6533b838fe1ef96bd12a3dc3
RESEARCH PRODUCT
A naïve approach to speed up portfolio optimization problem using a multiobjective genetic algorithm
J. Samuel Baixauli-solerMatilde O. Fernández-blancoEva Alfaro-cidsubject
Economics and EconometricsMathematical optimizationSpeedupAlgoritmo genéticoComputer scienceStrategy and ManagementComputationValue‑at‑RiskLarge rangelcsh:BusinessValue¿at¿Riskddc:650Genetic algorithmEconometricsG11Business and International ManagementMarketingValue-at-RiskEfficient frontierQuartileEfficient portfolioGenetic algorithmValor en riesgovalue.at.RiskC81Portfolio optimization problemlcsh:HF5001-6182Cartera eficienteLENGUAJES Y SISTEMAS INFORMATICOSdescription
a b s t r a c t 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. © 2011 AEDEM. Publicado por Elsevier Espana, S.L. Todos los derechos reservados.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2012-05-01 |