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Worst case risk measurement: Back to the future?

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Author Info

  • Goovaerts, Marc J.
  • Kaas, Rob
  • Laeven, Roger J.A.

Abstract

This paper studies the problem of finding best-possible upper bounds on a rich class of risk measures, expressible as integrals with respect to measures, under incomplete probabilistic information. Both univariate and multivariate risk measurement problems are considered. The extremal probability distributions, generating the worst case scenarios, are also identified.

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File URL: http://www.sciencedirect.com/science/article/pii/S0167668711000746
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Bibliographic Info

Article provided by Elsevier in its journal Insurance: Mathematics and Economics.

Volume (Year): 49 (2011)
Issue (Month): 3 ()
Pages: 380-392

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Handle: RePEc:eee:insuma:v:49:y:2011:i:3:p:380-392

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Web page: http://www.elsevier.com/locate/inca/505554

Related research

Keywords: Risk measurement; Generalized scenarios; Worst case scenario; Cones; Linear programming; Value-at-Risk; Tail-Value-at-Risk; Exponential premium;

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Cited by:
  1. Wong, Man Hong & Zhang, Shuzhong, 2013. "Computing best bounds for nonlinear risk measures with partial information," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 204-212.
  2. Goovaerts, Marc & Linders, Daniël & Van Weert, Koen & Tank, Fatih, 2012. "On the interplay between distortion, mean value and Haezendonck–Goovaerts risk measures," Insurance: Mathematics and Economics, Elsevier, vol. 51(1), pages 10-18.
  3. McNeil, Alexander J. & Smith, Andrew D., 2012. "Multivariate stress scenarios and solvency," Insurance: Mathematics and Economics, Elsevier, vol. 50(3), pages 299-308.

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