Skip to main content
Log in

Possibilistic Moment Models for Multi-period Portfolio Selection with Fuzzy Returns

  • Published:
Computational Economics Aims and scope Submit manuscript

Abstract

The aim of this paper is to investigate the effects of higher moments on multi-period portfolio selection with fuzzy returns. This paper gives the definitions of possibilistic mean and variance about the product of multiple fuzzy numbers. Based on these definitions, three multi-period fuzzy portfolio optimization models are proposed. The proposed models aim to maximize terminal wealth and minimize terminal risk by taking into account some realistic constraints including higher moments, budget constraint, round-lot constraint, cardinality constraint and bound constraint. To ensure the selection of the best solutions, a novel fuzzy programming approach-based self-adaptive differential evolution algorithm is designed to solve the proposed models. A numerical example is given to demonstrate the application of the proposed models. Computational results show that the designed algorithm is effective for solving complex portfolio selection model with realistic constraints.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Ammar, E., & Khalifa, H. A. (2003). Fuzzy portfolio optimization: A quadratic programming approach. Chaos, Solitons & Fractals, 18(5), 1045–1054.

    Article  Google Scholar 

  • Ballestero, E., Günther, M., Pla-Santamaria, D., & Stummer, C. (2007). Portfolio selection under strict uncertainty: A multi-criteria methodology and its application to the Frankfurt and Vienna stock exchanges. European Journal of Operational Research, 181(3), 1476–1487.

    Article  Google Scholar 

  • Bellman, R., & Zadeh, L. A. (1970). Decision making in a fuzzy environment. Management Science, 17(4), 141–164.

    Article  Google Scholar 

  • Briec, W., Kerstens, K., & Jokung, O. (2007). Mean–variance–skewness portfolio performance gauging: A general shortage function and dual approach. Management Science, 53(1), 135–149.

    Article  Google Scholar 

  • Carli, R., Dotoli, M., Pellegrino, R., & Ranieri, L. (2017). A decision making technique to optimize a buildings’ stock energy efficiency. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(5), 794–807.

    Article  Google Scholar 

  • Carlsson, C., & Fullér, R. (2001). On possibilistic mean value and variance of fuzzy numbers. Fuzzy Sets and Systems, 122(2), 315–326.

    Article  Google Scholar 

  • Chen, W. (2015). Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem. Physica A, 429, 125–139.

    Article  Google Scholar 

  • Chen, W., & Tan, S. (2009). On the possibilistic mean value and variance of multiplication of fuzzy numbers. Journal of Computational and Applied Mathematics, 232(2), 327–334.

    Article  Google Scholar 

  • Cheng, C. B. (2004). Group opinion aggregation based on a grading process: A method for constructing triangular fuzzy numbers. Computers & Mathematics with Applications, 48(10–11), 1619–1632.

    Article  Google Scholar 

  • Devi, B. B., & Sarma, V. V. S. (1985). Estimation of fuzzy memberships from histograms. Information Sciences, 35(1), 43–59.

    Article  Google Scholar 

  • Díaz, A., González, M. D. L. O., Navarro, E., & Skinner, F. S. (2009). An evaluation of contingent immunization. Journal of Banking & Finance, 33(10), 1874–1883.

    Article  Google Scholar 

  • DiTraglia, F. J., & Gerlach, J. R. (2013). Portfolio selection: An extreme value approach. Journal of Banking & Finance, 37(2), 305–323.

    Article  Google Scholar 

  • Dubios, D., & Prade, H. (1980). Fuzzy sets and system: Theory and application. New York: Academic Press.

    Google Scholar 

  • Fang, H., & Lai, T. Y. (1997). Go-kurtosis and capital asset pricing. Financial Review, 32(2), 293–307.

    Article  Google Scholar 

  • Ge, B., Hipel, K. W., Fang, L., Yang, K., & Chen, Y. (2014). An interactive portfolio decision analysis approach for system-of-systems architecting using the graph model for conflict resolution. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(10), 1328–1346.

    Article  Google Scholar 

  • Gong, C., Xu, C., Wang, J. (2017). An efficient adaptive real coded genetic algorithm to solve the portfolio choice problem under cumulative prospect theory. Computational Economics, 1C26 (in press).

  • Ghosh, A., Das, S., Chowdhury, A., & Giri, R. (2011). An improved differential evolution algorithm with fitness-based adaptation of the control parameters. Information Sciences, 181(18), 3749–3765.

