Original Research Article

Article volume = 2022 and issue = 2

Pages: 149–153

Article publication Date: November 21, 2022

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Geometric Portfolio Optimization based on Skewness Maximization and Monte Carlo Simulation

Parastoo Kabi-Nejad

Iran University of Science and Technology, Tehran, Iran.

Abstract:

Optimum portfolio selection is one of the financial issues that has been studied in various ways in recent decades. One of the important factors in the portfolio selection is to consider the behavior of returns distribution. In this paper, it is assumed that returns are skew normal distribution, and under skewness with risk optimum portfolio is examined. Optimality and simulation results considering the skewness showed that this method is more effective than Markowitz traditional method.

Keywords:

Portfolio optimization, Geometric mean return, Monte Carlo simulation - Skew-normal distribution.

References:
  • [1] A, Azzalini , and ,R.B Arellano -Valle, The centered parameterization and related quantities of the skew-t distribution, Jour. of Multiva.Analy, 113 (2013), 73–90
  • [2] B. Chen, Shih-Feng Huang, G. Pan, High dimensional mean–variance optimization through factor analysis, Journal of Multivariate Analysis, Volume 133, January (2015), 140–159
  • [3] C. B. Hunzinger, C. C. A. Labuschagne, Sven T. von Boetticher, Volatility skews of indexes of BRICS securities exchanges, Procedia Economics and Finance 14(2014), 263–272
  • [4] C. Jiang, Y. Ma, Y. An, Portfolio selection with a systematic skewness constraint, The North American Journal of Economics and Finance, Volume 37,(2016) Pages 393-405
  • [5] D. Huang, S. Zhu, F. J. Fabozzi, M. Fukushima, Portfolio selection under distributional uncertainty: A relative robust CVaR approach, European Journal of Operational Research, Volume 203, (2010), 185–194
  • [6] J. Li, J. Xu, Multi-objective portfolio selection model with fuzzy random returns and a compromise approachbased genetic algorithm, Information Sciences, , January 2013 Volume 220, 20, 507–521
  • [7] M. Zhang, J. Nan, G. Yuan, The Geometric Portfolio Optimization with Semivariance in Financial Engineering, Systems Engineering Procedia 3 (2012), 217– 221
  • [8] M. Liu, Y. Gao,An algorithm for portfolio selection in a frictional market, Applied Mathematics and Computation, Volume 182, Issue 2, 15 November (2006), 1629–1638
  • [9] R. Bhattacharyya, S. Ahmed Hossain, S. Kar, Fuzzy cross-entropy, mean, variance, skewness models for portfolio selection, Journal of King Saud University - Computer and Information Sciences, Volume 26, Issue 1, (2014), 79–87
  • [10] S. k. ashour , M. A. Abdel-hameed, Approximate skew normal distribution, journal of advanced research (2010), 341–350.
  • [11] S. Dedu, A. Toma, An Integrated Risk Measure And Information Theory Approach For Modeling Financial Data And Solving Decision Making , 2nd International Conference ‘Economic Scientific Research - Theoretical, Empirical and Practical Approaches’, ESPERA 2014, 13-14 November 2014, Bucharest, Romania, Procedia Economics and Finance 22 (2015 ), 531 – 537
  • [12] S. Dedu, F. Şerban, Multi objective Mean-Risk Models for Optimization in Finance and Insurance, Procedia Economics and Finance, Volume 32, 2015, Pages 973–980
  • [13] S. Benati, R. Rizzi, A mixed integer linear programming formulation of the optimal mean/Value-at-Risk portfolio problem, European Journal of Operational Research, January (2007), Volume 176, Issue 1, 423–434
  • [14] V. Platon, A. Constantinescu, Monte Carlo Method in risk analysis for investment projects, Procedia Economics and Finance 15(2015) 393-400
  • [15] W. J. Gutjahr, S. Katzensteiner, P. Reiter, C. Stummer, M. Denk, Multi-objective decision analysis for competence-oriented project portfolio selection, European Journal of Operational Research, September (2010), Volume 205, Issue 3, 670–679
  • [16] Y. H. Shin, B. Hwa Lim, Comparison of optimal portfolios with and without subsistence consumption constraints, Nonlinear Analysis: Theory, Methods and Applications , Volume 74, Issue 1,(2011) 1 50–58
  • [17] X. Huang, Mean–variance models for portfolio selection subject to experts’ estimations, Expert Systems with Applications, April 2012, Volume 39, Issue 5, (2012) 5887–5893
  • [18] X. Huang, Portfolio selection with a new definition of risk, European Journal of Operational Research, Volume 186, Issue 1, (2008) 351–357
Cite this article as:
  • Parastoo Kabi-Nejad, Geometric Portfolio Optimization based on Skewness Maximization and Monte Carlo Simulation, Communications in Combinatorics, Cryptography & Computer Science, 2022(2), PP.149–153, 2022
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