The mean-Value at Risk static portfolio optimization using genetic algorithm

Vladimir Ranković1, Mikica Drenovak1, Boban Stojanović2, Zoran Kalinić1 and Zora Arsovski1

  1. Faculty of Economics, University of Kragujevac
    Djure Pucara 3, 34000 Kragujevac, Serbia
    vladimir.rankovich@gmail.com, mikica.drenovak@yahoo.com, zkalinic@kg.ac.rs, zora@kg.ac.rs
  2. Faculty of Science, Department of Mathematics and Informatics, University of Kragujevac
    Radoja Domanovica 12, 34000 Kragujevac, Serbia
    boban.stojanovic@gmail.com

Abstract

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.

Key words

Genetic algorithm, Static portfolio optimization, Value at Risk, Mean-VaR efficient frontier

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS121024017R

Publication information

Volume 11, Issue 1 (January 2014)
Year of Publication: 2014
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Ranković, V., Drenovak, M., Stojanović, B., Kalinić, Z., Arsovski, Z.: The mean-Value at Risk static portfolio optimization using genetic algorithm. Computer Science and Information Systems, Vol. 11, No. 1, 89-109. (2014)