    Article  Google Scholar 

  • Guo, S., Yu, L., Li, X., & Kar, S. (2016). Fuzzy multi-period portfolio selection with different investment horizons. European Journal of Operational Research, 254(3), 1026–1035.

    Article  Google Scholar 

  • Gupta, P., Mehlawat, M. K., & Saxena, A. (2008). Asset portfolio optimization using fuzzy mathematical programming. Information Sciences, 178(6), 1734–1755.

    Article  Google Scholar 

  • Hjalmarsson, E., & Manchev, P. (2012). Characteristic-based mean–variance portfolio choice. Journal of Banking & Finance, 36(5), 1392–1401.

    Article  Google Scholar 

  • Jalota, H., Thakur, M., & Mittal, G. (2017). Modelling and constructing membership function for uncertain portfolio parameters: A credibilistic framework. Expert Systems with Applications, 71, 40–56.

    Article  Google Scholar 

  • Kim, T. (2015). Does individual-stock skewness/coskewness reflect portfolio risk? Finance Research Letters, 15, 167–174.

    Article  Google Scholar 

  • Kumar, R., & Bhattacharya, S. (2012). Cooperative search using agents for cardinality constrained portfolio selection problem. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 42(6), 1510–1518.

    Article  Google Scholar 

  • Leung, M. T., Daouk, H., & Chen, A. S. (2001). Using investment portfolio returns to combine forecasts: A multi-objective approach. European Journal of Operational Research, 134(1), 84–102.

    Article  Google Scholar 

  • Li, X., Guo, S., & Yu, L. (2015). Skewness of Fuzzy Numbers and Its Applications in Portfolio Selection. IEEE Transactions on Fuzzy Systems, 23(6), 2135–2143.

    Article  Google Scholar 

  • Li, J., Liu, G., Yan, C., & Jiang, C. (2017). Robust learning to rank based on portfolio theory and AMOSA algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 77(6), 1007–1018.

    Article  Google Scholar 

  • Li, X., Qin, Z. F., & Kar, S. (2010). Mean–variance–skewness model for portfolio selection with fuzzy returns. European Journal of Operational Research, 202(1), 239–247.

    Article  Google Scholar 

  • Liu, L., Shenoy, C., & Shenoy, P. P. (2006). Knowledge representation and integration for portfolio evaluation using linear belief functions. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 36(4), 774–785.

    Article  Google Scholar 

  • Liu, Y. J., & Zhang, W. G. (2013). Fuzzy portfolio optimization model under real constraints. Insurance: Mathematics and Economics, 53(3), 704–711.

    Google Scholar 

  • Mashayekhi, Z., & Omrani, H. (2016). An integrated multi-objective Markowitz-DEA cross-efficiency model with fuzzy returns for portfolio selection problem. Applied Soft Computing, 38, 1–9.

    Article  Google Scholar 

  • Mehlawat, M. K. (2016). Credibilistic mean-entropy models for multi-period portfolio selection with multi-choice aspiration levels. Information Sciences, 345, 9–26.

    Article  Google Scholar 

  • Mohamed, A. W., & Sabry, H. Z. (2012). Constrained optimization based on modified differential evolution algorithm. Information Sciences, 194, 171–208.

    Article  Google Scholar 

  • Saborido, R., Ruiz, A. B., Bermúdez, J. D., Vercher, E., & Luque, M. (2016). Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection. Applied Soft Computing, 39, 48–63.

    Article  Google Scholar 

  • Saeidifar, A., & Pasha, E. (2009). The possibilistic moments of fuzzy numbers and their applications. Journal of Computational and Applied Mathematics, 223(2), 1028–1042.

    Article  Google Scholar 

  • Shen, Y. (2015). Mean–variance portfolio selection in a complete market with unbounded random coefficients. Automatica, 55, 165–175.

    Article  Google Scholar 

  • Storn, R., & Price, K. (1995). Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report TR-95-012. International Computer Science Institute, Berkeley, CA.

  • Utz, S., Wimmer, M., Hirschberger, M., & Steuer, R. E. (2014). Tri-criterion inverse portfolio optimization with application to socially responsible mutual funds. European Journal of Operational Research, 234(2), 491–498.

    Article  Google Scholar 

  • Wu, L. H., & Wang, Y. N. (2007). Self-adapting control parameters modified differential evolution for trajectory planning manipulator. Control Theory and Technology, 5(4), 365–373.

