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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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    1. 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.
    2. Acharya, Viral & Engle, Robert & Pierret, Diane, 2014. "Testing macroprudential stress tests: The risk of regulatory risk weights," Journal of Monetary Economics, Elsevier, vol. 65(C), pages 36-53.
    3. Timothy Clark & Astrid A. Dick & Beverly Hirtle & Kevin J. Stiroh & Robard Williams, 2007. "The role of retail banking in the U.S. banking industry: risk, return, and industry structure," Economic Policy Review, Federal Reserve Bank of New York, vol. 13(Dec), pages 39-56.
    4. 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.).
    5. Eichengreen, Barry & Mody, Ashoka & Nedeljkovic, Milan & Sarno, Lucio, 2012. "How the Subprime Crisis went global: Evidence from bank credit default swap spreads," Journal of International Money and Finance, Elsevier, vol. 31(5), pages 1299-1318.
    6. Calomiris, Charles W. & Nissim, Doron, 2014. "Crisis-related shifts in the market valuation of banking activities," Journal of Financial Intermediation, Elsevier, vol. 23(3), pages 400-435.
    7. Thomas M. Eisenbach & Todd Keister & James J. McAndrews & Tanju Yorulmazer, 2014. "Stability of funding models: an analytical framework," Economic Policy Review, Federal Reserve Bank of New York, issue Feb, pages 29-47.
    8. Viral Acharya & Robert Engle & Matthew Richardson, 2012. "Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks," American Economic Review, American Economic Association, vol. 102(3), pages 59-64, May.
    9. Kau, James B, et al, 1992. "A Generalized Valuation Model for Fixed-Rate Residential Mortgages," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 24(3), pages 279-299, August.
    10. Eric Wong & Cho-Hoi Hui, 2009. "A Liquidity Risk Stress-Testing Framework with Interaction between Market and Credit Risks," Working Papers 0906, Hong Kong Monetary Authority.
    11. W. Scott Frame & Kristopher S. Gerardi & Paul S. Willen, 2015. "The failure of supervisory stress testing: Fannie Mae, Freddie Mac, and OFHEO," FRB Atlanta Working Paper 2015-3, Federal Reserve Bank of Atlanta.
    12. Allen Berger & Robert DeYoung & Mark Flannery & David Lee & Özde Öztekin, 2008. "How Do Large Banking Organizations Manage Their Capital Ratios?," Journal of Financial Services Research, Springer;Western Finance Association, vol. 34(2), pages 123-149, December.
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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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