IDEAS home Printed from
   My bibliography  Save this paper

Stress-testing U.S. bank holding companies: a dynamic panel quantile regression approach


  • Francisco Covas
  • Ben Rump
  • Egon Zakrajsek


We propose an econometric framework for estimating capital shortfalls of bank holding companies (BHCs) under pre-specified macroeconomic scenarios. To capture the nonlinear dynamics of bank losses and revenues during periods of financial stress, we use a fixed effects quantile autoregressive (FE-QAR) model with exogenous macroeconomic covariates, an approach that delivers a superior out-of-sample forecasting performance compared with the standard linear framework. According to the out-of-sample forecasts, the realized net charge-offs during the 2007-09 crisis are within the multi-step-ahead density forecasts implied by the FE-QAR model, but they are frequently outside the density forecasts generated using the corresponding linear model. This difference reflects the fact that the linear specification substantially underestimates loan losses, especially for real estate loan portfolios. Employing the macroeconomic stress scenario used in CCAR 2012, we use the density forecasts generated by the FE-QAR model to simulate capital shortfalls for a panel of large BHCs. For almost all institutions in the sample, the FE-QAR model generates capital shortfalls that are considerably higher than those implied by its linear counterpart, which suggests that our approach has the potential for detecting emerging vulnerabilities in the financial system.

Suggested Citation

  • Francisco Covas & Ben Rump & Egon Zakrajsek, 2013. "Stress-testing U.S. bank holding companies: a dynamic panel quantile regression approach," Finance and Economics Discussion Series 2013-55, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:2013-55

    Download full text from publisher

    File URL:
    Download Restriction: no

    File URL:
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    1. Wagner Piazza Gaglianone & Luiz Renato Lima, 2012. "Constructing Density Forecasts from Quantile Regressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1589-1607, December.
    2. Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
    3. Victor Chernozhukov & Iv·n Fern·ndez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves Without Crossing," Econometrica, Econometric Society, vol. 78(3), pages 1093-1125, May.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Beverly Hirtle & Til Schuermann & Kevin J. Stiroh, 2009. "Macroprudential supervision of financial institutions: lessons from the SCAP," Staff Reports 409, Federal Reserve Bank of New York.
    6. Antonella Foglia, 2009. "Stress Testing Credit Risk: A Survey of Authorities' Aproaches," International Journal of Central Banking, International Journal of Central Banking, vol. 5(3), pages 9-45, September.
    7. Boivin, Jean & Giannoni, Marc & Stevanovic, Dalibor, 2013. "Dynamic effects of credit shocks in a data-rich environment," Staff Reports 615, Federal Reserve Bank of New York, revised 01 Oct 2016.
    8. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 238-249, April.
    9. William B. English & Skander J. van den Heuvel & Egon Zakrajsek, 2012. "Interest rate risk and bank equity valuations," Finance and Economics Discussion Series 2012-26, Board of Governors of the Federal Reserve System (U.S.).
    10. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    11. Martin Cihak, 2007. "Introduction to Applied Stress Testing," IMF Working Papers 07/59, International Monetary Fund.
    12. Rodrigo Alfaro & Mathias Drehmann, 2009. "Macro stress tests and crises: what can we learn?," BIS Quarterly Review, Bank for International Settlements, December.
    13. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
    14. Gilchrist, Simon & Yankov, Vladimir & Zakrajsek, Egon, 2009. "Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 471-493, May.
    15. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    16. Pesaran, M.H., 2004. "‘General Diagnostic Tests for Cross Section Dependence in Panels’," Cambridge Working Papers in Economics 0435, Faculty of Economics, University of Cambridge.
    17. Donald P. Morgan & Stavros Peristiani & Vanessa Savino, 2010. "The information value of the stress test and bank opacity," Staff Reports 460, Federal Reserve Bank of New York.
    18. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    19. 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.).
    20. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    21. González-Rivera, Gloria & Yoldas, Emre, 2012. "Autocontour-based evaluation of multivariate predictive densities," International Journal of Forecasting, Elsevier, vol. 28(2), pages 328-342.
    22. Jon Faust & Simon Gilchrist & Jonathan H. Wright & Egon Zakrajšsek, 2013. "Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1501-1519, December.
    23. Marcella Lucchetta & Gianni De Nicolo, 2012. "Systemic Real and Financial Risks; Measurement, Forecasting, and Stress Testing," IMF Working Papers 12/58, International Monetary Fund.
    24. Roger Koenker & Zhijie Xiao, 2002. "Inference on the Quantile Regression Process," Econometrica, Econometric Society, vol. 70(4), pages 1583-1612, July.
    25. Lamarche, Carlos, 2010. "Robust penalized quantile regression estimation for panel data," Journal of Econometrics, Elsevier, vol. 157(2), pages 396-408, August.
    26. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    27. William B. English & William R. Nelson, 1998. "Profits and balance sheet developments at U.S. commercial banks in 1997," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), issue Jun, pages 391-419.
    28. Sorge, Marco & Virolainen, Kimmo, 2006. "A comparative analysis of macro stress-testing methodologies with application to Finland," Journal of Financial Stability, Elsevier, vol. 2(2), pages 113-151, June.
    29. Schechtman, Ricardo & Gaglianone, Wagner Piazza, 2012. "Macro stress testing of credit risk focused on the tails," Journal of Financial Stability, Elsevier, vol. 8(3), pages 174-192.
    30. Marco Sorge, 2004. "Stress-testing financial systems: an overview of current methodologies," BIS Working Papers 165, Bank for International Settlements.
    31. repec:spo:wpecon:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    32. Samuel G. Hanson & Anil K. Kashyap & Jeremy C. Stein, 2011. "A Macroprudential Approach to Financial Regulation," Journal of Economic Perspectives, American Economic Association, vol. 25(1), pages 3-28, Winter.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. repec:eee:finana:v:51:y:2017:i:c:p:25-53 is not listed on IDEAS
    2. Frantisek Cech & Jozef Barunik, 2017. "Measurement of Common Risk Factors: A Panel Quantile Regression Model for Returns," Papers 1708.08622,
    3. Chiu, Ching-Wai (Jeremy) & Hacioglu Hoke, Sinem, 2016. "Macroeconomic tail events with non-linear Bayesian VARs," Bank of England working papers 611, Bank of England.
    4. Paul Glasserman & Gowtham Tangirala, 2015. "Are the Federal Reserve's Stress Test Results Predictable?," Working Papers 15-02, Office of Financial Research, US Department of the Treasury.
    5. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2017. "Forecasting with Dynamic Panel Data Models," Papers 1709.10193,
    6. Kok, Christoffer & Pancaro, Cosimo & Mirza, Harun, 2017. "Macro stress testing euro area banks' fees and commissions," Working Paper Series 2029, European Central Bank.
    7. repec:gam:jrisks:v:5:y:2017:i:3:p:38-:d:105140 is not listed on IDEAS
    8. 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.
    9. Pavel Kapinos & Oscar A. Mitnik, 2016. "A Top-down Approach to Stress-testing Banks," Journal of Financial Services Research, Springer;Western Finance Association, vol. 49(2), pages 229-264, June.
    10. 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.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:fip:fedgfe:2013-55. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Franz Osorio). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.