    Article  Google Scholar 

  • Yu, J. R., & Lee, W. Y. (2011). Portfolio rebalancing model using multiple criteria. European Journal of Operational Research, 209(2), 166–175.

    Article  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

    Article  Google Scholar 

  • Zhang, W. G., Liu, Y. J., & Xu, W. J. (2012). A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs. European Journal of Operational Research, 222(2), 341–349.

    Article  Google Scholar 

  • Zhang, W. G., Xiao, W. L., & Xu, W. J. (2010a). A possibilistic portfolio adjusting model with new added assets. Economic Modelling, 27(1), 208–213.

    Article  Google Scholar 

  • Zhang, X. L., Zhang, W. G., Xu, W. J., & Xiao, W. L. (2010b). Possibilistic approaches to portfolio selection problem with general transaction costs and a CLPSO algorithm. Computational Economics, 36(3), 191–200.

    Article  Google Scholar 

  • Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45–55.

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Nos. 71501076 and 71720107002), the Natural Science Foundation of Guangdong Province of China (No. 2017A030312001), the Fundamental Research Funds for the Central Universities (No. 2017ZD102) and Guangzhou Financial Services Innovation and Risk Management Research Base.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong-Jun Liu.

Appendix

Appendix

Proof

(i) By Definition 1, we have \(M^{-}(A_{i})={\int }_{0}^{1}\gamma \underline{a}_{i}(\gamma )\text {d}\gamma \), \(M^{+}(A_{i})={\int }_{0}^{1}\gamma \overline{a}_{i}(\gamma )\text {d}\gamma \) and \(E(A_{i})=\tfrac{1}{2}(M^{-}(A_{i})+M^{+}(A_{i}))\). Then, by Eq. (7), we have

