This paper develops a semiparametric estimation method that jointly identifies the probability weighting and utility … rank-dependent utility model. We nonparametrically estimate the probability weighting function using the kernel density of … returns over the period 1996-2023 reveal the relevance of probability weighting, in particular at the monthly horizon where …

This paper develops a semiparametric estimation method that jointly identifies the probability weighting and utility … rank-dependent utility model. We nonparametrically estimate the probability weighting function using the kernel density of … returns over the period 1996-2023 reveal the relevance of probability weighting, in particular at the monthly horizon where …

probability weighting (w(p) function), we estimate structural models with two-parameter Prelec probability weighting functions to … probability weighting functions. …

decision errors are correlated with the strong inverse-S-shaped probability weighting (w(p)) patterns in our two samples. We … future. The probabilities of winning in the risky future prospects varied and facilitated the estimation of probability … weighting functions for the risky prospects placed 6 and 12 months into the future. The experiment is used to test whether …

theoretical framework is developed to demonstrate that: i) probability weighting and ambiguity attitude can lead individuals to …

decision errors are correlated with the strong inverse-S-shaped probability weighting (w(p)) patterns in our two samples. We … future. The probabilities of winning in the risky future prospects varied and facilitated the estimation of probability … weighting functions for the risky prospects placed 6 and 12 months into the future. The experiment is used to test whether …

probability weighting (w(p) function), we estimate structural models with two-parameter Prelec probability weighting functions to … probability weighting functions. …