Developing a Credit Risk Stress-Testing Model under Alternative Scenarios

Sadegh Adelnia; Seyyed Abbas Borhani; Seyyed Kazem Chavoshi; Mojgan Safa

Authors

Sadegh Adelnia Department of Accounting and Finance, Qo.C., Islamic Azad University, Qom, Iran Author https://orcid.org/0009-0001-3394-445X
Seyyed Abbas Borhani Department of Accounting, Qo.C., Islamic Azad University, Qom, Iran https://orcid.org/0000-0002-6790-3410
Seyyed Kazem Chavoshi Department of Business Management, Faculty of Management, Kharazmi University, Tehran, Iran Author https://orcid.org/0000-0002-7852-0132
Mojgan Safa Department of Accounting, Qo.C., Islamic Azad University, Qom, Iran Author https://orcid.org/0000-0002-7245-8753

Keywords:

financial crisis, macroeconomics, credit risk

Abstract

Banks, as a major component of the financial system, play a critical role in financing the production, commercial, consumer, and even governmental sectors. The banking industry is of considerable importance in the Iranian economy because, due to the insufficient development of the capital market, the money market—operating primarily through the banking system—has effectively assumed responsibility for providing both long-term and short-term financing to firms. Credit risk arises when borrowers, either intentionally or unintentionally, are unable to repay their loan installments and other financial obligations to banks. The level of credit risk in Iran is considered critical when compared with the global average. The objective of the present study was to develop a credit risk stress-testing model under alternative scenarios for banks. EViews Version 10 was employed to conduct the econometric analyses. One of the most important findings of the study was that the coefficient of determination (R²) for the GARCH model examining fluctuations between the study’s target variable and the economic growth rate, interest rate, unemployment rate, inflation rate, and per capita income growth rate was calculated as 0.978. Furthermore, the R² for the GARCH model incorporating the liquidity growth rate, oil revenue growth rate, bank deposit rate, and exchange rate was calculated as 0.891. These results indicate the very high predictive power of the econometric models developed in this study.

References

[1] A. Kanas, D. Vasiliou, and N. Eriotis, "Credit risk and profitability in banking: Evidence from international banks," Research in International Business and Finance, vol. 72, p. 102260, 2025, doi: 10.1016/j.ribaf.2024.102260.

[2] K. Ahmadi and M. Kazemi, "Analysis of the Effect of Financing Structure and Credit Risk on the Performance of Iranian Banks," Islamic Banking Studies Quarterly, vol. 18, no. 1, pp. 55-78, 2025.

[3] B. Komani, "The Impact of Corporate Governance on Credit Risk and Capital Adequacy in Iranian Private Banks," in Sixth International Conference on Political Science, Management, Economics, and Accounting, Hamedan, Iran, 2025.

[4] M. Hazrati and M. Razmjouei, "The Role of Credit Risk in the Relationship between Corporate Governance and Financial Performance," in 21st International Conference on Modern Research in Management, Economics, Accounting, and Banking, 2025.

[5] S. F. Aniran, S. A. Nabavi Chashmi, and A. Sorayyaei, "Investigating Credit Risk Assessment Using Effective Indicators on Estimating the Relationship Between Financial Development and Economic Growth - Markov Switching Approach," Investment Knowledge Scientific Research Quarterly, vol. 13, no. 3, pp. 55-78, 2025.

[6] D. K. Nguyen and N. Paltalidis, "Credit and financial cycle synchronization impact on sovereign credit risk," Finance Research Letters, vol. 86, no. Part A, p. 108236, 2025, doi: 10.1016/j.frl.2025.108236.

[7] C. Di Tommaso, M. Foglia, and V. Pacelli, "The Impact and the Contagion Effect of Natural Disasters on Sovereign Credit Risk: An Empirical Investigation," International Review of Financial Analysis, vol. 111, p. 105102, 2026, doi: 10.1016/j.irfa.2026.105102.

[8] V. Mahadevan, S. Subramaniam, and V. Srivastava, "Carbon Concentration in Bank Portfolios and Efficiency: The Role of Credit Risk and Capitalization," Journal of International Financial Markets, Institutions and Money, vol. 109, p. 102327, 2026, doi: 10.1016/j.intfin.2026.102327.

