IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v13y2025i2p38-d1595063.html
   My bibliography  Save this article

Improving Credit Risk Assessment in Uncertain Times: Insights from IFRS 9

Author

Listed:
  • Petr Jakubik

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Smetanovo nabrezi 6, 110 01 Prague 1, Czech Republic)

  • Saida Teleu

    (Central Bank of Barbados, Tom Adams Financial Centre, Spry Street, Bridgetown 11126, Barbados
    Department of Accounting and Finance, The School of Business, Anglo-American University in Prague, Letenská 120/5, 118 00 Prague 1, Czech Republic)

Abstract

This study highlights the superior performance of Bayesian Model Averaging (BMA) in credit risk modeling under IFRS 9, particularly during economic uncertainty, such as the COVID-19 pandemic. Using granular bank-level data from Malta, spanning 2017–2023, the analysis integrates macroeconomic scenarios and sector-specific transition matrices to assess credit risk dynamics. Key findings demonstrate BMA’s ability to outperform Single-Equation Models (SEM) in predictive accuracy, robustness, and adaptability. The results emphasize BMA’s resilience to structural economic changes, making it a critical tool for regulatory stress testing and provisioning in small open economies highly exposed to external shocks. This work underscores the importance of forward-looking, flexible frameworks for credit risk management and policy decisions.

Suggested Citation

  • Petr Jakubik & Saida Teleu, 2025. "Improving Credit Risk Assessment in Uncertain Times: Insights from IFRS 9," Risks, MDPI, vol. 13(2), pages 1-20, February.
    • Handle: RePEc:gam:jrisks:v:13:y:2025:i:2:p:38-:d:1595063
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/13/2/38/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/13/2/38/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January.
    2. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    3. Castrén, Olli & Dées, Stéphane & Zaher, Fadi, 2008. "Global macro-financial shocks and expected default frequencies in the euro area," Working Paper Series 875, European Central Bank.
    4. Korobilis, Dimitris & Koop, Gary, 2018. "Variational Bayes inference in high-dimensional time-varying parameter models," Essex Finance Centre Working Papers 22665, University of Essex, Essex Business School.
    5. Marta Bańbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92, January.
    6. Bloor, Chris & Matheson, Troy, 2011. "Real-time conditional forecasts with Bayesian VARs: An application to New Zealand," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 26-42, January.
    7. Avramov, Doron, 2002. "Stock return predictability and model uncertainty," Journal of Financial Economics, Elsevier, vol. 64(3), pages 423-458, June.
    8. Frydman, Halina & Schuermann, Til, 2008. "Credit rating dynamics and Markov mixture models," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1062-1075, June.
    9. Marco Gross & Javier Población, 2019. "Implications of Model Uncertainty for Bank Stress Testing," Journal of Financial Services Research, Springer;Western Finance Association, vol. 55(1), pages 31-58, February.
    10. Kevin Aretz & Peter F. Pope, 2013. "Common Factors in Default Risk Across Countries and Industries," European Financial Management, European Financial Management Association, vol. 19(1), pages 108-152, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    2. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    3. Öğünç, Fethi & Akdoğan, Kurmaş & Başer, Selen & Chadwick, Meltem Gülenay & Ertuğ, Dilara & Hülagü, Timur & Kösem, Sevim & Özmen, Mustafa Utku & Tekatlı, Necati, 2013. "Short-term inflation forecasting models for Turkey and a forecast combination analysis," Economic Modelling, Elsevier, vol. 33(C), pages 312-325.
    4. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    5. Figlewski, Stephen & Frydman, Halina & Liang, Weijian, 2012. "Modeling the effect of macroeconomic factors on corporate default and credit rating transitions," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 87-105.
    6. Myriam Ben Ayed & Adel Karaa & Jean‐Luc Prigent, 2018. "Duration Models For Credit Rating Migration: Evidence From The Financial Crisis," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1870-1886, July.
    7. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    8. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    9. Wozabal, David & Hochreiter, Ronald, 2012. "A coupled Markov chain approach to credit risk modeling," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 403-415.
    10. Johannes Hörner & Nicolas S Lambert, 2021. "Motivational Ratings [Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(4), pages 1892-1935.
    11. Jeffrey R. Stokes, 2023. "A nonlinear inversion procedure for modeling the effects of economic factors on credit risk migration," Review of Quantitative Finance and Accounting, Springer, vol. 61(3), pages 855-878, October.
    12. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    13. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    14. Auer, Simone, 2019. "Monetary policy shocks and foreign investment income: Evidence from a large Bayesian VAR," Journal of International Money and Finance, Elsevier, vol. 93(C), pages 142-166.
    15. Stepankova, Barbora & Teply, Petr, 2023. "Consistency of banks' internal probability of default estimates: Empirical evidence from the COVID-19 crisis," Journal of Banking & Finance, Elsevier, vol. 154(C).
    16. Bermingham, Colin & Conefrey, Thomas, 2014. "The Irish macroeconomic response to an external shock with an application to stress testing," Journal of Policy Modeling, Elsevier, vol. 36(3), pages 454-470.
    17. Jansen, W. Jos & Jin, Xiaowen & de Winter, Jasper M., 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 411-436.
    18. Xing, Haipeng & Sun, Ning & Chen, Ying, 2012. "Credit rating dynamics in the presence of unknown structural breaks," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 78-89.
    19. Bannier, Christina E. & Hirsch, Christian W., 2010. "The economic function of credit rating agencies - What does the watchlist tell us?," Journal of Banking & Finance, Elsevier, vol. 34(12), pages 3037-3049, December.
    20. Michael Kalkbrener & Natalie Packham, 2024. "A Markov approach to credit rating migration conditional on economic states," Papers 2403.14868, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:13:y:2025:i:2:p:38-:d:1595063. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.