Portfolio optimization using higher moments in an uncertain random environment

作     者：Mehlawat, Mukesh Kumar Gupta, Pankaj Khan, Ahmad Zaman 

作者机构：Univ Delhi Dept Operat Res Delhi 110007 India 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2021年第567卷

页      面：348-374页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Council of Scientific and Industrial Research - Human Resource Development Group (CSIR-HRDG) [09/045 (1691) 2019EMRI] Faculty Research Programme scheme of the Institution of Eminence, University of Delhi 

主　　题：Uncertain random variable Chance distribution Multi-objective programming Uncertain measure Portfolio optimization Real-coded genetic algorithm 

摘      要：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. (c) 2021 Elsevier Inc. All rights reserved.