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Emerging from the Storm: Forecasting Bank Loan Quality in the Aftermath of COVID-19

Author

Listed:
  • TORI Athina
  • GJECI Ardit
  • KUFO Andromahi

Abstract

As part of the credit risk management process of financial institutions, the non-performing loans (NPLs) ratio remains one of the essential components that distinguishes the well-managed assets of a bank. In this paper, we aim to empirically forecast the level of non-performing loans (NPL) including afflicted periods like the COVID-19 ­pandemic using a seasonal ARIMA model. Our analysis is based on the NPLs level observed in the Albanian banking system between December 2015 and December 2022. The results indicate that the seasonal ARIMA (0,1,1)x(2,2,2)12 is the appropriate model that can be applied to predict the monthly level of NPLs. The results also reveal that the expected average monthly ratio of NPLs remains stable, with a slight decrease until the end of 2023. Efforts to be proactive rather than reacting post-factum involve using mechanisms and forecasting models to define non-performing loan ratios and better manage them. This paper considers significant implications in credit risk management in terms of developing actions to manage the magnitude of non-performing loans throughout the COVID-19 pandemic.­

Suggested Citation

  • TORI Athina & GJECI Ardit & KUFO Andromahi, 2024. "Emerging from the Storm: Forecasting Bank Loan Quality in the Aftermath of COVID-19," European Journal of Interdisciplinary Studies, Bucharest Economic Academy, issue 01, March.
    • Handle: RePEc:jis:ejistu:y:2024:i:01:id:537
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    More about this item

    Keywords

    COVID-19; forecasting; SARIMA; non-performing loans;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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