Forecasting Financial Stability in the National Banking System Using the CLASS Model

This study aims to evaluate and forecast financial stability in Iran's banking system using the CLASS model, focusing on banks listed on the Tehran Stock Exchange. The research adopts a descriptive-analytical and correlational methodology, which is both applied and comparative in its approach. Given the retrospective nature of the study, which relies on historical financial data recorded in the banks' financial statements, the research employs correlational, descriptive, and causal-comparative methods.
For data analysis, research data was first extracted from document reviews and organized into a comprehensive information table. Subsequently, a capital stress test and bank loss analysis were conducted under the CLASS model. The statistical population includes all banks with available data on the Tehran Stock Exchange between 2009 and 2020. This study, situated in the financial domain, utilizes the CLASS model for assessing bank capital and losses. Additionally, time series equations and autoregressive models were used to compare different forecasting models within CLASS, estimating the forecasting power of the CLASS model against actual economic conditions during crises.
The findings reveal that the total capital shortfall in the banking system began to increase in the years preceding the financial crisis, peaking in the final quarter of 2019 under macroeconomic stress conditions. The results also indicate that the CLASS model provides an accurate forecast of financial stability in the country's banking system. These findings are interpreted as evidence of improved banking system resilience. Moreover, the stress test is presented as a valuable example of a macroprudential policy tool.