IDEAS home Printed from https://ideas.repec.org/p/wop/iasawp/ir97021.html
   My bibliography  Save this paper

Stochastic Generalized Gradient Method with Application to Insurance Risk Management

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.iiasa.ac.at/Publications/Documents/IR-97-021.pdf
    Download Restriction: no

    File URL: http://www.iiasa.ac.at/Publications/Documents/IR-97-021.ps
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Emelogu, Adindu & Chowdhury, Sudipta & Marufuzzaman, Mohammad & Bian, Linkan & Eksioglu, Burak, 2016. "An enhanced sample average approximation method for stochastic optimization," International Journal of Production Economics, Elsevier, vol. 182(C), pages 230-252.
    2. Fei, Xin & Gülpınar, Nalân & Branke, Jürgen, 2019. "Efficient solution selection for two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 277(3), pages 918-929.
    3. Keyzer, Michiel & van Wesenbeeck, Lia, 2005. "Equilibrium selection in games: the mollifier method," Journal of Mathematical Economics, Elsevier, vol. 41(3), pages 285-301, April.
    4. Paul Oslington, 2012. "General Equilibrium: Theory and Evidence," The Economic Record, The Economic Society of Australia, vol. 88(282), pages 446-448, September.
    5. Flåm, S.D. & Godal, O., 2008. "Market clearing and price formation," Journal of Economic Dynamics and Control, Elsevier, vol. 32(3), pages 956-977, March.
    6. Rosen, Scott L. & Harmonosky, Catherine M. & Traband, Mark T., 2007. "A simulation optimization method that considers uncertainty and multiple performance measures," European Journal of Operational Research, Elsevier, vol. 181(1), pages 315-330, August.
    7. Flåm, S. D. & Ermoliev, Y. M., 2009. "Investment, uncertainty, and production games," Environment and Development Economics, Cambridge University Press, vol. 14(1), pages 51-66, February.
    8. W D A Bryant, 2009. "General Equilibrium:Theory and Evidence," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6875, August.
    9. Flåm, Sjur Didrik & Gaasland, Ivar & Vårdal, Erling, 2006. "On Stabilizing or Deregulating Food Prices," Working Papers in Economics 08/06, University of Bergen, Department of Economics.
    10. Juan, Angel A. & Faulin, Javier & Grasman, Scott E. & Rabe, Markus & Figueira, Gonçalo, 2015. "A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems," Operations Research Perspectives, Elsevier, vol. 2(C), pages 62-72.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wop:iasawp:ir97021. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thomas Krichel (email available below). General contact details of provider: https://edirc.repec.org/data/iiasaat.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.