IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-319-07524-2_3.html
   My bibliography  Save this book chapter

Stress Testing Engineering: The Real Risk Measurement?

In: Future Perspectives in Risk Models and Finance

Author

Listed:
  • Dominique Guégan

    (University Paris 1 Panthéon-Sorbonne et New York University Polytechnic School of Engineering)

  • Bertrand K. Hassani

    (Université Paris 1 Panthéon-Sorbonne CES UMR 8174 and Grupo Santander)

Abstract

Stress testing is used to determine the stability or the resilience of a given financial institution by deliberately submitting the subject to intense and particularly adverse conditions which has not been considered a priori. This exercise does not imply that the entity’s failure is imminent, though its purpose is to address and prepare this potential failure. Consequently, as the focal point is a concept (Risk) the stress testing is the quintessence of risk management. In this chapter we focus on what may lead a bank to fail and how its resilience can be measured. Two families of triggers are analysed: the first stands in the impact of external (and/or extreme) events, the second one stands on the impacts of the choice of inadequate models for predictions or risks measurement; more precisely on models becoming inadequate with time because of not being sufficiently flexible to adapt themselves to dynamical changes. The first trigger needs to take into account fundamental macro-economic data or massive operational risks while the second trigger deals with the limitations of the quantitative models for forecasting, pricing, evaluating capital or managing the risks. It may be argued that if inside the banks-limitations, pitfalls and other drawbacks of models used were correctly identified, understood and handled, and if the associated products were correctly known, priced and insured, then the effects of the crisis may not have had so important impacts on the real economy. In other words, the appropriate model should be able to capture real risks (including in particular extreme events) at any point in time, or ultimately a model management strategy should be considered to switch from a model to another during extreme market conditions.

Suggested Citation

  • Dominique Guégan & Bertrand K. Hassani, 2015. "Stress Testing Engineering: The Real Risk Measurement?," International Series in Operations Research & Management Science, in: Alain Bensoussan & Dominique Guegan & Charles S. Tapiero (ed.), Future Perspectives in Risk Models and Finance, edition 127, pages 89-124, Springer.
  • Handle: RePEc:spr:isochp:978-3-319-07524-2_3
    DOI: 10.1007/978-3-319-07524-2_3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. 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.
    2. 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".
    3. Dominique Guegan & Bertrand K. Hassani & Kehan Li, 2015. "The Spectral Stress VaR (SSVaR)," Post-Print halshs-01169537, HAL.
    4. 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.

    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:spr:isochp:978-3-319-07524-2_3. 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.

    We have no bibliographic references for this item. You can help adding them by using 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://www.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.