IDEAS home Printed from https://ideas.repec.org/a/kap/rqfnac/v55y2020i1d10.1007_s11156-019-00840-5.html
   My bibliography  Save this article

Model and estimation risk in credit risk stress tests

Author

Listed:
  • Peter Grundke

    (Osnabrück University)

  • Kamil Pliszka

    (Deutsche Bundesbank)

  • Michael Tuchscherer

    (Osnabrück University)

Abstract

This paper deals with stress tests for credit risk and shows how exploiting the discretion when setting up and implementing a model can drive the results of a quantitative stress test for default probabilities. For this purpose, we employ several variations of a CreditPortfolioView-style model using US data ranging from 2004 to 2016. We show that seemingly only slightly differing specifications can lead to entirely different stress test results—in relative and absolute terms. That said, our findings reveal that the conversion of a shock (i.e., stress event) increases the (non-stress) default probability by 20–80%—depending on the stress test model selected. Interestingly, forecasts for non-stress default probabilities are less exposed to model and estimation risk. In addition, the risk horizon over which the stress default probabilities are forecasted and whether we consider mean stress default probabilities or quantiles seem to play only a minor role for the dispersion between the results of the different model specifications. Our findings emphasize the importance of extensive robustness checks for model-based credit risk stress tests.

Suggested Citation

  • Peter Grundke & Kamil Pliszka & Michael Tuchscherer, 2020. "Model and estimation risk in credit risk stress tests," Review of Quantitative Finance and Accounting, Springer, vol. 55(1), pages 163-199, July.
  • Handle: RePEc:kap:rqfnac:v:55:y:2020:i:1:d:10.1007_s11156-019-00840-5
    DOI: 10.1007/s11156-019-00840-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11156-019-00840-5
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11156-019-00840-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jan-Henning Trustorff & Paul Konrad & Jens Leker, 2011. "Credit risk prediction using support vector machines," Review of Quantitative Finance and Accounting, Springer, vol. 36(4), pages 565-581, May.
    2. Peter Grundke & Kamil Pliszka, 2018. "A macroeconomic reverse stress test," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 1093-1130, May.
    3. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    4. William Greene, 2001. "Fixed and Random Effects in Nonlinear Models," Working Papers 01-01, New York University, Leonard N. Stern School of Business, Department of Economics.
    5. Danielsson, Jon & James, Kevin R. & Valenzuela, Marcela & Zer, Ilknur, 2016. "Model risk of risk models," Journal of Financial Stability, Elsevier, vol. 23(C), pages 79-91.
    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. Anat Admati & Martin Hellwig, 2013. "The Bankers' New Clothes: What's Wrong with Banking and What to Do about It," Economics Books, Princeton University Press, edition 1, volume 1, number 9929.
    8. Blöchlinger, Andreas & Leippold, Markus, 2018. "Are Ratings the Worst Form of Credit Assessment Except for All the Others?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(1), pages 299-334, February.
    9. Martin Feldkircher & Stefan Zeugner, 2009. "Benchmark Priors Revisited: On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging," IMF Working Papers 2009/202, International Monetary Fund.
    10. Markus Behn & Rainer Haselmann & Vikrant Vig, 2022. "The Limits of Model‐Based Regulation," Journal of Finance, American Finance Association, vol. 77(3), pages 1635-1684, June.
    11. repec:zbw:bofrdp:2004_018 is not listed on IDEAS
    12. repec:onb:oenbwp:y:2002:i:3:b:3 is not listed on IDEAS
    13. Berg, Tobias & Koziol, Philipp, 2017. "An analysis of the consistency of banks’ internal ratings," Journal of Banking & Finance, Elsevier, vol. 78(C), pages 27-41.
    14. Ioan TRENCA & Annamaria BENYOVSZKI, 2008. "Credit risk, a macroeconomic model application for Romania," Finante - provocarile viitorului (Finance - Challenges of the Future), University of Craiova, Faculty of Economics and Business Administration, vol. 1(7), pages 118-126, May.
    15. Harvir Kalirai & Martin Scheicher, 2002. "Macroeconomic Stress Testing: Preliminary Evidence for Austria," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), issue 3, pages 58-74.
    16. McNeil, Alexander J. & Wendin, Jonathan P., 2007. "Bayesian inference for generalized linear mixed models of portfolio credit risk," Journal of Empirical Finance, Elsevier, vol. 14(2), pages 131-149, March.
    17. Fenech, Jean Pierre & Vosgha, Hamed & Shafik, Salwa, 2015. "Loan default correlation using an Archimedean copula approach: A case for recalibration," Economic Modelling, Elsevier, vol. 47(C), pages 340-354.
    18. Breuer, Thomas & Jandačka, Martin & Mencía, Javier & Summer, Martin, 2012. "A systematic approach to multi-period stress testing of portfolio credit risk," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 332-340.
    19. Theo S. Eicher & Chris Papageorgiou & Adrian E. Raftery, 2011. "Default priors and predictive performance in Bayesian model averaging, with application to growth determinants," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 30-55, January/F.
    20. Traczynski, Jeffrey, 2017. "Firm Default Prediction: A Bayesian Model-Averaging Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(3), pages 1211-1245, June.
