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Model Uncertainty and Scenario Aggregation

Author

Listed:
  • Mathieu CAMBOU

    (Ecole Polytechnique Fédérale de Lausanne)

  • Damir FILIPOVIC

    (Ecole Polytechnique Fédérale de Lausanne and Swiss Finance Institute)

Abstract

This paper provides a coherent method for scenario aggregation addressing model uncertainty. It is based on divergence minimization from a reference probability measure subject to scenario constraints. An example from regulatory practice motivates the definition of five fundamental criteria that serve as a basis for our method. Standard risk measures, such as value-at-risk and expected shortfall, are shown to be robust with respect to minimum divergence scenario aggregation. Various examples illustrate the tractability of our method.

Suggested Citation

  • Mathieu CAMBOU & Damir FILIPOVIC, 2014. "Model Uncertainty and Scenario Aggregation," Swiss Finance Institute Research Paper Series 14-38, Swiss Finance Institute, revised Nov 2015.
    • Handle: RePEc:chf:rpseri:rp1438
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    File URL: http://ssrn.com/abstract=2441328
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    Cited by:

    1. Daniel Bartl & Ludovic Tangpi, 2020. "Non-asymptotic convergence rates for the plug-in estimation of risk measures," Papers 2003.10479, arXiv.org, revised Oct 2022.

    More about this item

    Keywords

    model uncertainty; scenario aggregation; expected shortfall; value-at-risk; statistical divergence; Swiss Solvency Test;
    All these keywords.

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