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Assessing financial stability: The Capital and Loss Assessment under Stress Scenarios (CLASS) model

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  • Hirtle, Beverly
  • Kovner, Anna
  • Vickery, James
  • Bhanot, Meru

Abstract

The CLASS model is a top-down capital stress testing framework that uses public data, simple econometric models and auxiliary assumptions to project the effect of macroeconomic scenarios on U.S. banking firms. Through the lens of the model, we find that the total banking system capital shortfall under stressful macroeconomic conditions began to rise 4years before the financial crisis, peaking in the fourth quarter of 2008. The capital gap has since fallen sharply, and is now significantly below pre-crisis levels. In the cross-section, banking firms estimated to be most sensitive to macroeconomic conditions also have higher capital ratios, consistent with a “precautionary” view of bank capital, though this behavior is evident only since the crisis. We interpret our results as evidence that the resiliency of the U.S. banking system has improved since the financial crisis, and also as an illustration of the value of stress testing as a macroprudential policy tool.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jbfina:v:69:y:2016:i:s1:p:s35-s55
    DOI: 10.1016/j.jbankfin.2015.09.021
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    References listed on IDEAS

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    Cited by:

    1. Michael W. McCracken & Joseph T. McGillicuddy, 2019. "An empirical investigation of direct and iterated multistep conditional forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 181-204, March.
    2. Michael Jacobs, 2019. "An Analysis of the Impact of Modeling Assumptions in the Current Expected Credit Loss (CECL) Framework on the Provisioning for Credit Loss," Journal of Risk & Control, Risk Market Journals, vol. 6(1), pages 65-112.
    3. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Forecasting With Dynamic Panel Data Models," Econometrica, Econometric Society, vol. 88(1), pages 171-201, January.
    4. Kupiec, Paul H., 2018. "On the accuracy of alternative approaches for calibrating bank stress test models," Journal of Financial Stability, Elsevier, vol. 38(C), pages 132-146.
    5. Galina Hale & John Krainer & Erin McCarthy, 2020. "Aggregation Level in Stress-Testing Models," International Journal of Central Banking, International Journal of Central Banking, vol. 16(4), pages 1-46, September.
    6. Armantier, Olivier & Ghysels, Eric & Sarkar, Asani & Shrader, Jeffrey, 2015. "Discount window stigma during the 2007–2008 financial crisis," Journal of Financial Economics, Elsevier, vol. 118(2), pages 317-335.
    7. 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.
    8. Miora Rakotonirainy & Jean Razafindravonona & Christian Rasolomanana, 2020. "Macro Stress Testing Credit Risk: Case of Madagascar Banking Sector," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(2), pages 199-218.
    9. Fang, Cao & Yeager, Timothy J., 2020. "A historical loss approach to community bank stress testing," Journal of Banking & Finance, Elsevier, vol. 118(C).
    10. Jiri Panos & Petr Polak, 2019. "How to Improve the Model Selection Procedure in a Stress-testing Framework," Working Papers 2019/9, Czech National Bank.
    11. Flannery, Mark & Hirtle, Beverly & Kovner, Anna, 2017. "Evaluating the information in the federal reserve stress tests," Journal of Financial Intermediation, Elsevier, vol. 29(C), pages 1-18.
    12. de Mendonça, Helder Ferreira & Silva, Rafael Bernardo da, 2018. "Effect of banking and macroeconomic variables on systemic risk: An application of ΔCOVAR for an emerging economy," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 141-157.
    13. Gerlach, Jeffrey R. & Mora, Nada & Uysal, Pinar, 2018. "Bank funding costs in a rising interest rate environment," Journal of Banking & Finance, Elsevier, vol. 87(C), pages 164-186.
    14. Gerhard Hambusch & Sherrill Shaffer, 2016. "Forecasting bank leverage: an alternative to regulatory early warning models," Journal of Regulatory Economics, Springer, vol. 50(1), pages 38-69, August.
    15. Busch, Ramona & Koziol, Philipp & Mitrovic, Marc, 2018. "Many a little makes a mickle: Stress testing small and medium-sized German banks," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 237-253.
    16. 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.
    17. Silva, Walmir & Kimura, Herbert & Sobreiro, Vinicius Amorim, 2017. "An analysis of the literature on systemic financial risk: A survey," Journal of Financial Stability, Elsevier, vol. 28(C), pages 91-114.

    More about this item

    Keywords

    Capital; Bank; Stress testing; Systemic risk; Financial stability;

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G01 - Financial Economics - - General - - - Financial Crises

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