Paper Info
Title
Portfolio optimization using higher moments in an uncertain random environment
Abstract
In this paper, a multi-objective portfolio optimization problem is studied in an uncertain random environment using higher moments. We consider a scenario involving an asset universe wherein some assets have sufficient historical return data for modelling as random variables, and others, listed relatively recently, lack historical data. The asset returns of such assets are modelled as uncertain variables. Thus, a hybrid environment involving both uncertain and random variables is considered. We use mean absolute semi-deviation as a risk measure and employ skewness (i.e., third moment) in the portfolio optimization model. The expressions for the mean absolute semi-deviation and skewness of an uncertain random variable have been derived. We show that the derivation of mean absolute semi-deviation is not based on any stipulation and thus is an accurate measure of risk (unlike the variance of an uncertain random variable, which uses stipulation). We propose a hybrid genetic algorithm as a solution methodology and provide empirical proofs to illustrate its advantages. The proposed methodology has been applied to a case study involving 100 assets listed in the NASDAQ-100 index of the U.S. stock market. We do an ex-post analysis to track the performance of our model out-of-sample and illustrate its advantages.
Year
DOI
Venue
2021
10.1016/j.ins.2021.03.019
Information Sciences
Keywords
DocType
Volume
Uncertain random variable,Chance distribution,Multi-objective programming,Uncertain measure,Portfolio optimization,Real-coded genetic algorithm
Journal
567
ISSN
Citations 
PageRank 
0020-0255
2
0.37
References 
Authors
0
3
Name
Order
Citations
PageRank
Mukesh Kumar Mehlawat127522.90
Pankaj Gupta21479133.85
Ahmad Zaman Khan320.71