$$\begin{aligned} E\left[ \prod \limits _{i = 1}^n A_{i}\right]&=\int _{0}^{1}\ldots \int _{0}^{1}\gamma _{1}\ldots \gamma _{n}\left[ \prod \limits _{i = 1}^n\underline{a}_{i}(\gamma _{i})+\sum \limits _{k=1}^{n}\overline{a}_{k}(\gamma _{k})\mathop {\mathop {\mathop {\prod }\limits _{i= 1}} \limits _{i \ne k}}^n\underline{a}_{i}(\gamma _{i})\right. \nonumber \\&\quad \left. +\,\mathop {\mathop {\mathop {\sum }\limits _{k=1}}\limits _{k<j}}^{n}\overline{a}_{k}(\gamma _{k})\overline{a}_{j}(\gamma _{j}) \mathop {\mathop {\mathop {\mathop {\prod }\limits _{i = 1}}\limits _{ i \ne j}}\limits _{i \ne k}}^n\underline{a}_{i}(\gamma _{i})+\sum \limits _{k=1}^{n}\underline{a}_{k}(\gamma _{k}) \mathop {\mathop {\mathop {\prod }\limits _{i = 1}} \limits _{i \ne k}}^n\overline{a}_{i}(\gamma _{i})+\prod \limits _{i = 1}^n\overline{a}_{i}(\gamma _{i})\right] \text {d}\gamma _{1}\ldots \text {d}\gamma _{n}\\&= \prod \limits _{i = 1}^n\int _{0}^{1}\gamma _{i}\underline{a}_{i}(\gamma _{i})\text {d}\gamma _{i}+\sum \limits _{k=1}^{n}\int _{0}^{1}\gamma _{k}\overline{a}_{k}(\gamma _{k})\text {d}\gamma _{k}\mathop {\mathop {\mathop {\prod }\limits _{i = 1}}\limits _{i \ne k}}^n\int _{0}^{1}\gamma _{i}\underline{a}_{i}(\gamma _{i})\text {d}\gamma _{i}\nonumber \\&\quad +\,\mathop {\mathop {\mathop {\sum }\limits _{k=1}}\limits _{k<j}}^{n}\int _{0}^{1}\gamma _{k}\overline{a}_{k}(\gamma _{k})\text {d}\gamma _{k}\int _{0}^{1}\gamma _{j}\overline{a}_{j}(\gamma _{j})\text {d}\gamma _{j} \mathop {\mathop {\mathop {\mathop {\prod }\limits _{i = 1}} \limits _{ i \ne j}}\limits _{i \ne k }}^n\int _{0}^{1}\gamma _{i}\underline{a}_{i}(\gamma _{i})\text {d}\gamma _{i}+\cdots \nonumber \\&\quad \left. +\,\sum \limits _{k=1}^{n}\int _{0}^{1}\gamma _{k}\underline{a}_{k}(\gamma _{k})\text {d}\gamma _{k} \mathop {\mathop {\mathop {\prod }\limits _{i = 1}} \limits _{i \ne k}}^n\int _{0}^{1}\gamma _{i}\overline{a}_{i}(\gamma _{i})\text {d}\gamma _{i}+\prod \limits _{i = 1}^n\int _{0}^{1}\gamma _{i}\overline{a}_{i}(\gamma _{i})\text {d}\gamma _{i}\right] \\&= \frac{1}{2^{n}}M^{-}(A_{i})\left[ \prod \limits _{i = 1}^n M^{-}(A_{i})+\sum \limits _{k=1}^{n}M^{+}(A_{k}) \mathop {\mathop {\mathop {\prod }\limits _{i = 1}} \limits _{i \ne k}}^n M^{-}(A_{i})+ \mathop {\mathop {\mathop {\sum }\limits _{k=1}} \limits _{k<j}}^{n}M^{+}(A_{k})M^{+}(A_{j})\right. \nonumber \\&\quad \left. \times \mathop {\mathop {\mathop {\mathop {\prod }\limits _{ i = 1}}\limits _{ i \ne j}}\limits _{i \ne k }}^n M^{-}(A_{i})+\cdots +\sum \limits _{k=1}^{n}M^{-}(A_{k}) \mathop {\mathop {\mathop {\prod }\limits _{i = 1}} \limits _{i \ne k}}^n M^{+}(A_{i})+\prod \limits _{i = 1}^n M^{+}(A_{i})\right] \\&= \frac{1}{2^{n-1}}E(A_{1})\left[ \prod \limits _{i =2}^n M^{-}(A_{i})+\sum \limits _{k=2}^{n}M^{+}(A_{k}) \mathop {\mathop {\mathop {\prod }\limits _{i =2}} \limits _{i \ne k}}^n M^{-}(A_{i})+ \mathop {\mathop {\mathop {\sum }\limits _{k=2}} \limits _{k<j}}^{n}M^{+}(A_{k})M^{+}(A_{j})\right. \nonumber \\&\quad \left. \times \mathop {\mathop {\mathop {\mathop {\prod }\limits _{ i =2}}\limits _{ i \ne j}}\limits _{i \ne k }}^n M^{-}(A_{i})+\cdots +\sum \limits _{k=2}^{n}M^{-}(A_{k}) \mathop {\mathop {\mathop {\prod }\limits _{i =2}} \limits _{i \ne k}}^n M^{+}(A_{i})+\prod \limits _{i =2}^n M^{+}(A_{i})\right] \\&\qquad \vdots \\&= \frac{1}{2^{n-l}}\prod \limits _{i =1}^l E(A_{i})\left[ \prod \limits _{i =l}^n M^{-}(A_{i})+\sum \limits _{k=l}^{n}M^{+}(A_{k}) \mathop {\mathop {\mathop {\prod }\limits _{i =l}} \limits _{i \ne k}}^n M^{-}(A_{i}) \mathop {+\mathop {\mathop {\sum }\limits _{k=l}} \limits _{k<j}}^{n}M^{+}(A_{k})M^{+}(A_{j})\right. \nonumber \\&\quad \left. \times \mathop {\mathop {\mathop {\mathop {\prod }\limits _{ i =l}} \limits _{ i \ne j }}\limits _{i \ne k }}^n M^{-}(A_{i})+\cdots +\sum \limits _{k=l}^{n}M^{-}(A_{k}) \mathop {\mathop {\mathop {\prod }\limits _{i =l}} \limits _{i \ne k}}^n M^{+}(A_{i})+\prod \limits _{i =l}^n M^{+}(A_{i})\right] \\&\qquad \vdots \\&=\prod \limits _{i = 1}^n E(A_{i}). \end{aligned}$$

