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Sánchez-Serna, A., Camacho-Zabala, E.-A., Carvajal-Sandoval, A.-R. ., & Rueda-Varon, M.-J. . (2025). Calculation of Expected Credit Losses in Uncertain Scenarios for the Real Sector. Revista Finanzas Y Política Económica, 17, 1–26. https://doi.org/10.14718/revfinanzpolitecon.v17.2025.12
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Abstract

The objective of this article is to analyze the effects on the calculation of expected losses due to the impact of covid-19 on the credit risk impairment model according to IFRS 9, for financial assets valued at amortized cost by companies in the real sector. The model, based on the Monte Carlo and International Scoring, Fair Isaac and Company (FICO) methodologies, is applicable to any region. A credit risk rating was obtained for each sector by comparing actual financial information with estimates. The deviation between the probability of risk calculated without the pandemic effect and the actual post-pandemic results was analyzed. The results showed adverse effects on the risk rating for some sectors.
The implications of the study guide the formulation of risk management policies, the adaptation of accounting practices in crisis contexts, and the development of predictive models for future studies and analysis of disruptive events.

References

Alsuwailem, A. A., Salem, E., Saudagar, A. K. J., AlTameem, A., AlKhathami, M., Khan, M. B. & Hasanat, M. H. A. (2022). Impacts of covid-19 on the food supply chain: a case study on Saudi Arabia. Sustainability, 14(1), 254. https://doi.org/10.3390/su14010254

Altman, E. I. (1968). Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x

Altman, E. I., Haldeman, R. G. & Narayanan, P. (1977). ZETATM analysis. A new model to identify bankruptcy risk of corporations. Journal of Banking & Finance, 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6

Ballesteros-Bejarano, J., González-Calzadilla, A. C., Ramón-Jerónimo, J. M. & Flórez-López, R. (2022). Impact of covid-19 on the internationalisation of the Spanish agri-food sector. Journal Foods, 11(7), 938. https://doi.org/10.3390/foods11070938

Bank for International Settlements (BIS). (2015, 18 de diciembre). Guidance on credit risk and accounting for expected credit losses. https://www.bis.org/bcbs/publ/d350.htm

Beerbaum, D. (2020). Accounting treatment of credit loss allowances amid covid-19: Current Expected Credit Loss (CECL) versus IFRS 9 Expected Credit Loss (ECL). Journal of Applied Research in the Digital Economy (JADE), special issue on covid-19, June 2020. http://dx.doi.org/10.2139/ssrn.3824287

Black, F. & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637-654. https://www.jstor.org/stable/1831029

Caniato, F., Gelsomino, L. M., Perego, A. & Ronchi, S. (2016). Does finance solve the supply chain financing problem? Supply Chain Management and International Journal, 21(5), 534-549. https://doi.org/10.1108/SCM-11-2015-0436

Delgado-Vaquero, D., Moralez-Díaz, J. & Zamora-Ramírez, C., (2020). IFRS 9 expected loss. A model proposal for estimating the probability of default for non-rated companies. Spanish Accounting Review, 23(2), 180-196. https://digitum.um.es/digitum/handle/10201/94542

Deloitte. (2013, marzo). Going up? The impact of impairment proposals on regulatory capital.

Duan, J. C., Kim, B., Kim, W. & Shin, D. (2018). Default probabilities of privately held firms. Journal of Banking & Finance, 94, 235-250. https://doi.org/10.1016/j.jbankfin.2018.08.006

Ernst & Young (EY). (2018, 12 de abril). Impairment of financial instruments under IFRS 9 Financial Instruments. https://www.ey.com/en_gl/technical/ifrs-technical-resources/impairment-of-financial-instruments-under-ifrs-9-financial-instruments

Ernst & Young (EY). (2020, mayo). Covid-19: Impact on the expected credit loss using simplified approach. https://www.studocu.com/in/document/dav-university/seminar-2/covid-19-impact-on-the-expected-credit-loss-using-simplified-approach/43065366

Ernst & Young (EY). (2021, 30 de julio). Disclosure of covid-19 impact on expected credit losses of banks. https://www.ey.com/en_gl/technical/ifrs-technical-resources/disclosure-of-covid-19-impact-on-expected-credit-losses-of-banks

Fazlalipour Miyandoab, M., Nasiri, P. & Mosammam, A. (2023). Bayesian estimation of fractional difference parameter in ARFIMA models and its application. Information Sciences, 629, 144-154. https://doi.org/10.1016/j.ins.2023.01.108

Gentle, J. (ed.). (2020). Statistical analysis of financial data. With examples in R. CRC Press.

