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Portfolio Optimization Using Higher Moments in an Uncertain Random Environment by Mukesh Kumar Mehlawat; Pankaj Gupta; Ahmad Zaman Khan is a Computer Science article available to read on EtoBox.

What is Portfolio Optimization Using Higher Moments in an Uncertain Random Environment about?

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 semideviation 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.

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Author

Mukesh Kumar Mehlawat; Pankaj Gupta; Ahmad Zaman Khan

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)