(ii) According to Eq. (8), we have

$$\begin{aligned}&Var\left( \prod \limits _{i = 1}^n A_{i}\right) =\int _{0}^{1}\ldots \int _{0}^{1} \gamma _{1}\ldots \gamma _{n}\left[ \left( \prod \limits _{i = 1}^n E(A_{i})- \prod \limits _{i = 1}^n\underline{a}_{i}(\gamma _{i})\right) ^{2} \right. \nonumber \\&\quad +\sum \limits _{k=1}^{n}\left( \prod \limits _{i = 1}^n E(A_{i})-\overline{a}_{k} (\gamma _{k}) \mathop {\mathop {\mathop {\prod }\limits _{i = 1}} \limits _{i \ne k}}^T \underline{a}_{i}(\gamma _{i})\right) ^{2}\nonumber \\&\quad +\,\mathop {\mathop {\mathop {\sum }\limits _{k=1}}\limits _{k<j}}^{T} \left( \prod \limits _{i = 1}^n E(A_{i})-\overline{a}_{k}(\gamma _{k})\overline{a}_{j}(\gamma _{j}) \mathop {\mathop {\mathop {\mathop {\prod }\limits _{i = 1}} \limits _{ i \ne j }}\limits _{i \ne k }}^T\underline{a}_{i} (\gamma _{i})\right) ^{2}+\cdots +\sum \limits _{k=1}^{n}\left( \prod \limits _{i = 1}^n E(A_{i})-\underline{a}_{k}(\gamma _{k})\prod \limits _{i = 1}^n \overline{a}_{i}(\gamma _{i})^{2}\right. \nonumber \\&\quad +\,\left. \left( \prod \limits _{i = 1}^n E(A_{i})-\prod \limits _{i = 1}^n\overline{a}_{i}(\gamma _{i})\right) ^{2}\right] \text {d}\gamma _{1} \ldots \text {d}\gamma _{n}. \end{aligned}$$
(36)

Notice that the rth item in Eq. (36) can be represented by

$$\begin{aligned}&\int _{0}^{1}\ldots \int _{0}^{1}\gamma _{1}\ldots \gamma _{n} \mathop {\mathop {\mathop {\sum }\limits _{k_{j}=1}}\limits _{j\in \{1,2, \ldots ,r\}}}^{n}\left( \prod \limits _{i = 1}^n E(A_{i})-\prod \limits _{j = 1}^r\overline{a}_{k_{j}}(\gamma _{k_{j}}) \mathop {\mathop {\mathop {\prod }\limits _{ i = 1}}\limits _{ i \ne k_{j}}}^n \underline{a}_{i}(\gamma _{i})\right) ^{2}\text {d}\gamma _{1}\ldots \text {d} \gamma _{n}\nonumber \\&=\int _{0}^{1}\ldots \int _{0}^{1}\gamma _{1}\ldots \gamma _{n} \mathop {\mathop {\mathop {\sum }\limits _{k_{j}=1}}\limits _{\;j\in \{1,2, \ldots ,r\}}}^{n}\left[ \left( \prod \limits _{i = 1}^n E(A_{i})\right) ^{2}-2\prod \limits _{i = 1}^n E(A_{i})\prod \limits _{j = 1}^r\overline{a}_{k_{j}}(\gamma _{k_{j}}) \mathop {\mathop {\mathop {\prod }\limits _{ i = 1}}\limits _{ i \ne k_{j}}}^n\underline{a}_{i}(\gamma _{i})\right. \nonumber \\&\quad +\,\left. \left( \prod \limits _{j =1}^r\overline{a}_{k_{j}}(\gamma _{k_{j}}) \mathop {\mathop {\mathop {\prod }\limits _{ i = 1}}\limits _{ i \ne k_{j}}}^T\underline{a}_{i}(\gamma _{i})\right) ^{2}\right] \text {d}\gamma _{1}\ldots \text {d}\gamma _{n}\nonumber \\&= \frac{1}{2^{n}} \left( \prod \limits _{i = 1}^n E(A_{i})\right) ^{2}-2\prod \limits _{i = 1}^n \mathop {\mathop {E(A_{i})\mathop {\sum }\limits _{k_{j}=1}} \mathop {\;j\in \{1,2,\ldots ,r\}}}^{n}\prod \limits _{j = 1}^r\int _{0}^{1}\gamma _{k_{j}}\overline{a}_{k_{j}}(\gamma _{k_{j}}) \text {d}\gamma _{k_{j}}\nonumber \\&\quad \times \mathop {\mathop {\mathop {\prod }\limits _{i = 1}}\limits _{ i \ne k_{j}}}^n\int _{0}^{1}\gamma _{i}\underline{a}_{i}(\gamma _{i}) \text {d}\gamma _{i}+\mathop {\mathop {\mathop {\sum }\limits _{k_{j}=1}} \limits _{\;j\in \{1,2,\ldots ,r\}}}^{n}\prod \limits _{j = 1}^r\int _{0}^{1}\gamma _{k_{j}}(\overline{a}_{k_{j}}(\gamma _{k_{j}}))^2 \text {d}\gamma _{k_{j}} \mathop {\mathop {\mathop {\prod }\limits _{i = 1}}\limits _{ i \ne k_{j}}}^T \int _{0}^{1}\gamma _{i}(\underline{a}_{i}(\gamma _{i})\text {d} \gamma _{i})^{2}\nonumber \\&= \frac{1}{2^{n}} \left( \prod \limits _{i = 1}^n E(A_{i})\right) ^{2}-\tfrac{1}{2^{n-1}}\prod \limits _{i = 1}^n E(A_{i}) \mathop {\mathop {\mathop {\sum }\limits _{k_{j}=1}}\limits _{\;j\in \{1,2, \ldots ,r\}}}^{n}\prod \limits _{j = 1}^r M^{+}(A_{k_{j}}) \mathop {\mathop {\mathop {\prod }\limits _{ i = 1}} \limits _{ i \ne k_{j}}}^n M^{-}(A_{i})\nonumber \\&\quad +\,\mathop {\mathop {\mathop {\sum }\limits _{k_{j}=1}} \limits _{\;j\in \{1,2,\ldots ,r\}}}^{n}\prod \limits _{j = 1}^r\int _{0}^{1}\gamma _{k_{j}}(\overline{a}_{k_{j}} (\gamma _{k_{j}}))^2\text {d}\gamma _{k_{j}} \mathop {\mathop {\mathop {\prod }\limits _{i = 1}} \limits _{ i \ne k_{j}}}^T\int _{0}^{1}\gamma _{i}(\underline{a}_{i} (\gamma _{i}))^{2}\text {d}\gamma _{i} \end{aligned}$$
(37)

