The shape of the utility function under risk in the loss domain and the 'ruinous losses' hypothesis: some experimental results
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- Nathalie Etchart-Vincent, 2009. "The shape of the utility function under risk in the loss domain and the "ruinous losses" hypothesis: some experimental results," Economics Bulletin, AccessEcon, vol. 29(2), pages 1393-1402.
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Citations
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Cited by:
- Kpegli, Yao Thibaut & Corgnet, Brice & Zylbersztejn, Adam, 2023.
"All at once! A comprehensive and tractable semi-parametric method to elicit prospect theory components,"
Journal of Mathematical Economics, Elsevier, vol. 104(C).
- Yao Thibaut Kpegli & Brice Corgnet & Adam Zylbersztejn, 2020. "All at Once! A Comprehensive and Tractable Semi-Parametric Method to Elicit Prospect Theory Components," Working Papers 2034, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
- Yao Thibaut Kpegli & Brice Corgnet & Adam Zylbersztejn, 2020. "All at Once! A Comprehensive and Tractable Semi-Parametric Method to Elicit Prospect Theory Components," Working Papers halshs-03016517, HAL.
- Kontek, Krzysztof, 2011. "What is the actual shape of perception utility?," MPRA Paper 31715, University Library of Munich, Germany.
- Nathalie Etchart-Vincent, 2009.
"Probability weighting and the ‘level’ and ‘spacing’ of outcomes: An experimental study over losses,"
Journal of Risk and Uncertainty, Springer, vol. 39(1), pages 45-63, August.
- Nathalie Etchart-Vincent, 2009. "Probability weighting and the 'level' and 'spacing' of outcomes: An experimental study over losses," Post-Print hal-00395876, HAL.
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More about this item
Keywords
utility under risk; large losses; ruin; trade-off method; individual decision making under risk;All these keywords.
JEL classification:
- D8 - Microeconomics - - Information, Knowledge, and Uncertainty
- C9 - Mathematical and Quantitative Methods - - Design of Experiments
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