Uncertain random mean–variance–skewness models for the portfolio optimization problem

Zhai, Jia; Bai, Manying; Hao, Junzhang

doi:10.1080/02331934.2021.1928122

Cited by 8 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Qin [31] proposed for the first time a mean-variance model for portfolio optimization with uncertain random returns. Zhai et al [37] used skewness as the risk measure for the portfolio selection problem in an uncertain random environment. Li et al [18] studied the concept of skewness of an uncertain random variable and incorporated it into the portfolio selection problem.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Uncertain random portfolio selection with high order moments

Amor¹,

Chennaf

2023

JIMO

View full text Add to dashboard Cite

<p style='text-indent:20px;'>In financial phenomena, investors are confronted with a hybrid uncertainty, where random and uncertain returns can occur simultaneously. This paper studies the kurtosis of an uncertain random variable and its application in portfolio selection. We are aware that the kurtosis measures the extreme values in each tail of the distribution. However, the notion of kurtosis for uncertain random variables has not been properly established. Thus, we introduce the concept of kurtosis of an uncertain random variable and extract some significant properties. Next, by invoking chance distribution we derive the kurtosis of three particular uncertain random variables, as well as the variance. Then, we present an uncertain random mean-variance-skewness-kurtosis portfolio selection model with their corresponding variations to meet various investors' requirements. Finally, two numerical examples are presented along with a comparative study to explain and illustrate the main results.</p>

show abstract

mentioning

confidence: 99%

“…The aim of this model is to minimize risk, where γ, δ and η are the levels of expected return, skewness and kurtosis, respectively. If the last constraint does not exist, then the above model degenerates into an uncertain random mean-varianceskewness model proposed by Zhai et al [37].…”

mentioning

confidence: 99%