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Credit Risk Modeling: Evidence from the EMV Model Applied to the Autonomous Region of Madeira (2019–2025)

Sérgio Jesus Teixeira

Abstract

This study aims to analyze the evolution and determinants of credit risk in the Autonomous Region of Madeira (RAM) between 2019 and 2025, applying the EMV (Exogenous–Maturity– Vintage) model and robust econometric techniques. The research is based on quarterly data on loans, default rates, and deposits from financial institutions in the region, and compares them with the Portuguese national context. The EMV model allows us to decompose the default rate into three main components: (i) exogenous effects associated with the economic cycle, (ii) maturity effects related to the aging of credit portfolios, and (iii) generation effects (vintage), which capture differences between lending periods. In addition, RLM-Huber models (robust linear regression), quantile regressions, and the Theil–Sen estimator are applied to estimate trends and assess the persistence of defaults. The results reveal that the nonperforming loan ratio exhibits strong temporal inertia, a positive impact of credit growth on delinquency, and a negative correlation between deposits and credit risk, suggesting that savings play a stabilizing role. The conclusion is that the RAM maintains credit quality standards similar to those of the national context, but is more sensitive to economic shocks due to its regional production structure and high dependence on the tertiary sector.

Keywords

Autonomous Region of MadeiraCredit RiskEMV ModelDefaultRegression

References

Banco de Portugal. (2025). Relatório de estabilidade financeira 2025. Lisboa: Banco de Portugal. Bank for International Settlements (BIS). (2021). Annual economic report 2021. Basel: BIS. Basel Committee on Banking Supervision (BCBS). (2020). Credit risk modeling practices and applications. Bank for International Settlements. Borges, R., & Machado, J. (2021). Aplicação do modelo EMV ao crédito hipotecário em Portugal. Revista de Estudos Económicos e Financeiros, 12(2), 45–67. Briguglio, L., Cordina, G., Farrugia, N., & Vella, S. (2009). Economic vulnerability and resilience: Concepts and measurements. Oxford Development Studies, 37(3), 229–247. Brownlees, C., & Engle, R. (2017). SRISK: A conditional capital shortfall measure of systemic risk. The Review of Financial Studies, 30(1), 48–79. Burian, D., & Panagiotidis, T. (2018). Credit default dynamics: Vintage decomposition and macro linkages (Working Paper Series). University of Edinburgh Business School. Direção Regional de Estatística da Madeira (DREM). (2025). Indicadores de crédito e depósitos regionais 2024–2025. Funchal: Governo Regional da Madeira. Drehmann, M., & Juselius, M. (2014). Evaluating early warning indicators of banking crises: Satisfying policy requirements. International Journal of Forecasting, 30(3), 759–780. Edge, R. M., & Meisenzahl, R. R. (2011). The unreliability of credit-to-GDP ratio gaps in real time: Implications for countercyclical capital buffers. International Journal of Central Banking, 7(4), 261–298. European Central Bank (ECB). (2023). Financial stability review: November 2023. Frankfurt am Main: ECB. Forster, J. J., & Sudjianto, A. (2013). Exogenous–Maturity–Vintage (EMV) models for credit risk and stress testing (arXiv preprint arXiv:1312.2035). Glenn, N. D. (2005). Cohort analysis (2nd ed.). Sage Publications. Hao, L., & Naiman, D. Q. (2007). Quantile regression. Sage Publications. Huber, P. J. (2011). Robust statistics (2nd ed.). Wiley. International Monetary Fund (IMF). (2021). World Economic Outlook: Managing Divergent Recoveries. Washington, DC: IMF. International Monetary Fund (IMF). (2022). Global financial stability report: Navigating the high-inflation environment. Washington, DC: IMF. Koenker, R. (2005). Quantile regression. Cambridge University Press. Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33–50. Mason, K. O., Mason, W. M., Winsborough, H. H., & Poole, W. K. (1973). Some methodological issues in cohort analysis of archival data. American Sociological Review, 38(2), 242–258. Thomas, L. C., Crook, J. N., & Edelman, D. B. (2017). Credit scoring and its applications (2nd ed.). SIAM. White, H. (2014). Asymptotic theory for econometricians. Academic Press. World Bank. (2021). Global economic prospects: The global economy in transition. Washington, DC: World Bank. Yu, K., Lu, Z., & Stander, J. (2003). Quantile regression: Applications and current research areas. The Statistician, 52(3), 331–350. Zhu, S., Chatterjee, S., & Hossain, M. (2023). Machine learning applications in credit risk modeling: A review. Journal of Risk and Financial Management, 16(5), 230.

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