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A coherent framework for stress-testing

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  • Jeremy Berkowitz

Abstract

In recent months and years both practitioners and regulators have embraced the ideal of supplementing VaR estimates with "stress-testing". Risk managers are beginning to place an emphasis and expend resources on developing more and better stress-tests. In the present paper, we hold the standard approach to stress-testing up to a critical light. The current practice is to stress-test outside the basic risk model. Such an approach yields two sets of forecasts -- one from the stress-tests and one from the basic model. The stress scenarios, conducted outside the model, are never explicitly assigned probabilities. As such, there is no guidance as to the importance or revelance of the results of stress-tests. Moreover, how to combine the two forecasts into a usable risk metric is not known. Instead, we suggest folding the stress-tests into the risk model, thereby requiring all scenarios to be assigned probabilities.

Suggested Citation

  • Jeremy Berkowitz, 1999. "A coherent framework for stress-testing," Finance and Economics Discussion Series 1999-29, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:1999-29
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    References listed on IDEAS

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    1. Jeremy Berkowitz, 1999. "Evaluating the forecasts of risk models," Finance and Economics Discussion Series 1999-11, Board of Governors of the Federal Reserve System (U.S.).
    2. Brian H. Boyer & Michael S. Gibson & Mico Loretan, 1997. "Pitfalls in tests for changes in correlations," International Finance Discussion Papers 597, Board of Governors of the Federal Reserve System (U.S.).
    3. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
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    Cited by:

    1. Diebold, Francis X. & Yilmaz, Kamil, 2015. "Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring," OUP Catalogue, Oxford University Press, number 9780199338306.
    2. W. Scott Frame & Lawrence J. White, 2009. "Technological Change, Financial Innovation, and Diffusion in Banking," Working Papers 09-03, New York University, Leonard N. Stern School of Business, Department of Economics.
    3. Guoyin Li & Alfred Ma & Ting Pong, 2014. "Robust least square semidefinite programming with applications," Computational Optimization and Applications, Springer, vol. 58(2), pages 347-379, June.
    4. Alexander N. Bogin & Nataliya Polkovnichenko & William M. Doerner, 2015. "Additional Market Risk Shocks: Prepayment Uncertainty and Option-Adjusted Spreads," Staff Working Papers 15-03, Federal Housing Finance Agency.
    5. Vazquez, Francisco & Tabak, Benjamin M. & Souto, Marcos, 2012. "A macro stress test model of credit risk for the Brazilian banking sector," Journal of Financial Stability, Elsevier, vol. 8(2), pages 69-83.
    6. Basu, Sanjay, 2011. "Comparing simulation models for market risk stress testing," European Journal of Operational Research, Elsevier, vol. 213(1), pages 329-339, August.
    7. Martin Cihak, 2004. "Stress Testing: A Review of Key Concepts," Research and Policy Notes 2004/02, Czech National Bank, Research Department.
    8. Alexander, Carol & Sheedy, Elizabeth, 2008. "Developing a stress testing framework based on market risk models," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2220-2236, October.
    9. Dominique Guegan & Bertrand Hassani, 2014. "Stress Testing Engineering: the real risk measurement?," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00951593, HAL.
    10. So, Mike K.P. & Wong, Jerry & Asai, Manabu, 2013. "Stress testing correlation matrices for risk management," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 310-322.
    11. Matthias Meyer & Cathérine Grisar & Felix Kuhnert, 2011. "The impact of biases on simulation-based risk aggregation: modeling cognitive influences on risk assessment," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 22(1), pages 79-105, September.

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    Keywords

    Risk management ; Risk assessment;

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