tool for addressing this issue. In this study, we specifically focus on liquidity risk within the banking system and investigate the global stability with and without this delayed risk. We formulate a model to examine the potential impact of central bank interventions on the economy, utilizing simulated data from the largest European banks. The methodology emphasizes the importance of research results and specifies key variables or factors considered in the model. Study objectives focus on the modeling and analysis of the stability of liquidity risk contagion in the banking system, with a particular emphasis on the temporal dimension. The connection between modeling contagious spread in infectious diseases and its recent application in the banking system is well-established. To reach these conclusions, the study employed a rigorous methodology, integrating advanced mathematical models and in-depth statistical analysis. This methodological approach led to significant findings that shed new light on the dynamics of liquidity risk contagion in the financial context. The practical implications of these results are crucial for various stakeholders. Risk managers within financial institutions can utilize our findings to identify potential vulnerabilities and implement more effective risk management strategies. Policymakers and regulators can use our results to shape monetary and macroprudential policies aimed at stabilizing the financial system. The study’s global stability perspectives also provide a basis for improving crisis management practices, ensuring that institutions are better prepared to handle liquidity shocks. Additionally, integrating advanced modeling techniques encourages innovation in financial risk management, equipping institutions with enhanced predictive and responsive capabilities. By specifying key variables such as asset liquidity, interest rates, and other relevant factors, the model provides a solid foundation for a more comprehensive understanding of the underlying mechanisms of liquidity risk contagion in the banking system.