    21. 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.
    22. 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.
    23. Filippo Curti & Ibrahim Ergen & Minh Le & Marco Migueis & Rob T. Stewart, 2016. "Benchmarking Operational Risk Models," Finance and Economics Discussion Series 2016-070, Board of Governors of the Federal Reserve System (U.S.).
    24. Miroslav Misina & David Tessier & Shubhasis Dey, 2006. "Stress Testing the Corporate Loans Portfolio of the Canadian Banking Sector," Staff Working Papers 06-47, Bank of Canada.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Martin Guth, 2022. "Predicting Default Probabilities for Stress Tests: A Comparison of Models," Papers 2202.03110, arXiv.org.
    2. Jeffrey R. Stokes, 2023. "A nonlinear inversion procedure for modeling the effects of economic factors on credit risk migration," Review of Quantitative Finance and Accounting, Springer, vol. 61(3), pages 855-878, October.
    3. Angelos Kanas & Panagiotis D. Zervopoulos, 2022. "Federal home loan bank advances and systemic risk," Review of Quantitative Finance and Accounting, Springer, vol. 59(4), pages 1525-1557, November.
    4. Pliszka, Kamil, 2021. "System-wide and banks' internal stress tests: Regulatory requirements and literature review," Discussion Papers 19/2021, Deutsche Bundesbank.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ruja, Catalin, 2014. "Macro Stress-Testing Credit Risk in Romanian Banking System," MPRA Paper 58244, University Library of Munich, Germany.
    2. Patrick Van Roy & Stijn Ferrari & Cristina Vespro, 2018. "Sensitivity of credit risk stress test results: Modelling issues with an application to Belgium," Working Paper Research 338, National Bank of Belgium.
    3. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    4. Abildgren, Kim, 2014. "Far out in the tails – The historical distributions of macro-financial risk factors in Denmark," Nationaløkonomisk tidsskrift, Nationaløkonomisk Forening, vol. 2014(1), pages 1-31.
    5. Ferrari, Stijn & Van Roy, Patrick & Vespro, Cristina, 2021. "Sensitivity of credit risk stress test results: Modelling issues with an application to Belgium," Journal of Financial Stability, Elsevier, vol. 52(C).
    6. Kanas, Angelos & Molyneux, Philip, 2018. "Macro stress testing the U.S. banking system," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 54(C), pages 204-227.
    7. Antonio Ciccone & Marek Jarociński, 2010. "Determinants of Economic Growth: Will Data Tell?," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(4), pages 222-246, October.
    8. Feldkircher, Martin, 2014. "The determinants of vulnerability to the global financial crisis 2008 to 2009: Credit growth and other sources of risk," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 19-49.
    9. Vašíček, Bořek & Žigraiová, Diana & Hoeberichts, Marco & Vermeulen, Robert & Šmídková, Kateřina & de Haan, Jakob, 2017. "Leading indicators of financial stress: New evidence," Journal of Financial Stability, Elsevier, vol. 28(C), pages 240-257.
    10. Ley, Eduardo & Steel, Mark F.J., 2012. "Mixtures of g-priors for Bayesian model averaging with economic applications," Journal of Econometrics, Elsevier, vol. 171(2), pages 251-266.
    11. Roman Horvath & Marek Rusnak & Katerina Smidkova & Jan Zapal, 2014. "The dissent voting behaviour of central bankers: what do we really know?," Applied Economics, Taylor & Francis Journals, vol. 46(4), pages 450-461, February.
    12. Aart Kraay & Norikazu Tawara, 2013. "Can specific policy indicators identify reform priorities?," Journal of Economic Growth, Springer, vol. 18(3), pages 253-283, September.
    13. Peter Grundke & Kamil Pliszka, 2018. "A macroeconomic reverse stress test," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 1093-1130, May.
    14. Wang, Zheqi & Crook, Jonathan & Andreeva, Galina, 2020. "Reducing estimation risk using a Bayesian posterior distribution approach: Application to stress testing mortgage loan default," European Journal of Operational Research, Elsevier, vol. 287(2), pages 725-738.
    15. Tomas Havranek, Dominik Herman, and Zuzana Irsova, 2018. "Does Daylight Saving Save Electricity? A Meta-Analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    16. Václav Brož & Lukáš Pfeifer, 2021. "Are risk weights of banks in the Czech Republic procyclical? Evidence from wavelet analysis," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(1), pages 113-139.
    17. Bruns, Stephan B. & Ioannidis, John P.A., 2020. "Determinants of economic growth: Different time different answer?," Journal of Macroeconomics, Elsevier, vol. 63(C).
    18. Gernot Doppelhofer & Melvyn Weeks, 2011. "Robust Growth Determinants," CESifo Working Paper Series 3354, CESifo.
    19. Havranek, Tomas & Rusnak, Marek & Sokolova, Anna, 2017. "Habit formation in consumption: A meta-analysis," European Economic Review, Elsevier, vol. 95(C), pages 142-167.
    20. 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.

    More about this item

    Keywords

    Credit risk; Default probability; Estimation risk; Model risk; Stress tests;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    Statistics

    Access and download statistics

    Corrections

    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:kap:rqfnac:v:55:y:2020:i:1:d:10.1007_s11156-019-00840-5. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://springer.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.