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Are Risk Averse Agents More Optimistic? A Bayesian Estimation Approach

Elyès Jouini (), Selima Ben Mansour, Clotilde NAPP, Marin, Jean-Michel and Christian Robert
Additional contact information
Selima Ben Mansour: DRM - Dauphine Recherches en Management - CNRS : UMR7088 - Université Paris Dauphine - Paris IX
Marin, Jean-Michel: INRIA Saclay - Ile de France - SELECT - INRIA - Université Paris Sud - Paris XI - CNRS : UMR
Christian Robert: CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - CNRS : UMR7534 - Université Paris Dauphine - Paris IX

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Abstract: Our aim is to analyze the link between optimism and risk aversion in a subjective expected utility setting and to estimate the average level of optimism when weighted by risk tolerance. This quantity is of particular importance since it characterizes the consensus belief in risk-taking situations with heterogeneous beliefs. Its estimation leads to a nontrivial statistical problem. We start from a large lottery survey (1,536 individuals). We assume that individuals have true unobservable characteristics and that their answers in the survey are noisy realizations of these characteristics. We adopt a Bayesian approach for the statistical analysis of this problem and use an hybrid MCMC approximation method to numerically estimate the distributions of the unobservable characteristics. We obtain that individuals are on average pessimistic and that pessimism and risk tolerance are positively correlated. As a consequence, we conclude that the consensus belief is biased towards pessimism.

Keywords: Bayesian estimation; MCMC scheme; importance sampling; pessimism; risk tolerance; risk aversion; consensus belief (search for similar items in EconPapers)
Date: 2007-10-04
Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00176629/en/

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Related works:
Working Paper: Are risk averse agents more optimistic? A Bayesian estimation approach (2007) Downloads
Journal Article: Are risk-averse agents more optimistic? A Bayesian estimation approach (2008) Downloads
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