This paper presents the description of the efficient frontier for a portfolio made of three assets. We use data analysis to obtain three clusters, then, we estimate the risk of each asset corresponding to each class we obtained. Thus, we get the best three assets among the ones we analyzed and for which we will construct the efficient frontier. The originality of our paper consists in the combination of classification theory and risk estimation theory to determine the best assets. To illustrate the efficiency of the method we used, we present a case study which makes reference to the stocks listed at BSE. We construct the efficient frontier based on the existent correlation of the best analyzed stocks that we obtained by data analyses (for classification), and by the evaluation of the loss repartition (for risk estimation)

Portfolio optimization and building of its efficient frontier / Serban, F; Stefanescu, M. V.; Ferrara, Massimiliano. - In: ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH. - ISSN 0424-267X. - 45(2):(2011), pp. 125-137.

Portfolio optimization and building of its efficient frontier

FERRARA, Massimiliano
2011-01-01

Abstract

This paper presents the description of the efficient frontier for a portfolio made of three assets. We use data analysis to obtain three clusters, then, we estimate the risk of each asset corresponding to each class we obtained. Thus, we get the best three assets among the ones we analyzed and for which we will construct the efficient frontier. The originality of our paper consists in the combination of classification theory and risk estimation theory to determine the best assets. To illustrate the efficiency of the method we used, we present a case study which makes reference to the stocks listed at BSE. We construct the efficient frontier based on the existent correlation of the best analyzed stocks that we obtained by data analyses (for classification), and by the evaluation of the loss repartition (for risk estimation)
2011
risk, selection of assets, principal components analysis,
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/7853
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