RESEARCH ARTICLE


Portfolio Analysis of Investments in Risk Management



D.S. Hooda1, M. Stehlík2, *
1 Jaypee University of Engineering Technology, A.B. Road, Raghogarh-473226 Distt. Guna-M.P, India
2 Department of Applied Statistics, Johannes Kepler University, Freistädter Straße 315, 2. Stock A-4040 Linz a. D. Linz, Austria


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© 2011 Hooda et al.;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Applied Statistics, Johannes Kepler University, Freistädter Straße 315, 2. Stock A-4040 Linz a. D. Linz, Austria. Tel: +43 732 2468 5881; Fax: +43 732 2468 9846; E-mail: Milan.Stehlik@jku.at


Abstract

In many practical investment situations the amount of available memory on stock data is extremely huge. Thus many investors are attracted to base their decisions on the information "currently available in their minds" (see [1, 2]). In the present paper various risk measurement models having application in the investment management are discussed. First we explain the concept of mean variance efficient frontier and Markowitz’s model to find all efficient portfolios that maximize the expected returns and minimize the risk. Markovian risk measures are also mentioned. Some measures of portfolio analysis based on entropy mean-variance frontier are studied. Risk aversion index and Pareto-optimal sharing of risk are explained. In view of these facts it is very interesting to study how the investor should make investments so that his total expected return is maximized and the risk of losing his capital is minimized. A maximum entropy model in risk sharing is proposed and applied to some problems.

Keywords: Portfolio, Markowitz’s model, Mean-Variance, Entropy..