[9] J. H. Plikas and D. Kenourgios, "Renewable Energy and Bank Credit Risk: The Mediating Role of Economic Growth," Economic Modelling, p. 107576, 2026, doi: 10.1016/j.econmod.2026.107576.

[10] M. Rowshan, B. Khodarahmi, and F. Sarraf, "Measuring Credit Risk and Capital Adequacy with Attention to the Size and Ownership Structure of Listed Iranian Companies Based on the Generalized Method of Moments (GMM) Model," Scientific-Research Quarterly of Accounting and Auditing Knowledge, vol. 14, no. 2, Serial 54, pp. 313-329, 2025.

[11] A. Yāvarī, H. Jabbārī, and H. Panāhiyān, "Designing a Credit Risk Management Model with a Pathological Approach to Guarantees and Collaterals of Bank Facilities," Technology in Entrepreneurship and Strategic Management Quarterly, vol. 4, no. 2, pp. 1-22, 2025, doi: 10.61838/kman.jtesm.4.2.11.

[12] Tafreshi, "Credit risk assessment using machine learning algorithms: Logistic regression, artificial neural network, and support vector machine," Biannual Journal of Financial Knowledge of Securities Analysis, vol. 18, no. 65, pp. 45-62, 2025.

[13] I. H. Sugozu, C. Verberi, and S. Yasar, "Machine Learning Approaches to Credit Risk: Evaluating Turkish Participation and Conventional Banks," Borsa Istanbul Review, vol. 25, no. 3, pp. 497-512, 2025, doi: 10.1016/j.bir.2025.02.001.

[14] A. Paz et al., "Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review," Biomimetics, vol. 10, no. 5, p. 326, 2025, doi: 10.3390/biomimetics10050326.

[15] M. R. Machado, D. T. Chen, and J. R. Osterrieder, "An Analytical Approach to Credit Risk Assessment Using Machine Learning Models," Decision Analytics Journal, vol. 16, p. 100605, 2025, doi: 10.1016/j.dajour.2025.100605.

[16] Q. Peng, Y. Zhu, and G. J. Wang, "SCF Credit Risk Assessment with Limited Labeled Data Using Label Propagation Algorithm and Complex Network Approaches," International Review of Financial Analysis, vol. 107, p. 104619, 2025, doi: 10.1016/j.irfa.2025.104619.

[17] Y. Li, R. Zhao, and Y. An, "A Credit Risk Prediction Model Combining Ensemble Learning and Deep Learning Through Three-Way Decisions," Journal of Big Data, vol. 13, p. 44, 2026, doi: 10.1186/s40537-026-00944.

[18] E. Akhmetshin, I. Abdullayev, S. Makhmudov, E. Klochko, and M. Boltaeva, "An Advancing Financial Credit Risk Forecasting Model Using Graph Convolutional Networks for Sustainable Economic Analysis," Engineering, Technology & Applied Science Research, vol. 16, no. 1, pp. 30948-30953, 2026, doi: 10.48084/etasr.15057.

[19] M. Gramifar, G. Kabir, and S. A. Khan, "Credit Risk Assessment Using Bayesian Networks and Machine Learning Approaches," Advanced Engineering Informatics, vol. 74, no. Part A, p. 104638, 2026, doi: 10.1016/j.aei.2026.104638.

[20] S. Lin, X. Lin, and X. J. He, "Credit Risk Identification with Hawkes Processes: Theory and Evidence," The Quarterly Review of Economics and Finance, vol. 103, p. 102027, 2025, doi: 10.1016/j.qref.2025.102027.

[21] N. Arora and P. D. Kaur, "The Confluence of Artificial Intelligence and Credit Risk Assessment: A Scientometric Analysis and Research Frontiers," Technology in Society, vol. 87, p. 103341, 2026, doi: 10.1016/j.techsoc.2026.103341.

[22] M. Golec and M. Alabduljalil, "Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy," Expert Systems with Applications, vol. 306, p. 130941, 2026, doi: 10.1016/j.eswa.2025.130941.

[23] J. Yu, "The Superiority of Local ESG Ratings in China’s Credit Risk Assessment: An Empirical Study Based on Default Distance," Journal of World Economy, vol. 3, no. 4, pp. 79-84, 2024, doi: 10.56397/jwe.2024.12.09.