Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

All at Once! A Comprehensive and Tractable Semi-Parametric Method to Elicit Prospect Theory Components

Abstract : Eliciting all the components of prospect theory-curvature of the utility function, weighting function and loss aversion-remains an open empirical challenge. We develop a semi-parametric method that keeps the tractability of parametric methods while providing more precise estimates. Using the data of Tversky and Kahneman (1992), we revisit their main parametric results. We reject the convexity of the utility function in the loss domain, find lower probability weighting, and confirm loss aversion. We also report that the probability weighting function does not exhibit duality and equality across domains, in line with cumulative prospect theory and in contrast with original prospect and rank dependent utility theories.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

https://halshs.archives-ouvertes.fr/halshs-03016517
Contributor : Nelly Wirth <>
Submitted on : Friday, November 20, 2020 - 2:16:23 PM
Last modification on : Wednesday, November 25, 2020 - 9:42:25 AM

File

2034.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : halshs-03016517, version 1

Citation

Yao Kpegli, Brice Corgnet, Adam Zylbersztejn. All at Once! A Comprehensive and Tractable Semi-Parametric Method to Elicit Prospect Theory Components. 2020. ⟨halshs-03016517⟩

Share

Metrics

Record views

84

Files downloads

39