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Optimal Risk Sharing with Optimistic and Pessimistic Decision Makers

Aloisio Araujo, Jean-Marc Bonnisseau, Alain Chateauneuf and Rodrigo Novinski

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

Abstract: We prove that under mild conditions individually rational Pareto optima will exist even in presence of non-convex preferences. We consider decision makers dealing with a countable flow of payoffs or choosing among financial assets whose outcomes depend on the realization of a countable set of states of the world. Our conditions for the existence of Pareto optima can be interpreted as a requirement of impatience in the first context and of some pessimism or not unrealistic optimism in the second context. A non-existence example is provided when, in the second context, some decision maker is too optimistic. We furthermore show that at an individually rational Pareto optimum at most one strictly optimistic decision maker will avoid ruin at each state or date. Considering a risky context this entails that even is risk averters will share risk in a comonotonic way as usual, at most one classical strong risk lover will avoid ruin at each state or date. Finally some examples illustrate circumstances when a risk averter could take advantage of sharing risk with a risk lover rather than with a risk averter.

Keywords: optimistic; Risk sharing; Pareto optimum; Partage de risque; optimum de Pareto; optimiste (search for similar items in EconPapers)
Date: 2015-09
New Economics Papers: this item is included in nep-mic, nep-rmg and nep-upt
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01224491v1
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Citations: View citations in EconPapers (1)

Published in 2015

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Working Paper: Optimal Risk Sharing with Optimistic and Pessimistic Decision Makers (2015) Downloads
Working Paper: Optimal Risk Sharing with Optimistic and Pessimistic Decision Makers (2015) Downloads
Working Paper: Optimal Risk Sharing with Optimistic and Pessimistic Decision Makers (2014) Downloads
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