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A macroeconomic reverse stress test

Author

Listed:
  • Peter Grundke

    (Osnabrueck University)

  • Kamil Pliszka

    (Deutsche Bundesbank)

Abstract

Reverse stress tests are a relatively new stress test instrument that aims at finding exactly those scenarios that cause a bank to cross the frontier between survival and default. Afterward, the scenario which is most probable has to be identified. This paper sketches a framework for a quantitative reverse stress test for maturity-transforming banks that are exposed to credit and interest rate risk and demonstrates how the model can be calibrated empirically. The main features of the proposed framework are: (1) the necessary steps of a reverse stress test (solving an inversion problem and computing the scenario probabilities) can be performed within one model, (2) scenarios are characterized by realizations of macroeconomic risk factors, (3) principal component analysis helps to reduce the dimensionality of the space of systematic risk factors, (4) due to data limitations, the results of reverse stress tests are exposed to considerable model and estimation risk, which makes numerous robustness checks necessary.

Suggested Citation

  • Peter Grundke & Kamil Pliszka, 2018. "A macroeconomic reverse stress test," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 1093-1130, May.
    • Handle: RePEc:kap:rqfnac:v:50:y:2018:i:4:d:10.1007_s11156-017-0655-8
    DOI: 10.1007/s11156-017-0655-8
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    Citations

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    Cited by:

    1. 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.
    2. Daniel Grigat & Fabio Caccioli, 2017. "Reverse stress testing interbank networks," Papers 1702.08744, arXiv.org, revised Mar 2017.
    3. Claudio Albanese & Stéphane Crépey & Stefano Iabichino, 2023. "Quantitative reverse stress testing, bottom up," Quantitative Finance, Taylor & Francis Journals, vol. 23(5), pages 863-875, May.
    4. Colin Ellis, 2017. "Scenario-based stress tests: are they painful enough?," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 11(2), June.
    5. Cristófoli, María Elizabeth & García Fronti, Javier, 2019. "Macroeconomic Reverse Stress Testing: An Early-Warning System for Spanish Banking Regulators. Analysis Based on the 2008 Global Financial Crisis / Prueba de resistencia inversa Macroeconómica: una pru," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, vol. 9(2), pages 181-204, julio-dic.
    6. Chang Liu & Lin Tang & Dongtao Lin & Jiayi Guo, 2023. "Testing to extreme: An application of reverse stress testing engineering on mortgages of commercial banks in China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 187-192, January.
    7. Michael B. Imerman, 2020. "When enough is not enough: bank capital and the Too-Big-To-Fail subsidy," Review of Quantitative Finance and Accounting, Springer, vol. 55(4), pages 1371-1406, November.
    8. Giuseppe Montesi & Giovanni Papiro & Massimiliano Fazzini & Alessandro Ronga, 2020. "Stochastic Optimization System for Bank Reverse Stress Testing," JRFM, MDPI, vol. 13(8), pages 1-44, August.
    9. Michael B. Imerman, 0. "When enough is not enough: bank capital and the Too-Big-To-Fail subsidy," Review of Quantitative Finance and Accounting, Springer, vol. 0, pages 1-36.
    10. Ahn, Dohyun & Kim, Kyoung-Kuk & Kwon, Eunji, 2023. "Multivariate stress scenario selection in interbank networks," Journal of Economic Dynamics and Control, Elsevier, vol. 154(C).
    11. Pliszka, Kamil, 2021. "System-wide and banks' internal stress tests: Regulatory requirements and literature review," Discussion Papers 19/2021, Deutsche Bundesbank.

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    More about this item

    Keywords

    Copula functions; Extreme value theory; Principal component analysis; Reverse stress testing;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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