Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2023, , 311 - 322, 30.06.2023
https://doi.org/10.28979/jarnas.1216406

Öz

Destekleyen Kurum

Yok

Proje Numarası

Yok

Teşekkür

Yok

Kaynakça

  • Biswas, A., Verma, S., & Ojha, D. B. (2017). Optimality and convexity theorems for linear fractional programming problem. International Journal of Computational and Applied Mathematics, 12(3), 911-916. Retrieved from: https://www.ripublication.com/ijcam17/ijcamv12n3_27.pdf
  • Carlsson, C., Fullér, R., & Majlender, P. (2002). A possibilistic approach to selecting portfolios with highest utility score. Fuzzy Sets and Systems, 131(1), 13-21. DOI: https://doi.org/10.1016/S0165-0114(01)00251-2
  • Corazza, M., & Nardelli, C. (2019). Possibilistic mean–variance portfolios versus probabilistic ones: the winner is... Decisions in Economics and Finance, 42(1), 51-75. DOI: https://doi.org/10.1007/s10203-019-00234-1
  • Fullér, R., & Harmati, I. Á. (2018). On Possibilistic Dependencies: A Short Survey of Recent Developments. Soft Computing Based Optimization and Decision Models, 261-273. DOI: https://doi.org/10.1007/978-3-319-64286-4_16
  • Göktaş, F., & Duran, A. (2020). Olabilirlik ortalama–varyans modelinin matematiksel analizi. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(1), 80-91. DOI: https://doi.org/10.25092/baunfbed.677022 Klir, G. and Yuan, B. (1995). Fuzzy Sets and Fuzzy Logic. Prentice Hall.
  • Kosinski, W. (2006). On fuzzy number calculus. International Journal of Applied Mathematics and Computer Science, 16(1), 51-57. Retrieved from: http://matwbn.icm.edu.pl/ksiazki/amc/amc16/amc1614.pdf
  • Rustem, B., Becker, R. G., & Marty, W. (2000). Robust min–max portfolio strategies for rival forecast and risk scenarios. Journal of Economic Dynamics and Control, 24(11-12), 1591-1621. DOI: https://doi.org/10.1016/S0165-1889(99)00088-3
  • Tanaka, H., & Guo, P. (1999). Portfolio selection based on upper and lower exponential possibility distributions. European Journal of Operational Research, 114(1), 115-126. DOI: https://doi.org/10.1016/S0377-2217(98)00033-2
  • Tanaka, H., Guo, P., & Türksen, I. B. (2000). Portfolio selection based on fuzzy probabilities and possibility distributions. Fuzzy Sets and Systems, 111(3), 387-397. DOI: https://doi.org/10.1016/S0165-0114(98)00041-4
  • Taş, O., Kahraman, C., & Güran, C. B. (2016). A scenario based linear fuzzy approach in portfolio selection problem: application in the Istanbul Stock Exchange. Journal of Multiple-Valued Logic & Soft Computing, 26(3-5), 269-294. Retrieved from: http://www.oldcitypublishing.com/pdf/7431
  • Zhang, W. G. (2007). Possibilistic mean–standard deviation models to portfolio selection for bounded assets. Applied Mathematics and Computation, 189(2), 1614-1623. DOI: https://doi.org/10.1016/j.amc.2006.12.080
  • Zhang, W. G., Wang, Y. L., Chen, Z. P., & Nie, Z. K. (2007). Possibilistic mean-variance models and efficient frontiers for portfolio selection problem. Information Sciences, 177(13), 2787–2801. DOI: https://doi.org/10.1016/j.ins.2007.01.030
  • Zhang, W. G., Zhang, X. L., & Xiao, W. L. (2009). Portfolio selection under possibilistic mean–variance utility and a SMO algorithm. European Journal of Operational Research, 197(2), 693-700. DOI: https://doi.org/10.1016/j.ejor.2008.07.011
  • Zhang, Y., Li, X., & Guo, S. (2018). Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature. Fuzzy Optimization and Decision Making, 17(2), 125-158. DOI: https://doi.org/10.1007/s10700-017-9266-z
  • Zimmermann, H. J. (2001). Fuzzy Set Theory and Its Applications. Springer.

