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Extreme events, discounting and stochastic optimization

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  • Yuri Ermoliev
  • Tatiana Ermolieva
  • Guenther Fischer
  • Marek Makowski

Abstract

The paper analyzes the implications of extreme events on the proper choice of discounting. Any discounting with constant or declining rates can be linked to random “stopping time” events, which define the internal discount-related horizons of evaluations. Conversely, any stopping time induces a discounting, in particular, with the standard discount rates. The expected duration of the stopping time horizon for discount rates obtained from capital markets does not exceed a few decades and, as such, these rates may significantly underestimate the net benefits of long-term decisions. The alternative undiscounted stopping time criterion allows to induce social discounting focusing on arrival times of potential extreme events rather then horizons of market interests. Induced discount rates are conditional on the degree of social commitment to mitigate risk. In general, extreme events affect these rates, which alter the optimal mitigation efforts that, in turn, change events. The use of undiscounted stopping time criteria requires stochastic optimisation methods. Copyright Springer Science+Business Media, LLC 2010

Suggested Citation

  • Yuri Ermoliev & Tatiana Ermolieva & Guenther Fischer & Marek Makowski, 2010. "Extreme events, discounting and stochastic optimization," Annals of Operations Research, Springer, vol. 177(1), pages 9-19, June.
  • Handle: RePEc:spr:annopr:v:177:y:2010:i:1:p:9-19:10.1007/s10479-009-0606-4
    DOI: 10.1007/s10479-009-0606-4
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    References listed on IDEAS

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    1. Y.M. Ermoliev & T.Y. Ermolieva & G.J. MacDonald & V.I. Norkin, 2000. "Stochastic Optimization of Insurance Portfolios for Managing Exposure to Catastrophic Risks," Annals of Operations Research, Springer, vol. 99(1), pages 207-225, December.
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    5. Y. Ermoliev & L. Hordijk, 2006. "Facets of Robust Decisions," Lecture Notes in Economics and Mathematical Systems, in: Coping with Uncertainty, pages 3-28, Springer.
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    Cited by:

    1. Emilio L. Cano & Javier M. Moguerza & Tatiana Ermolieva & Yurii Yermoliev, 2017. "A strategic decision support system framework for energy-efficient technology investments," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 249-270, July.
    2. Anna Timonina, 2015. "Multi-stage stochastic optimization: the distance between stochastic scenario processes," Computational Management Science, Springer, vol. 12(1), pages 171-195, January.
    3. Ludovic Gaudard & Franco Romerio, 2015. "Natural hazard risk in the case of an emergency: the real options’ approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 473-488, January.

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