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The Insurance of Low Probability Events

Author

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  • Eeckhoudt, L.
  • Gollier, C.

Abstract

We consider a model in which the agent faces two independant risks of losswith different probabilities of occurence and (possibly) different levels of potential loss. We show that it is optimal to select a deductable for the low probability event that is not larger than the optimal deductable for the other risk. This result holds for any preference functional that satisfies the second-order stochastic dominance property. When the expected loss is the same for the two risks, i.e. when the low probability event is also "catastrophic", it is never optimal not to insure the catastrophic risk when some insurance is purchased for the other risk. We also obtain some additional properties of the optimal insurance strategy in the case of expected utility, or in the case of Yaari's dual theory.

Suggested Citation

  • Eeckhoudt, L. & Gollier, C., 1996. "The Insurance of Low Probability Events," Papers 976.423, Toulouse - GREMAQ.
  • Handle: RePEc:fth:gremaq:976.423
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    Citations

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    Cited by:

    1. Cohen Alma, 2006. "The Disadvantages of Aggregate Deductibles," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 6(1), pages 1-28, April.
    2. Howard Kunreuther & Erwann Michel-Kerjan & Beverly Porter, 2003. "Assessing, Managing, and Financing Extreme Events: Dealing with Terrorism," NBER Working Papers 10179, National Bureau of Economic Research, Inc.
    3. Eric LANGLAIS, 2008. "On Insurance Contract Design For Low Probability Events," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 3(2(4)_Summ).
    4. Christian Gollier, 2005. "Some Aspects of the Economics of Catastrophe Risk Insurance," CESifo Working Paper Series 1409, CESifo Group Munich.

    More about this item

    Keywords

    INFORMATION ; INSURANCE;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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