Mathematical Analyses of the Upper and Lower Possibilistic Mean – Variance Models and Their Extensions to Multiple Scenarios

Yıl 2023, , 311 - 322, 30.06.2023
https://doi.org/10.28979/jarnas.1216406

Öz

Possibility theory is the one of the most important and widely used uncertainty theories because it is closely related to the imprecise probability and expert knowledge. The possibilistic mean - variance (MV) model is the counterpart of the Markowitz’s MV model in the possibility theory. There are variants of the possibilistic MV model, which are called as the upper and lower possibilistic MV models. However, to the best of our knowledge, analytical solutions and exact efficient frontiers of these variants are not presented in the literature when the possibil-ity distributions are given with trapezoidal fuzzy numbers. In this study, under this assumption, we make mathemat-ical analyses of the upper and lower possibilistic MV models and derive their analytical solutions and exact efficient frontiers. Based on the max-min optimization framework, we also propose their extensions where there are multiple upper (lower) possibilistic mean scenarios. We show that the proposed extensions have the ease of use as the upper and lower possibilistic MV models. We also illustrate and compare the upper and lower possibilistic mean - variance models and their proposed extensions with an explanatory example. As we expect, we see that these extensions can be effectively used in portfolio selection by conservative investors.

Proje Numarası

Yok

Kaynakça

  • Biswas, A., Verma, S., & Ojha, D. B. (2017). Optimality and convexity theorems for linear fractional programming problem. International Journal of Computational and Applied Mathematics, 12(3), 911-916. Retrieved from: https://www.ripublication.com/ijcam17/ijcamv12n3_27.pdf
  • Carlsson, C., Fullér, R., & Majlender, P. (2002). A possibilistic approach to selecting portfolios with highest utility score. Fuzzy Sets and Systems, 131(1), 13-21. DOI: https://doi.org/10.1016/S0165-0114(01)00251-2
  • Corazza, M., & Nardelli, C. (2019). Possibilistic mean–variance portfolios versus probabilistic ones: the winner is... Decisions in Economics and Finance, 42(1), 51-75. DOI: https://doi.org/10.1007/s10203-019-00234-1
  • Fullér, R., & Harmati, I. Á. (2018). On Possibilistic Dependencies: A Short Survey of Recent Developments. Soft Computing Based Optimization and Decision Models, 261-273. DOI: https://doi.org/10.1007/978-3-319-64286-4_16
  • Göktaş, F., & Duran, A. (2020). Olabilirlik ortalama–varyans modelinin matematiksel analizi. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(1), 80-91. DOI: https://doi.org/10.25092/baunfbed.677022 Klir, G. and Yuan, B. (1995). Fuzzy Sets and Fuzzy Logic. Prentice Hall.
  • Kosinski, W. (2006). On fuzzy number calculus. International Journal of Applied Mathematics and Computer Science, 16(1), 51-57. Retrieved from: http://matwbn.icm.edu.pl/ksiazki/amc/amc16/amc1614.pdf
  • Rustem, B., Becker, R. G., & Marty, W. (2000). Robust min–max portfolio strategies for rival forecast and risk scenarios. Journal of Economic Dynamics and Control, 24(11-12), 1591-1621. DOI: https://doi.org/10.1016/S0165-1889(99)00088-3
  • Tanaka, H., & Guo, P. (1999). Portfolio selection based on upper and lower exponential possibility distributions. European Journal of Operational Research, 114(1), 115-126. DOI: https://doi.org/10.1016/S0377-2217(98)00033-2
  • Tanaka, H., Guo, P., & Türksen, I. B. (2000). Portfolio selection based on fuzzy probabilities and possibility distributions. Fuzzy Sets and Systems, 111(3), 387-397. DOI: https://doi.org/10.1016/S0165-0114(98)00041-4
  • Taş, O., Kahraman, C., & Güran, C. B. (2016). A scenario based linear fuzzy approach in portfolio selection problem: application in the Istanbul Stock Exchange. Journal of Multiple-Valued Logic & Soft Computing, 26(3-5), 269-294. Retrieved from: http://www.oldcitypublishing.com/pdf/7431
  • Zhang, W. G. (2007). Possibilistic mean–standard deviation models to portfolio selection for bounded assets. Applied Mathematics and Computation, 189(2), 1614-1623. DOI: https://doi.org/10.1016/j.amc.2006.12.080
  • Zhang, W. G., Wang, Y. L., Chen, Z. P., & Nie, Z. K. (2007). Possibilistic mean-variance models and efficient frontiers for portfolio selection problem. Information Sciences, 177(13), 2787–2801. DOI: https://doi.org/10.1016/j.ins.2007.01.030
  • Zhang, W. G., Zhang, X. L., & Xiao, W. L. (2009). Portfolio selection under possibilistic mean–variance utility and a SMO algorithm. European Journal of Operational Research, 197(2), 693-700. DOI: https://doi.org/10.1016/j.ejor.2008.07.011
  • Zhang, Y., Li, X., & Guo, S. (2018). Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature. Fuzzy Optimization and Decision Making, 17(2), 125-158. DOI: https://doi.org/10.1007/s10700-017-9266-z
  • Zimmermann, H. J. (2001). Fuzzy Set Theory and Its Applications. Springer.
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Furkan Göktaş 0000-0001-9291-3912

