MITIGATING FINANCIAL RISKS IN THE ARAB REGION: A LOGIT REGRESSION APPROACH TO EARLY WARNING OF BANK FAILURE

Abstract

The stability of the financial system is constantly threatened by the potential bank failures, which could lead to negative impacts on economies as a whole. Therefore, the development of effective early warning systems by central banks is essential to mitigate these risks. This study aims to identify financial soundness indicators that can help build an early warning system to predict and prevent bank failures in the Arab region. Logistic regression model was used to analyze data from 60 commercial banks in the Arab region from 2000-2010. The study found that financial soundness indicators such as ROA and CAR had a significant impact in predicting bank failure. Moreover, macro-prudential control was deemed necessary to monitor and reduce risks in the financial system. Various early warning indicators such as Aggregate Micro Prudential Indicators, Heat Maps and stress tests were discussed in terms of their effectiveness in predicting bank failures. The paper recommends that financial stability and banking supervision departments should prioritize the building of early warning systems, using methods such as the logistic regression model and financial stability indicators. Overall, this study provides valuable insights for both households and the corporate sector to anticipate potential crises and predict the performance of the banking sector across Arab countries.