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Stressing dynamic loss models

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  • Kroell, Emma
  • Pesenti, Silvana M.
  • Jaimungal, Sebastian

Abstract

Stress testing, and in particular, reverse stress testing, is a prominent exercise in risk management practice. Reverse stress testing, in contrast to (forward) stress testing, aims to find an alternative but plausible model such that under that alternative model, specific adverse stresses (i.e. constraints) are satisfied. Here, we propose a reverse stress testing framework for dynamic models. Specifically, we consider a compound Poisson process over a finite time horizon and stresses composed of expected values of functions applied to the process at the terminal time. We then define the stressed model as the probability measure under which the process satisfies the constraints and which minimizes the Kullback-Leibler divergence to the reference compound Poisson model.

Suggested Citation

  • Kroell, Emma & Pesenti, Silvana M. & Jaimungal, Sebastian, 2024. "Stressing dynamic loss models," Insurance: Mathematics and Economics, Elsevier, vol. 114(C), pages 56-78.
  • Handle: RePEc:eee:insuma:v:114:y:2024:i:c:p:56-78
    DOI: 10.1016/j.insmatheco.2023.11.002
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    References listed on IDEAS

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    Cited by:

    1. Sebastian Jaimungal & Silvana M. Pesenti, 2024. "Kullback-Leibler Barycentre of Stochastic Processes," Papers 2407.04860, arXiv.org.

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    More about this item

    Keywords

    Reverse stress testing; Compound Poisson processes; KL divergence; Value-at-Risk; Conditional Value-at-Risk;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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