The skewness for uncertain random variable and application to portfolio selection problem

Bo Li; Yadong Shu

doi:10.3934/jimo.2020163

Article Contents

2022, Volume 18, Issue 1: 457-467. Doi: 10.3934/jimo.2020163

This issue Previous Article Solving fuzzy linear fractional set covering problem by a goal programming based solution approach Next Article Second-Order characterizations for set-valued equilibrium problems with variable ordering structures

The skewness for uncertain random variable and application to portfolio selection problem

Bo Li^1, , and
Yadong Shu^2,

1.
School of Applied Mathematics, Nanjing University of Finance and Economics, Nanjing 210023, China
2.
School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China

^* Corresponding author: Bo Li
^* Corresponding author: Bo Li

Received: June 2020

Revised: August 2020

Early access: November 2020

Published: January 2022

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Abstract

Uncertainty and randomness are two basic types of indeterminacy, where uncertain variable is used to represent quantities with human uncertainty and random variable is applied for modeling quantities with objective randomness. In many real systems, uncertainty and randomness often exist simultaneously. Then uncertain random variable and chance measure can be used to handle such cases. We know that the skewness is a measure of distributional asymmetry. However, the concept of skewness for uncertain random variable has not been clearly defined. In this paper, we first propose a concept of skewness for uncertain random variable and then present a formula for calculating the skewness via chance distribution. Applying the presented formula, the skewnesses of three special uncertain random variables are derived. Finally, a portfolio selection problem is carried out for showing the efficiency and applicability of skewness and presented formula.

Keywords:

Mathematics Subject Classification: 91B06(Decision theory).

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Table 1. The computational results for different $ p $ and $ q $

$ (p, q) $	$ (x_{1}^{}, x_{2}^{}) $	Expected value	Variance	Skewness
$ (0.04, 0.2) $	$ (0, 1) $	$ 0.04 $	0.0075	$ 1.125\times10^{-4} $
$ (0.04, 0.5) $	$ (0, 1) $	$ 0.04 $	0.0075	$ 1.125\times10^{-4} $
$ (0.04, 0.8) $	$ (0, 1) $	$ 0.04 $	0.0075	$ 1.125\times10^{-4} $
$ (0.02, 0.2) $	$ (0, 1) $	$ 0.04 $	0.0075	$ 1.125\times10^{-4} $
$ (0.02, 0.5) $	$ (0, 1) $	$ 0.04 $	0.0075	$ 1.125\times10^{-4} $
$ (0.02, 0.8) $	$ (0, 1) $	$ 0.04 $	0.0075	$ 1.125\times10^{-4} $
$ (0.01, 0.2) $	$ (0, 1) $	$ 0.04 $	0.0075	$ 1.125\times10^{-4} $
$ (0.01, 0.5) $	$ (0, 1) $	$ 0.04 $	0.0075	$ 1.125\times10^{-4} $
$ (0.01, 0.8) $	$ (0, 1) $	$ 0.04 $	0.0075	$ 1.125\times10^{-4} $

