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Abstract

In this paper, we compare the optimal portfolio weight of mean-variance (MV) method with mean-variance-skewness-kurtosis (MVSK) method. MV is a method to get weight on a portfolio. This method can be developed into the method of MVSK with attention to the higher-order moment of return distribution; skewness and kurtosis. In determining the weight of portfolio is also important to consider the skewness and kurtosis of return distribution. This method of considering the aspect of skewness and kurtosis is called the MVSK method with the aim of maximizing the level of return and skewness and minimizing the risks and exceeding of kurtosis. The result indicate that the optimal portfolio return of all methods is MVSK method with minimize variance priority.

Keywords

mean, variance, skewness, kurtosis, portfolio

Article Details

How to Cite
1.
Agustina D, Sari DP, Winanda RS, Hilmi MR, Fakhriyana D. Comparison of Portfolio Mean-Variance Method with the Mean-Variance-Skewness-Kurtosis Method in Indonesia Stocks. Eksakta [Internet]. 2022Jun.30 [cited 2022Jul.5];23(02):88-97. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/316

References

  1. Zhou, W., Zhu, W., Chen, Y., & Chen, J. (2021). Dynamic changes and multi-dimensional evolution of portfolio optimization. Economic Research-Ekonomska Istraživanja, 0(0), 1–26.
  2. Ortiz, R., Contreras, M., & Mellado, C. (2021). Improving the volatility of the optimal weights of the Markowitz model. Economic Research-Ekonomska Istrazivanja, 0(0), 1–23.
  3. 3. Moradi, M., Sadollah, A., Eskandar, H., & Eskandar, H. (2017). The application of water cycle algorithm to portfolio selection. Economic Research-Ekonomska Istrazivanja, 30(1), 1277–1299.
  4. Yuanyuan Zhang, Xiang Li, S. G. (2018). Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature. Fuzzy Optimization and Decision Making Volume, 7, 125–158.
  5. Kołodziejczyk, B., Mielcarz, P., & Osiichuk, D. (2019). The concept of the real estate portfolio matrix and its application for structural analysis of the Polish commercial real estate market. Economic Research-Ekonomska Istrazivanja, 32(1), 301–320.
  6. Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91.
  7. Turcas, F., Dumiter, F., Brezeanu, P., Farcas, P., & Coroiu, S. (2017). Practical aspects of portfolio selection and optimisation on the capital market. Economic Research-Ekonomska Istrazivanja, 30(1), 14–30.
  8. K. K. Lai, L. Y. and S. W. (2006). Mean-Variance-Skewness-Kurtosis-based Portfolio Optimization. First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS’06), 2, 292–297.
  9. Gotoh, J. ya, Kim, M. J., & Lim, A. E. B. (2018). Robust empirical optimization is almost the same as mean–variance optimization. Operations Research Letters, 46(4), 448–452.
  10. Naqvi, B., Mirza, N., Naqvi, W. A., & Rizvi, S. K. A. (2017). Portfolio optimisation with higher moments of risk at the Pakistan stock exchange. Economic Research-Ekonomska Istrazivanja, 30(1), 1594–1610.
  11. Metaxiotis, K. (2019). A Mean – Variance – Skewness Portfolio Optimization Model. 13(2), 85–88.
  12. Lu, X., Liu, Q., & Xue, F. (2019). Unique closed-form solutions of portfolio selection subject to mean-skewness-normalization constraints. Operations Research Perspectives, 6(2018), 100094.
  13. Khan, K. I., Waqar, S. M., Naqvi, A., & Ghafoor, M. M. (2020). Sustainable Portfolio Optimization with Higher-Order Moments of Risk. 1952, 1–14.
  14. Chen, B., Zhong, J., & Chen, Y. (2020). A hybrid approach for portfolio selection with higher-order moments: Empirical evidence from Shanghai Stock Exchange. Expert Systems with Applications, 145, 113104.
  15. Yaoqi Peng, Yingxin Xiao, Zetian Fu, Yuhong Dong, Yongjun Zheng, Haijun Yan, X. L. (2019). Precision irrigation perspectives on the sustainable water-saving of field crop production in China: Water demand prediction and irrigation scheme optimization. Journal of Cleaner Production, 230, 365–377.
  16. Pahade, J. K., & Jha, M. (2021). Credibilistic variance and skewness of trapezoidal fuzzy variable and mean–variance–skewness model for portfolio selection. Results in Applied Mathematics, 11, 100159.
  17. Marques, J. M. E., & Benasciutti, D. (2020). More on variance of fatigue damage in non-Gaussian random loadings - Effect of skewness and kurtosis. Procedia Structural Integrity, 25(2019), 101–111.
  18. Barillas, F., & Shanken, J. (2018). Comparing Asset Pricing Models. Journal of Finance, 73(2), 715–754.
  19. Díaz, A., Esparcia, C., & López, R. (2022). The diversifying role of socially responsible investments during the COVID-19 crisis : A risk management and portfolio performance analysis. Economic Analysis and Policy, 75, 39–60.
  20. Pollacco, J. A. P., Fernández-Gálvez, J., Ackerer, P., Belfort, B., Lassabatere, L., Angulo-Jaramillo, R., Rajanayaka, C., Lilburne, L., Carrick, S., & Peltzer, D. A. (2022). HyPix: 1D physically based hydrological model with novel adaptive time-stepping management and smoothing dynamic criterion for controlling Newton–Raphson step. Environmental Modelling & Software, 105386.
  21. Dancker, J., & Wolter, M. (2021). Improved quasi-steady-state power flow calculation for district heating systems: A coupled Newton-Raphson approach. Applied Energy, 295(May), 116930.
  22. Gnetchejo, P. J., Ndjakomo Essiane, S., Dadjé, A., & Ele, P. (2021). A combination of Newton-Raphson method and heuristics algorithms for parameter estimation in photovoltaic modules. Heliyon, 7(4).
  23. Witkowska, D., Kompa, K., & Staszak, M. (2021). Indicators for the efficient portfolio construction. The case of Poland. Procedia Computer Science, 192(2021), 2022–2031.
  24. Liesiö, J., Kallio, M., & Argyris, N. (2022). Incomplete risk-preference information in portfolio decision analysis.