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Systematic Systemic Stress Tests

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Abstract

For a given set of banks, which economic and financial scenarios will lead to big losses? How big can losses in such scenarios possibly get? These are the two central questions of macro stress tests. We believe that most current macro stress testing models have deficits in answering these questions. They select stress scenarios in a way which might leave aside many dangerous scenarios and thus create an illusion of safety; and which might consider highly implausible scenarios and thus trigger a false alarm. With respect to loss evaluation most stress tests do not include tools to analyse systemic risk arising from the interactions of banks with each other and with the markets. We make a conceptual proposal how these shortcomings may be addressed and how stress tests could be made both systematic and systemic. We demonstrate the application of our concepts using publicly available data on European banks and capital markets, in particular the EBA 2016 stress test results.

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  • Thomas Breuer & Martin Summer, 2018. "Systematic Systemic Stress Tests," Working Papers 225, Oesterreichische Nationalbank (Austrian Central Bank).
  • Handle: RePEc:onb:oenbwp:225
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    References listed on IDEAS

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    1. Yann Braouezec & Lakshithe Wagalath, 2016. "Risk-based capital requirements and optimal liquidation in a stress scenario," Working Papers 2016-ACF-01, IESEG School of Management.
    2. Martin Hellwig, 2009. "Systemic Risk in the Financial Sector: An Analysis of the Subprime-Mortgage Financial Crisis," De Economist, Springer, vol. 157(2), pages 129-207, June.
    3. Breuer, Thomas & Csiszár, Imre, 2013. "Systematic stress tests with entropic plausibility constraints," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1552-1559.
    4. Rama Cont & Lakshithe Wagalath, 2012. "Fire Sales Forensics: Measuring Endogenous Risk," Working Papers hal-00697224, HAL.
    5. Mark D. Flood & George G. Korenko, 2015. "Systematic scenario selection: stress testing and the nature of uncertainty," Quantitative Finance, Taylor & Francis Journals, vol. 15(1), pages 43-59, January.
    6. Fernando Duarte & Thomas M. Eisenbach, 2021. "Fire‐Sale Spillovers and Systemic Risk," Journal of Finance, American Finance Association, vol. 76(3), pages 1251-1294, June.
    7. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    8. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    9. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
    10. Thomas Breuer & Imre Csiszár, 2016. "Measuring Distribution Model Risk," Mathematical Finance, Wiley Blackwell, vol. 26(2), pages 395-411, April.
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    Cited by:

    1. Bonucchi, Manuel & Catalano, Michele, 2022. "How severe are the EBA macroeconomic scenarios for the Italian Economy? A joint probability approach," Journal of International Money and Finance, Elsevier, vol. 129(C).

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    More about this item

    Keywords

    Stress Testing; Risk Measures; Scenario Analysis; Systemic Risk;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • G01 - Financial Economics - - General - - - Financial Crises
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • M48 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Government Policy and Regulation

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