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The mean-Value at Risk static portfolio optimization using genetic algorithm
Computer Science and Information Systems, Tome 11 (2014) no. 1 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

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In this paper we solve the problem of static portfolio allocation based on historical Value at Risk (VaR) by using genetic algorithm (GA). VaR is a predominantly used measure of risk of extreme quantiles in modern finance. For estimation of historical static portfolio VaR, calculation of time series of portfolio returns is required. To avoid daily recalculations of proportion of capital invested in portfolio assets, we introduce a novel set of weight parameters based on proportion of shares. Optimal portfolio allocation in the VaR context is computationally very complex since VaR is not a coherent risk metric while number of local optima increases exponentially with the number of securities. We presented two different single-objective and a multiobjective technique for generating mean–VaR efficient frontiers. Results document good risk/reward characteristics of solution portfolios while there is a trade-off between the ability to control diversity of solutions and computation time.
Keywords: Genetic algorithm, Static portfolio optimization, Value at Risk, Mean-VaR efficient frontier 
@article{CSIS_2014_11_1_a8,
     author = {Vladimir Rankovi\'c and Mikica Drenovak and Boban Stojanovi\'c and Zoran Kalini\'c and Zora Arsovski},
     title = {The {mean-Value} at {Risk} static portfolio optimization using genetic algorithm},
     journal = {Computer Science and Information Systems},
     year = {2014},
     volume = {11},
     number = {1},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2014_11_1_a8/}
}
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AU  - Mikica Drenovak
AU  - Boban Stojanović
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%J Computer Science and Information Systems
%D 2014
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%U http://geodesic.mathdoc.fr/item/CSIS_2014_11_1_a8/
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Vladimir Ranković; Mikica Drenovak; Boban Stojanović; Zoran Kalinić; Zora Arsovski. The mean-Value at Risk static portfolio optimization using genetic algorithm. Computer Science and Information Systems, Tome 11 (2014) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2014_11_1_a8/