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References

[1]	H. Ahmadzade, Y. Sheng and F. Hassantabar Darzi, Some results of moments of uncertain random variables, Iran. J. Fuzzy Syst., 14 (2017), 1-21.
[2]	R. Bhattacharyya, A. Chatterjee and S. Kar, Mean-variance-skewness portfolio selection model in general uncertain environment, Indian J. Ind. Appl. Math., 3 (2012), 45-61.
[3]	W. Briec, K. Kerstens and I. Van de Woestyne, Portfolio selection with skewness: A comparison of methods and a generalized one fund result, Eur. J. Oper. Res., 230 (2013), 412-421. doi: 10.1016/j.ejor.2013.04.021.
[4]	A. Chatterjee, R. Bhattacharyya, S. Mukherjee and S. Kar, Optimization of mean-semivariance-skewness portfolio selection model in fuzzy random environment, ICOMOS 2010, American Institute of Physics conference proceedings, 1298 (2010), 516-521. doi: 10.1063/1.3516359.
[5]	W. Chen, Y. Wang, P. Gupta and M. K. Mehlawat, A novel hybrid heuristic algorithm for a new uncertain mean-variance-skewness portfolio selection model with real constraints, Appl. Intell., 48 (2018), 2996-3018. doi: 10.1007/s10489-017-1124-8.
[6]	Y. Chen and Y. Zhu, Indefinite LQ optimal control with process state inequality constraints for discrete-time uncertain systems, J. Ind. Manag. Optim., 14 (2018), 913-930. doi: 10.3934/jimo.2017082.
[7]	A. Fernandez-Perez, B. Frijns, A. M. Fuertes and J. Miffre, The skewness of commodity futures returns, J. Bank. Financ., 86 (2018), 143-158.
[8]	R. Gao and D. A. Ralescu, Elliptic entropy of uncertain set and its applications, Int. J. Intell. Syst., 33 (2018), 836-857. doi: 10.1002/int.21970.
[9]	X. Huang and H. Ying, Risk index based models for portfolio adjusting problem with returns subject to experts' evaluations, Econ. Model., 30 (2013), 61-66.
[10]	R. G. Ibbotson, Price performance of common stock new issues, J. Financ. Econ., 2 (1975), 235-272. doi: 10.1016/0304-405X(75)90015-X.
[11]	A. Kolmogorov, Grundbegriffe Der Wahrscheinlichkeitsrechnung, Julius Springer, Berlin, 1933.
[12]	H. Kwakernaak, Fuzzy random variables-Ⅰ: Definitions and theorems, Inform. Sciences, 15 (1978), 1-29. doi: 10.1016/0020-0255(78)90019-1.
[13]	H. Kwakernaak, Fuzzy random variables-Ⅱ: Algorithms and examples for the discrete case, Inform. Sciences, 17 (1979), 253-278. doi: 10.1016/0020-0255(79)90020-3.
[14]	D. Kahneman and A. Tversky, Prospect theory: An analysis of decision under risk, Econometrica, 47 (1979), 263-292. doi: 10.2307/1914185.
[15]	X. Li, Z. Qin and K. Kar, Mean-variance-skewness model for portfolio selection with fuzzy returns, Eur. J. Oper. Res., 202 (2010), 239-247.
[16]	B. Liu, Uncertainty Theory, Second ed., Springer-Verlag, Berlin, 2007.
[17]	B. Liu, Uncertainty Theory: A Branch of Mathematics for Modeling Human Uncertainty, Springer-Verlag, Berlin, 2004. doi: 10.1007/978-3-540-39987-2.
[18]	B. Li, Y. Sun, G. Aw and K. L. Teo, Uncertain portfolio optimization problem under a minimax risk measure, Appl. Math. Model., 76 (2019), 274-281. doi: 10.1016/j.apm.2019.06.019.
[19]	Y. Liu, Uncertain random variables: A mixture of uncertainty and randomness, Soft Comput., 17 (2013), 625-634. doi: 10.1007/s00500-012-0935-0.
[20]	B. Liu, Some research problems in uncertainty theory, J. Uncertain Syst., 3 (2009), 3-10.
[21]	Y. Liu, Uncertain random programming with applications, Fuzzy Optim. Decis. Ma., 12 (2013), 153-169. doi: 10.1007/s10700-012-9149-2.
[22]	H. M. Markowitz, Portfolio selection, J. Financ., 7 (1952), 77-91.
[23]	A. J. Prakash, C. H. Chang and T. E. Pactwa, Selecting a portfolio with skewness: Recent evidence from US, European and Latin American equity markets, J. Bank. Financ., 27 (2003), 1375-1390.
[24]	Z. Qin, Mean-variance model for portfolio optimization problem in the simultaneous presence of random and uncertain returns, Eur. J. Oper. Res., 245 (2015), 480-488. doi: 10.1016/j.ejor.2015.03.017.
[25]	W. Xu, G. Liu, H. Li and W. Luo, A study on project portfolio models with skewness risk and staffing, Int. J. Fuzzy Syst., 19 (2017), 2033-2047. doi: 10.1007/s40815-017-0295-0.
[26]	H. Yan, Y. Sun and Y. Zhu, A linear-quadratic control problem of uncertain discrete-time switched systems, J. Ind. Manag. Optim., 13 (2017), 267-282. doi: 10.3934/jimo.2016016.
[27]	X. Yang and J. Gao, Linear-quadratic uncertain differential games with application to resource extraction problem, IEEE Trans. Fuzzy Syst., 24 (2016), 819-826. doi: 10.1109/TFUZZ.2015.2486809.
[28]	T. Ye and Y. Zhu, A metric on uncertain variables, Int. J. Uncertain. Quan., 8 (2018), 251-266. doi: 10.1615/Int.J.UncertaintyQuantification.2018020455.
[29]	J. Zhai, M. Bai and H. Wu, Mean-risk-skewness models for portfolio optimization based on uncertain measure, Optimization, 67 (2018), 701-714. doi: 10.1080/02331934.2018.1426577.
[30]	L. A. Zadeh, Fuzzy sets, Inform. Control, 8 (1965), 338-353. doi: 10.1016/S0019-9958(65)90241-X.