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Stress Testing Engineering: The Real Risk Measurement?

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Future Perspectives in Risk Models and Finance

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.

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Notes

  1. 1.

    In October 2012, U.S. regulators unveiled new rules expanding this practice by requiring the largest American banks to undergo stress tests twice per year, once internally and once conducted by the regulators.

  2. 2.

    \( VaR_{\alpha} (X) = q_{1-\alpha}= F_{X}^{-1}(\alpha)\).

  3. 3.

    A low number of degrees of freedom imply a higher dependence in the tail of the marginal distributions.

  4. 4.

    http://www.standardandpoors.com/ratings/articles/en/us/?articleType=HTML&assetID=1245350156739.

  5. 5.

    The maturity adjustment is not always present as it is contingent to the type of credit.

  6. 6.

    Note that the ES obtained from the NIG is far superior to the initial investment, but is still consistent regarding a continously coumpounded portfolio.

  7. 7.

    This section presents the methodologies applied to weekly time series, as presented in the result section. They have also been applied to monthly time series.

  8. 8.

    Maximum Likelihood Estimation.

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Correspondence to Dominique Guégan .

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Guégan, D., Hassani, B. (2015). Stress Testing Engineering: The Real Risk Measurement?. In: Bensoussan, A., Guegan, D., Tapiero, C. (eds) Future Perspectives in Risk Models and Finance. International Series in Operations Research & Management Science, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-319-07524-2_3

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