The paper relates cumulative prospect theory to the moments of returns distributions, e.g. skewness and kurtosis, assuming returns are normal inverse Gaussian distributed. The normal inverse Gaussian distribution parametrizes the first- to forth-order moments, making the investigation straightforward. Cumulative prospect theory utility is found to be positively related to the skewness. However, the relation is negative when probability weighing is set aside. This shows that cumulative prospect theory investors display a preference for skewness through the probability weighting function. Furthermore, the investors utility is inverse hump-shape related to the kurtosis. Consequences for portfolio choice issues are studied. The findings, among others, suggest that optimal cumulative prospect theory portfolios are not meanvariance efficient under the normal inverse Gaussian distribution.
cumulative prospect theory skewness kurtosis normal inverse Gaussian distribution portfolio choice