1- Faculty of Economics, University of Tehran , sm.shadab1372@gmail.com
2- Faculty of Economics, University of Tehran
2- Faculty of Economics, University of Tehran
Abstract: (12 Views)
Backward-looking approaches to loss recognition are among the main causes of banking crisis. This study, emphasizing the calculation of expected credit portfolio losses, focuses on the implications of credit risk models for banking stability. Given data limitations in Iran, a synthetic dataset consistent with IFRS 9 was generated from existing data. The dataset consists of a credit portfolio with 1,000 loans that were assigned credit ratings based on the empirical frequency distribution, probabilities of default estimated using the beta-binomial distribution, and loan exposures simulated through the truncated Pareto distribution. The generation of standardized synthetic data from available information was based on Monte Carlo simulation with one million iterations. The results indicate that the Vasicek model yields more conservative estimates of expected loss compared with Mixture models, yet its outcomes are more sensitive to changes in default correlation. Credit risk analysts face a trade-off between conservatism and stability. Regulatory focus on setting correlation thresholds can more effectively reduce the likelihood of banking crises and enhance the resilience of the banking system.
Keywords: Credit Risk, International Financial Reporting Standard 9 (IFRS 9), Expected Credit Loss, Banking Stability, Threshold and Hybrid Models
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