Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/191030 
Erscheinungsjahr: 
2012
Quellenangabe: 
[Journal:] Investigaciones Europeas de Dirección y Economía de la Empresa (IEDEE) [ISSN:] 1135-2523 [Volume:] 18 [Issue:] 2 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2012 [Pages:] 126-131
Verlag: 
Elsevier, Amsterdam
Zusammenfassung: 
Genetic algorithms (GAs) are appropriate when investors have the objective of obtaining mean.variance (VaR) efficient frontier as minimising VaR leads to non.convex and non.differential risk.return optimisation problems. However GAs are a time.consuming optimisation technique. In this paper, we propose to use a naive approach consisting of using samples split by quartile of risk to obtain complete efficient frontiers in a reasonable computation time. Our results show that using reduced problems which only consider a quartile of the assets allow us to explore the efficient frontier for a large range of risk values. In particular, the third quartile allows us to obtain efficient frontiers from the 1.8% to 2.5% level of VaR quickly, while that of the first quartile of assets is from 1% to 1.3% level of VaR.
Schlagwörter: 
efficient portfolio
genetic algorithm
value.at.Risk
JEL: 
G11
C81
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