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Stochastic Generalized Gradient Method with Application to Insurance Risk Management

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  • Y.M. Ermoliev
  • V.I. Norkin

Abstract

Recently we analyzed important classes of nonsmooth and nonconvex risk control problems which can not be solved by standard optimization techniques. The aim of this article is to develop computational procedures enabling us to bypass some of the obstacles identified in this paper. We illustrate this by using insurance risk processes with insolvency (stopping time).

Suggested Citation

  • Y.M. Ermoliev & V.I. Norkin, 1997. "Stochastic Generalized Gradient Method with Application to Insurance Risk Management," Working Papers ir97021, International Institute for Applied Systems Analysis.
  • Handle: RePEc:wop:iasawp:ir97021
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    File URL: http://www.iiasa.ac.at/Publications/Documents/IR-97-021.pdf
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    References listed on IDEAS

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    1. G. Guerkan & A.Y. Oezge & S.M. Robinson, 1994. "Sample-Path Optimization in Simulation," Working Papers wp94070, International Institute for Applied Systems Analysis.
    2. Ermoliev, Yu. & Keyzer, M. A. & Norkin, V., 2000. "Global convergence of the stochastic tatonnement process," Journal of Mathematical Economics, Elsevier, vol. 34(2), pages 173-190, October.
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    Cited by:

    1. T.Y. Ermolieva, 1997. "The Design of Optimal Insurance Decisions in the Presence of Catastrophic Risks," Working Papers ir97068, International Institute for Applied Systems Analysis.

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