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On the accuracy of alternative approaches for calibrating bank stress test models

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  • Paul H. Kupiec

    (American Enterprise Institute)

Abstract

Multi-year forecasts of bank performance under stressful economic conditions determine large institution regulatory capital requirements and yet the accuracy of these forecasts is undocumented. I compare the accuracies of alternative stress test model forecasts using the financial crisis as the stress scenario. Models include specifications that mimic the Federal Reserve CLASS model and alternatives that use Lasso, the AIC and an abridged set of explanatory variables. A simple single-equation Lasso model has, by far, the best forecast accuracy. Large differences in model forecast accuracy are undetectable from estimation sample statistics. These findings highlight the need for new methods for validating bank stress test models.
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Suggested Citation

  • Paul H. Kupiec, 2018. "On the accuracy of alternative approaches for calibrating bank stress test models," AEI Economics Working Papers 980152, American Enterprise Institute.
    • Handle: RePEc:aei:rpaper:980152
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    Citations

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    Cited by:

    1. Kupiec, Paul H., 2020. "Policy uncertainty and bank stress testing," Journal of Financial Stability, Elsevier, vol. 51(C).
    2. Bocchio, Cecilia & Crook, Jonathan & Andreeva, Galina, 2023. "The impact of macroeconomic scenarios on recurrent delinquency: A stress testing framework of multi-state models for mortgages," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1655-1677.
    3. Małgorzata Iwanicz-Drozdowska & Krzysztof Jackowicz & Maciej Karczmarczyk, 2021. "“The Crooked Smile of TCR†: Banks’ Solvency and Restructuring Costs in the European Banking Industry," SAGE Open, , vol. 11(3), pages 21582440211, September.
    4. repec:aei:rpaper:008586461 is not listed on IDEAS
    5. Christos Floros & Efstathios Karpouzis & Nikolaos Daskalakis, 2024. "Stock Markets and Stress Test Announcements: Evidence from European Banks," Economies, MDPI, vol. 12(7), pages 1-11, July.
    6. Brummelhuis, Raymond & Luo, Zhongmin, 2019. "Bank Net Interest Margin Forecasting and Capital Adequacy Stress Testing by Machine Learning Techniques," MPRA Paper 94779, University Library of Munich, Germany.
    7. Mai Dao & Lam Nguyen, 2025. "Variable selection in macroeconomic stress test: a Bayesian quantile regression approach," Empirical Economics, Springer, vol. 68(3), pages 1113-1169, March.
    8. Nguyen, Thach Vu Hong & Ahmed, Shamim & Chevapatrakul, Thanaset & Onali, Enrico, 2020. "Do stress tests affect bank liquidity creation?," Journal of Corporate Finance, Elsevier, vol. 64(C).
    9. Pedro Guerra & Mauro Castelli, 2021. "Machine Learning Applied to Banking Supervision a Literature Review," Risks, MDPI, vol. 9(7), pages 1-24, July.

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    JEL classification:

    • A - General Economics and Teaching

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