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Comparative Ross Risk Aversion in the Presence of Mean Dependent Risks

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  • Georges Dionne
  • Jingyuan Li
Abstract
This paper studies comparative risk aversion between risk averse agents in the presence of a background risk. Although the literature covers this question extensively, our contribution differs from most of the literature in two respects. First, background risk does not need to be additive or multiplicative. Second, the two risks are not necessary mean independent, and may be conditional expectation increasing or decreasing. We show that our order of cross Ross risk aversion is equivalent to the order of partial risk premium, while our index of decreasing cross Ross risk aversion is equivalent to decreasing partial risk premium. These results generalize the comparative risk aversion model developed by Ross (1981) for mean independent risks. Finally, we show that decreasing cross Ross risk aversion gives rise to the utility function family belonging to the class of n-switch utility functions.

Suggested Citation

  • Georges Dionne & Jingyuan Li, 2012. "Comparative Ross Risk Aversion in the Presence of Mean Dependent Risks," Cahiers de recherche 1211, CIRPEE.
    • Handle: RePEc:lvl:lacicr:1211
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    References listed on IDEAS

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    2. Dionne, Georges & Harrington, Scott, 2017. "Insurance and Insurance Markets," Working Papers 17-2, HEC Montreal, Canada Research Chair in Risk Management.
    3. Wong, Kit Pong, 2021. "Comparative risk aversion with two risks," Journal of Mathematical Economics, Elsevier, vol. 97(C).
    4. Jiehua Xie & Zhengyong Zhou, 2022. "Patchwork Constructions of Multiattribute Utility Functions," Decision Analysis, INFORMS, vol. 19(2), pages 141-169, June.

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    More about this item

    Keywords

    Comparative cross Ross risk aversion; Dependent background risk; Partial risk premium; Decreasing cross Ross risk aversion; n-switch utility function;
    All these keywords.

    JEL classification:

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

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