Understanding Bank Failures: The Crucial Role of Macroeconomic Variables

This article provides an in-depth analysis of the subprime crisis, which revealed the crucial importance of interactions between macroeconomic variables and the stability of the global banking system. This work examines the triggering factors of systemic risk based on macroeconomic data covering the period from 2001 to 2013. Using advanced statistical tools, we identify key variables that contributed to the cascade of bank failures, including interest rates, the market volatility index (VIX), and international exchange rates. The results underscore the need for proactive regulation to prevent future crises. The central issue of this article lies in the importance of integrating the monitoring of macroeconomic variables, such as the Fed rate, real GDP, and VIX, which can serve as early indicators of systemic risks. To address this question, we employed stationarity tests (ADF, Phillips-Perron) to identify non-stationary time series. Subsequently, we applied a VAR model and cointegration analysis to explore long-term relationships between these variables. We implemented a VAR-ECM model to examine long-term relationships and short-term adjustments among the variables.Thismethodological approach is relevant for understanding how shocks affect these variables over time. Proposing an improved version of bank failure prediction models to banks is essential for enhancing the resilience of the banking sector against potential economic crises. By integrating modernanalytical techniques, particularly through machine learning tools in Python, and adapting these models to the specific realities of each institution, it is possible to significantly improve risk management and overall bank performance.