Hayter. A. (2012). Probability and statistics for engineers and scientists. Cengage Learning.

Hogg, R. V., McKean, J. & Craig, A. T. (2012). Introduction to mathematical statistics (7th ed.). Pearson Education.

International Accounting Standards Board (IASB). (2014a). IFRS 9 Financial Instruments. https://www.ifrs.org/issued-standards/list-of-standards/ifrs-9-financial-instruments/

International Accounting Standards Board (IASB). (2014b). IFRS 15 revenue from contracts with customers. https://www.ifrs.org/issued-standards/list-of-standards/ifrs-15-revenue-from-contracts-with-customers/

ISO 22301:2020-04. Seguridad y resiliencia. Sistema de Gestión de la Continuidad del Negocio. Requisitos. (ISO 22301:2019).

Ivanovic, Z., Bogdan, S. & Baresa, S. (2015). Modeling and estimating shadow sovereign ratings. Contemporary Economics, 9(3), 367-384. http://dx.doi.org/10.5709/ce.1897-9254.175

KPMG. (2017, julio). Demystifying Expected Credit Loss (ECL). https://assets.kpmg.com/content/dam/kpmg/in/pdf/2017/07/Demystifying-Expected-Credit-Loss.pdf

Lisicky, B. (2021). Impairment of assets and market reaction during covid-19 pandemic on the example of WSE. Risk, 9(10), 183. https://doi.org/10.3390/risks9100183

Merton, R. C. (1974). On the pricing of corporate debt: the risk structure of interest rates. The Journal of Finance, 29(2), 449-470. https://doi.org/10.1111/j.1540-6261.1974.tb03058.x

Miu, P. & Ozdemir, B. (2017). Adapting the Basel II advanced internal-ratings-based models for International Financial Reporting Standard 9. Journal of Credit Risk, 13(2), 53-83. https://doi.org/gbk7k5

Moretto, A. & Caniato, F. (2021). Can supply chain finance help mitigate the financial disruption brought on by covid-19? Journal of Purchasing and Supply Management, 27(4), art. 100713. https://doi.org/10.1016/j.pursup.2021.100713

Novotny-Farkas, Z. (2016). The interaction of the IFRS 9 expected loss approach with supervisory rules and implications for financial stability. Accounting in Europe, 13(2), 197-227. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2817983

Rahman, M. L., Amin, A. & Abdullah, M. A. (2021). The covid-19 outbreak and stock market reactions: evidence from Australia. Finance Research Letters, 38, art. 101832. https://doi.org/10.1016/j.frl.2020.101832

Rostek, K., Wiśniewski, M. & Skomra, W. (2022). Analysis and evaluation of business continuity measures employed in critical infrastructure during the covid-19 pandemic. Sustainability, 14(22), 15388. https://doi.org/10.3390/su142215388

Sánchez, S. A., Camacho, Z. E., Varon, R. M. & Carvajal, S. A. (2021). Impairment model applying Montecarlo simulation: expected loss approach for companies in the real sector. International Journal of Business and Management Science, 11(1), 99-117.

Uddin, M., Chowdhury, A., Anderson, K. & Chaudhuri, K. (2021). The effect of covid-19 pandemic on global stock market volatility: can economic strength help to manage the uncertainty? Journal of Business Research, 128, 31-44. https://doi.org/10.1016/j.jbusres.2021.01.061

Volarević, H. & Varovic, M. (2018). Internal model for IFRS 9 - Expected credit losses calculation. Ekonomski Pregled, 69(3), 269-297. https://doi.org/10.32910/ep.69.3.4

Zhang, D., Hu, M. & Ji, Q. (2020). Financial markets under the global pandemic of covid-19. Finance Research Letters, 36, art. 101528. https://doi.org/10.1016/j.frl.2020.101528

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