Proje Numarası Yok
Erken Görünüm Tarihi 21 Haziran 2023
Yayımlanma Tarihi 30 Haziran 2023
Gönderilme Tarihi 8 Aralık 2022
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Göktaş, F. (2023). Mathematical Analyses of the Upper and Lower Possibilistic Mean – Variance Models and Their Extensions to Multiple Scenarios. Journal of Advanced Research in Natural and Applied Sciences, 9(2), 311-322. https://doi.org/10.28979/jarnas.1216406
AMA Göktaş F. Mathematical Analyses of the Upper and Lower Possibilistic Mean – Variance Models and Their Extensions to Multiple Scenarios. JARNAS. Haziran 2023;9(2):311-322. doi:10.28979/jarnas.1216406
Chicago Göktaş, Furkan. “Mathematical Analyses of the Upper and Lower Possibilistic Mean – Variance Models and Their Extensions to Multiple Scenarios”. Journal of Advanced Research in Natural and Applied Sciences 9, sy. 2 (Haziran 2023): 311-22. https://doi.org/10.28979/jarnas.1216406.
EndNote Göktaş F (01 Haziran 2023) Mathematical Analyses of the Upper and Lower Possibilistic Mean – Variance Models and Their Extensions to Multiple Scenarios. Journal of Advanced Research in Natural and Applied Sciences 9 2 311–322.
IEEE F. Göktaş, “Mathematical Analyses of the Upper and Lower Possibilistic Mean – Variance Models and Their Extensions to Multiple Scenarios”, JARNAS, c. 9, sy. 2, ss. 311–322, 2023, doi: 10.28979/jarnas.1216406.
ISNAD Göktaş, Furkan. “Mathematical Analyses of the Upper and Lower Possibilistic Mean – Variance Models and Their Extensions to Multiple Scenarios”. Journal of Advanced Research in Natural and Applied Sciences 9/2 (Haziran 2023), 311-322. https://doi.org/10.28979/jarnas.1216406.
JAMA Göktaş F. Mathematical Analyses of the Upper and Lower Possibilistic Mean – Variance Models and Their Extensions to Multiple Scenarios. JARNAS. 2023;9:311–322.
MLA Göktaş, Furkan. “Mathematical Analyses of the Upper and Lower Possibilistic Mean – Variance Models and Their Extensions to Multiple Scenarios”. Journal of Advanced Research in Natural and Applied Sciences, c. 9, sy. 2, 2023, ss. 311-22, doi:10.28979/jarnas.1216406.
Vancouver Göktaş F. Mathematical Analyses of the Upper and Lower Possibilistic Mean – Variance Models and Their Extensions to Multiple Scenarios. JARNAS. 2023;9(2):311-22.


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