People care about higher statistical moments in decision making under uncertainty
Decision-making models and financial analyses frequently account for higher statistical moments, such as skewness and kurtosis, to better handle uncertainty.
The claim is specific and empirical, dealing with behavioral and mathematical decision-making under uncertainty. The retrieved papers, particularly [0], [1], and [2], provide evidence that higher-order moments like skewness and kurtosis are incorporated into decision frameworks and utility functions. The other papers are off-topic clinical or educational studies.
Emmanuel Jurczenko, Bertrand B. Maillet, Paul M. Merlin. Hedge Funds Portfolio Selection with Higher-Order Moments: A Non-Parametric Mean-Variance-Skewness-Kurtosis Efficient Frontier. 2005. https://doi.org/10.2139/ssrn.676904
Paper [0] demonstrates that portfolio selection models optimize choices by incorporating higher-order moments such as skewness and kurtosis.
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Xueying Yu, Chuancun Yin. Some results on multivariate measures of elliptical and skew-elliptical distributions: higher-order moments, skewness and kurtosis. 2023. https://doi.org/10.3934/math.2023370
Paper [1] discusses the importance of higher-order moments like skewness and kurtosis in characterizing return distributions.
Wolf-Dieter Richter. Skewness-Kurtosis Controlled Higher Order Equivalent Decisions. 2016. https://doi.org/10.2174/1876527001607010001
Paper [2] evaluates how skewness and kurtosis parameters influence decision equivalence and asymptotic tests under uncertainty.
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