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Integrating Stress Scenarios into Risk Quantification Models

Author

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  • Azamat Abdymomunov
  • Sharon Blei
  • Bakhodir Ergashev

Abstract

We enhance the method of integrating scenarios proposed in Ergashev (J Financ Serv Res 41(3):145–161, 2012) into risk models. In particular, we provide additional theoretical insights of the method with focus on stress testing Value-at-Risk models. We extend the application of the method, which is originally proposed for scenario analysis in the operational risk context, to market and credit risks. We provide detailed application guidance of the method for market, credit, and operational risks. The method (i) ensures that a stressed model produces a higher risk estimate than the model based on historical data only and (ii) does not require assumptions on stressed loss distributions, thereby simplifying the scenario generation process. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Azamat Abdymomunov & Sharon Blei & Bakhodir Ergashev, 2015. "Integrating Stress Scenarios into Risk Quantification Models," Journal of Financial Services Research, Springer;Western Finance Association, vol. 47(1), pages 57-79, February.
    • Handle: RePEc:kap:jfsres:v:47:y:2015:i:1:p:57-79
    DOI: 10.1007/s10693-014-0194-6
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    References listed on IDEAS

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    1. Chib, Siddhartha, 2001. "Markov chain Monte Carlo methods: computation and inference," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 57, pages 3569-3649, Elsevier.
    2. Thomas Breuer & Martin Jandacka & Klaus Rheinberger & Martin Summer, 2009. "How to Find Plausible, Severe and Useful Stress Scenarios," International Journal of Central Banking, International Journal of Central Banking, vol. 5(3), pages 205-224, September.
    3. Maria Stepanova & Lyn Thomas, 2002. "Survival Analysis Methods for Personal Loan Data," Operations Research, INFORMS, vol. 50(2), pages 277-289, April.
    4. Bakhodir Ergashev, 2012. "A Theoretical Framework for Incorporating Scenarios into Operational Risk Modeling," Journal of Financial Services Research, Springer;Western Finance Association, vol. 41(3), pages 145-161, June.
    5. 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.
    6. J-K Im & D W Apley & C Qi & X Shan, 2012. "A time-dependent proportional hazards survival model for credit risk analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(3), pages 306-321, March.
    7. anonymous, 2001. "Guidance on risk management of leveraged financing," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), issue Jun, pages 413-414.
    8. Garthwaite, Paul H. & Kadane, Joseph B. & O'Hagan, Anthony, 2005. "Statistical Methods for Eliciting Probability Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 680-701, June.
    9. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    10. 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.
    11. Kiefer, Nicholas M., 2010. "Default Estimation and Expert Information," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 320-328.
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    Citations

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    Cited by:

    1. Dominique Gu�gan & Bertrand Hassani & Kehan Li, 2015. "The Spectral Stress VaR (SSVaR)," Working Papers 2015:17, Department of Economics, University of Venice "Ca' Foscari".
    2. Dominique Guegan & Bertrand K. Hassani & Kehan Li, 2015. "The Spectral Stress VaR (SSVaR)," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01169537, HAL.
    3. Zi-Yi Guo, 2017. "A Model of Plausible, Severe and Useful Stress Scenarios for VIX Shocks," Applied Economics and Finance, Redfame publishing, vol. 4(3), pages 155-163, May.
    4. Dominique Guegan & Bertrand K. Hassani & Kehan Li, 2015. "The Spectral Stress VaR (SSVaR)," Documents de travail du Centre d'Economie de la Sorbonne 15052, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    5. 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.
    6. 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.
    7. Azamat Abdymomunov & Filippo Curti, 2020. "Quantifying and Stress Testing Operational Risk with Peer Banks’ Data," Journal of Financial Services Research, Springer;Western Finance Association, vol. 57(3), pages 287-313, June.
    8. Dominique Guegan & Bertrand K. Hassani & Kehan Li, 2015. "The Spectral Stress VaR (SSVaR)," Post-Print halshs-01169537, HAL.

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    More about this item

    Keywords

    Stress test; Scenarios; VaR; Interest rate risk; Operational risk; Credit risk; G32; G21; G20;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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