It follows that Eq. (37) can be rewritten as the following form

$$\begin{aligned}&Var\left( \prod \limits _{i = 1}^n A_{i}\right) =\frac{1}{2^{n}} \left( \prod \limits _{i = 1}^n E(A_{i})\right) ^{2}-\frac{1}{2^{n-1}}\prod \limits _{i = 1}^n E(A_{i})\prod \limits _{i = 1}^n M^{-}(A_{i})+\prod \limits _{ i = 1}^n\int _{0}^{1}\gamma _{i}(\underline{a}_{i}(\gamma _{i}))^{2}\text {d}\gamma _{i}\nonumber \\&\quad +\,2^{n-2}\times \frac{1}{2^{n}} \left( \prod \limits _{i = 1}^n E(A_{i})\right) ^{2}-\frac{1}{2^{n-1}}\prod \limits _{i = 1}^n E(A_{i})\sum \limits _{r = 1}^{n-2} \mathop {\mathop {\mathop {\sum }\limits _{k_{j}=1}} \limits _{\;j\in \{1,2,\ldots ,r\}}}^{n}\prod \limits _{j = 1}^r M^{+}(A_{k_{j}}) \mathop {\mathop {\mathop {\prod }\limits _{ i = 1}} \limits _{ i \ne k_{j}}}^n M^{-}(A_{i})\nonumber \\&\quad +\,\sum \limits _{r = 1}^{n-2} \mathop {\mathop {\mathop {\sum }\limits _{k_{j}=1}} \limits _{\;j\in \{1,2,\ldots ,r\}}}^{n}\prod \limits _{j = 1}^r\int _{0}^{1}\gamma _{k_{j}}(\overline{a}_{k_{j}} (\gamma _{k_{j}}))^2\text {d}\gamma _{k_{j}} \mathop {\mathop {\mathop {\prod }\limits _{i = 1}} \limits _{ i \ne k_{j}}}^n\int _{0}^{1}\gamma _{i}(\underline{a}_{i} (\gamma _{i}))^{2}\text {d}\gamma _{i}+\tfrac{1}{2^{n}}(\prod \limits _{i = 1}^n E(A_{i}))^{2}\nonumber \\&\quad -\,\frac{1}{2^{n-1}}\prod \limits _{i = 1}^n E(A_{i}) \prod \limits _{i = 1}^n M^{+}(A_{i})+\prod \limits _{ i = 1}^n \int _{0}^{1}\gamma _{i}(\overline{a}_{i}(\gamma _{i}))^{2}\text {d} \gamma _{i}\nonumber \\&=\left( \prod \limits _{i = 1}^n E(A_{i})\right) ^{2}-\frac{1}{2^{n-1}}\prod \limits _{i = 1}^n E(A_{i})\left[ \prod \limits _{i = 1}^n M^{-}(A_{i})+\sum \limits _{r = 1}^{n-2} \mathop {\mathop {\mathop {\sum }\limits _{k_{j}=1}} \limits _{\;j\in \{1,2,\ldots ,r\}}}^{n}\prod \limits _{j = 1}^r M^{+}(A_{k_{j}}) \mathop {\mathop {\mathop {\prod }\limits _{ i = 1}} \limits _{ i \ne k_{j}}}^n M^{-}(A_{i})\right. \nonumber \\&\quad +\,\left. \prod \limits _{i = 1}^n M^{+}(A_{i})\right] +\prod \limits _{ i = 1}^n\int _{0}^{1}\gamma _{i}(\underline{a}_{i} (\gamma _{i}))^{2}\text {d}\gamma _{i}+\sum \limits _{r = 1}^{n-2} \mathop {\mathop {\mathop {\sum }\limits _{k_{j}=1}} \limits _{\;j\in \{1,2,\ldots ,r\}}}^{n}\prod \limits _{j = 1}^r \int _{0}^{1}\gamma _{k_{j}}(\overline{a}_{k_{j}}(\gamma _{k_{j}}))^2 \text {d}\gamma _{k_{j}}\nonumber \\&\quad \times \,\mathop {\mathop {\mathop {\prod }\limits _{ i = 1}} \limits _{ i \ne k_{j}}}^n\int _{0}^{1}\gamma _{i}(\underline{a}_{i}(\gamma _{i}))^{2} \text {d}\gamma _{i}+\prod \limits _{ i = 1}^n\int _{0}^{1}\gamma _{i}(\overline{a}_{i}(\gamma _{i}))^{2} \text {d}\gamma _{i}. \end{aligned}$$

