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Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach

Author

Listed:
  • González, Marta Ramos
  • Ureña, Antonio Partal
  • Fernández-Aguado, Pilar Gómez

Abstract

The economic onslaught of the COVID-19 pandemic has compromised the risk management of financial institutions. The consequences related to such an unprecedented situation are difficult to foresee with certainty using traditional methods. The regulatory credit loss attached to defaulted mortgages, so-called expected loss best estimate (ELBE), is forecasted using a machine learning technique. The projection of two ELBEs for 2022 and their comparison are presented. One accounts for the outbreak's impact, and the other presumes the nonexistence of the pandemic. Then, it is concluded that the referred crisis surely adversely affects said high-risk portfolios. The proposed method has excellent performance and may serve to estimate future expected and unexpected losses amidst any event of extraordinary magnitude.

Suggested Citation

  • González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023. "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:riibaf:v:64:y:2023:i:c:s0275531923000338
    DOI: 10.1016/j.ribaf.2023.101907
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    References listed on IDEAS

    as
    1. Karadima, Maria & Louri, Helen, 2021. "Economic policy uncertainty and non-performing loans: The moderating role of bank concentration," Finance Research Letters, Elsevier, vol. 38(C).
    2. Shamima Ahmed & Muneer Alshater & Anis El Ammari & Helmi Hammami, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Post-Print hal-03697290, HAL.
    3. Hu, Shiwei & Zhang, Yuyao, 2021. "COVID-19 pandemic and firm performance: Cross-country evidence," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 365-372.
    4. Neudecker, Heinz & Polasek, Wolfgang & Liu, Shuangzhe, 1995. "The heteroskedastic linear regression model and the Hadamard product a note," Journal of Econometrics, Elsevier, vol. 68(2), pages 361-366, August.
    5. World Bank, 2020. "Global Economic Prospects, January 2020," World Bank Publications - Books, The World Bank Group, number 33044, December.
    6. Seetharaman, Priya, 2020. "Business models shifts: Impact of Covid-19," International Journal of Information Management, Elsevier, vol. 54(C).
    7. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    8. Li, Jing-Ping & Mirza, Nawazish & Rahat, Birjees & Xiong, Deping, 2020. "Machine learning and credit ratings prediction in the age of fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    9. Yu, Lean & Huang, Xiaowen & Yin, Hang, 2020. "Can machine learning paradigm improve attribute noise problem in credit risk classification?," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 440-455.
    10. Ampudia, Miguel & van Vlokhoven, Has & Żochowski, Dawid, 2016. "Financial fragility of euro area households," Journal of Financial Stability, Elsevier, vol. 27(C), pages 250-262.
    11. Nicolas Huck, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," Post-Print hal-02143971, HAL.
    12. García-Céspedes, Rubén & Moreno, Manuel, 2022. "The generalized Vasicek credit risk model: A Machine Learning approach," Finance Research Letters, Elsevier, vol. 47(PA).
    13. Kelly, Robert & O’Malley, Terence, 2016. "The good, the bad and the impaired: A credit risk model of the Irish mortgage market," Journal of Financial Stability, Elsevier, vol. 22(C), pages 1-9.
    14. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    15. Cathal O'Donoghue & Denisa M. Sologon & Iryna Kyzyma & John McHale, 2020. "Modelling the Distributional Impact of the COVID‐19 Crisis," Fiscal Studies, John Wiley & Sons, vol. 41(2), pages 321-336, June.
    16. Ioannidis, John P.A. & Cripps, Sally & Tanner, Martin A., 2022. "Forecasting for COVID-19 has failed," International Journal of Forecasting, Elsevier, vol. 38(2), pages 423-438.
    17. World Bank, 2021. "Global Economic Prospects, January 2021," World Bank Publications - Books, The World Bank Group, number 34710, December.
    18. Jurgita Markevičiūtė & Jolita Bernatavičienė & Rūta Levulienė & Viktor Medvedev & Povilas Treigys & Julius Venskus, 2022. "Impact of COVID-19-Related Lockdown Measures on Economic and Social Outcomes in Lithuania," Mathematics, MDPI, vol. 10(15), pages 1-20, August.
    19. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.
    20. Al-Maadid, Alanoud & Alhazbi, Saleh & Al-Thelaya, Khaled, 2022. "Using machine learning to analyze the impact of coronavirus pandemic news on the stock markets in GCC countries," Research in International Business and Finance, Elsevier, vol. 61(C).
    21. Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.
    22. Sharif, Arshian & Aloui, Chaker & Yarovaya, Larisa, 2020. "COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach," International Review of Financial Analysis, Elsevier, vol. 70(C).
    23. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    24. World Bank, 2021. "Global Economic Prospects, June 2021," World Bank Publications - Books, The World Bank Group, number 35647, December.
    25. World Bank, 2020. "Global Economic Prospects, June 2020," World Bank Publications - Books, The World Bank Group, number 33748, December.
    26. Xiao Ke & Cheng Hsiao, 2022. "Economic impact of the most drastic lockdown during COVID‐19 pandemic—The experience of Hubei, China," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 187-209, January.
    27. Luis Pedauga & Francisco Sáez & Blanca L. Delgado-Márquez, 2022. "Macroeconomic lockdown and SMEs: the impact of the COVID-19 pandemic in Spain," Small Business Economics, Springer, vol. 58(2), pages 665-688, February.
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    More about this item

    Keywords

    Machine learning; COVID-19; Internal-rating-based; Credit risk; Defaulted exposures;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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