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Testing Theories of Choice under Risk: Estimation of Individual Functionals

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  • Blondel, Serge

Abstract

The method introduced here allows us to use a data set with a non-restricted number of outcomes, here 21. Hence, our method complements the other ones developed in the domain of the probability triangle. Individual parameters are estimated for expected utility and various non-expected utility theories. We use CRRA and CARA utility functions, both without and with the assumption of weakly concavity. Rank-dependent utility, prospective reference and cognitive consistency theories emerge from the others. Copyright 2002 by Kluwer Academic Publishers

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  • Blondel, Serge, 2002. "Testing Theories of Choice under Risk: Estimation of Individual Functionals," Journal of Risk and Uncertainty, Springer, vol. 24(3), pages 251-265, May.
    • Handle: RePEc:kap:jrisku:v:24:y:2002:i:3:p:251-65
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    1. Serge Blondel, 2003. "Généralisation de l’espérance d’utilité : le cas des jeux de loterie en France," Économie et Prévision, Programme National Persée, vol. 159(3), pages 105-112.
    2. Jakusch, Sven Thorsten, 2017. "On the applicability of maximum likelihood methods: From experimental to financial data," SAFE Working Paper Series 148, Leibniz Institute for Financial Research SAFE, revised 2017.
    3. Arjun Chatrath & Rohan A. Christie‐David & Hong Miao & Sanjay Ramchander, 2019. "Losers and prospectors in the short‐term options market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(6), pages 721-743, June.
    4. Birgit Löhndorf & Anna-Lena Sachs & Rudolf Vetschera, 2014. "Stability of probability effects in utility elicitation," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(4), pages 755-777, December.
    5. Ehrhart, Karl-Martin & Ott, Marion & Abele, Susanne, 2008. "Auction fever : theory and experimental evidence," Papers 08-27, Sonderforschungsbreich 504.
    6. Chao Gong & Chunhui Xu & Ji Wang, 2018. "An Efficient Adaptive Real Coded Genetic Algorithm to Solve the Portfolio Choice Problem Under Cumulative Prospect Theory," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 227-252, June.
    7. Jakusch, Sven Thorsten & Meyer, Steffen & Hackethal, Andreas, 2019. "Taming models of prospect theory in the wild? Estimation of Vlcek and Hens (2011)," SAFE Working Paper Series 146, Leibniz Institute for Financial Research SAFE, revised 2019.
    8. Henry Stott, 2006. "Cumulative prospect theory's functional menagerie," Journal of Risk and Uncertainty, Springer, vol. 32(2), pages 101-130, March.
    9. Peter P. Wakker, 2023. "The correct formula of 1979 prospect theory for multiple outcomes," Theory and Decision, Springer, vol. 94(2), pages 183-187, February.

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