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Can macro variables used in stress testing forecast the performance of banks?

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Abstract

When stress tests for the banking sector use a macroeconomic scenario, an unstated premise is that macro variables should be useful factors in forecasting the performance of banks. We assess whether variables such as the ones included in stress tests for U.S. bank holding companies help improve out of sample forecasts of chargeoffs on loans, revenues, and capital measures, relative to forecasting models that exclude a role for macro factors. Using only public data on bank performance, we find the macro variables helpful, but not for all measures. Moreover, even our best-performing models imply bands of uncertainty around the forecasts so large as to make it challenging to distinguish the implications of alternative macro scenarios.

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  • Luca Guerrieri & Michelle Welch, 2012. "Can macro variables used in stress testing forecast the performance of banks?," Finance and Economics Discussion Series 2012-49, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:2012-49
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    References listed on IDEAS

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    1. Quagliariello,Mario (ed.), 2009. "Stress-testing the Banking System," Cambridge Books, Cambridge University Press, number 9780521767309, January.
    2. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    3. Estrella, Arturo & Hardouvelis, Gikas A, 1991. "The Term Structure as a Predictor of Real Economic Activity," Journal of Finance, American Finance Association, vol. 46(2), pages 555-576, June.
    4. Crook, Jonathan & Banasik, John, 2012. "Forecasting and explaining aggregate consumer credit delinquency behaviour," International Journal of Forecasting, Elsevier, vol. 28(1), pages 145-160.
    5. Maria Soledad Martinez Peria & Mr. Giovanni Majnoni & Mr. Matthew T Jones & Mr. Winfrid Blaschke, 2001. "Stress Testing of Financial Systems: An Overview of Issues, Methodologies, and FSAP Experiences," IMF Working Papers 2001/088, International Monetary Fund.
    6. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
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    Cited by:

    1. Acharya, Viral V. & Berger, Allen N. & Roman, Raluca A., 2018. "Lending implications of U.S. bank stress tests: Costs or benefits?," Journal of Financial Intermediation, Elsevier, vol. 34(C), pages 58-90.
    2. Wu, Deming & Fang, Ming & Wang, Qing, 2018. "An empirical study of bank stress testing for auto loans," Journal of Financial Stability, Elsevier, vol. 39(C), pages 79-89.
    3. Kolari, James W. & López-Iturriaga, Félix J. & Sanz, Ivan Pastor, 2019. "Predicting European bank stress tests: Survival of the fittest," Global Finance Journal, Elsevier, vol. 39(C), pages 44-57.
    4. Covas, Francisco B. & Rump, Ben & Zakrajšek, Egon, 2014. "Stress-testing US bank holding companies: A dynamic panel quantile regression approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 691-713.
    5. repec:aei:rpaper:008586461 is not listed on IDEAS
    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. Pritsker, Matt, 2019. "An overview of regulatory stress-testing and steps to improve it," Global Finance Journal, Elsevier, vol. 39(C), pages 39-43.
    8. Fang, Cao & Yeager, Timothy J., 2020. "A historical loss approach to community bank stress testing," Journal of Banking & Finance, Elsevier, vol. 118(C).
    9. Hirtle, Beverly & Kovner, Anna & Vickery, James & Bhanot, Meru, 2016. "Assessing financial stability: The Capital and Loss Assessment under Stress Scenarios (CLASS) model," Journal of Banking & Finance, Elsevier, vol. 69(S1), pages 35-55.
    10. Matthew Pritsker, 2017. "Choosing Stress Scenarios for Systemic Risk Through Dimension Reduction," Supervisory Research and Analysis Working Papers RPA 17-4, Federal Reserve Bank of Boston.

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