Notice that \(\tfrac{1}{2^{n-1}}\prod \limits _{i = 1}^n E(A_{i})[\prod \limits _{i = 1}^n M^{-}(A_{i})+\sum \limits _{r = 1}^{n-2}\mathop {\mathop {\mathop {\sum }\limits _{k_{j}=1}}\limits _{\;j\in \{1,2,\ldots ,r\}}}^{n}\prod \limits _{j = 1}^r M^{+}(A_{k_{j}}) \mathop {\mathop {\mathop {\prod }\limits _{ i = 1}} \limits _{ i \ne k_{j}}}^n M^{-}(A_{i})+\prod \limits _{i = 1}^n M^{+}(A_{i})]=2(\prod \limits _{i = 1}^n E(A_{i}))^{2}\). Then, we have

$$\begin{aligned} Var\left( \prod \limits _{i = 1}^n A_{i}\right)&=\prod \limits _{ i = 1}^n\int _{0}^{1}\gamma _{i}(\underline{a}_{i}(\gamma _{i}))^{2}\text {d}\gamma _{i}+\sum \limits _{r = 1}^{n-2} \mathop {\mathop {\mathop {\sum }\limits _{k_{j}=1}}\limits _{\;j\in \{1,2,\ldots ,r\}}}^{n}\prod \limits _{j = 1}^r\int _{0}^{1}\gamma _{k_{j}}(\overline{a}_{k_{j}}(\gamma _{k_{j}}))^2\text {d}\gamma _{k_{j}}\nonumber \\&\quad \times \mathop {\mathop {\mathop {\prod }\limits _{ i = 1}}\limits _{ i \ne k_{j}}}^n\int _{0}^{1}\gamma _{i}(\underline{a}_{i}(\gamma _{i}))^{2}\text {d}\gamma _{i}+\prod \limits _{ i = 1}^n\int _{0}^{1}\gamma _{i}(\overline{a}_{i}(\gamma _{i}))^{2}\text {d}\gamma _{i}-\left( \prod \limits _{i = 1}^n E(A_{i})\right) ^{2}. \end{aligned}$$

The theorem is proved. \(\square \)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, YJ., Zhang, WG. Possibilistic Moment Models for Multi-period Portfolio Selection with Fuzzy Returns. Comput Econ 53, 1657–1686 (2019). https://doi.org/10.1007/s10614-018-9833-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10614-018-9833-6

Keywords

Navigation