Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

1.60

CiteScore

Murat Cal1This email address is being protected from spambots. You need JavaScript enabled to view it., Sibel Atan2

1Department of Engineering and Technology, American College of the Middle East, 50000, Egaila Block 6, Ahmadi, Kuwait

2Department of Economics, Faculty of Economics and Administrative Sciences, Haci Bayram Veli University, 06570, Cankaya, Ankara, Turkey


 

Received: March 2, 2023
Accepted: January 3, 2023
Publication Date: October 5, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202405_27(5).0009  


Nonlinear mathematical models are widely used better to reflect the stochastic structure of financial investment problems and to express them numerically. However, in some real-life situations, it is necessary to consider not only one purpose but many purposes simultaneously. Therefore, we have to define these models with multi-objective programming. This study defines a multi-objective nonlinear Eurobond investment portfolio and showcases the normal distribution of purchase and selling prices. The study then proposes a mechanism to convert the stochastic constraint into an equivalent deterministic form and provides near-optimal solutions in reasonable times.


Keywords: financial investment models; chance constraints; nonlinear optimization


  1. [1] Is Bankasi A.S. Eurobond | Türkiye ˙I¸s Bankası A.¸S. 2023.
  2. [2] Y. Seppälä, (1971) “Constructing Sets of Uniformly Tighter Linear Approximations for a Chance Constraint" Management Science 17(11): 736–749. DOI: 10.1287/mnsc.17.11.736.
  3. [3] J. C. Spall, S. D. Hill, and D. R. Stark, (2002) “Theoretical Framework for Comparing Several Stochastic Optimization Approaches" Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301) 4: 99–117. DOI: 10.1109/ACC.2002.1025274.
  4. [4] D. Dentcheva. “Optimization Models with Probabilistic Constraints”. In: Probabilistic and Randomized Methods for Design under Uncertainty. Ed. by Calafiore, G. and F. Dabbene. Springer, London, 2006, 49–97. DOI: 10.1007/1-84628-095-8_2.
  5. [5] A. Nemirovski and A. Shapiro. “Scenario Approximations of Chance Constraints”. In: Probabilistic and Randomized Methods for Design under Uncertainty. Ed. by G. Calafiore and F. Dabbene. Springer, London, 2006, 3–47. DOI: 10.1007/1-84628-095-8_1.
  6. [6] G. Calafiore and F. Dabbene. Probabilistic and randomized methods for design under uncertainty. Springer, 2006.
  7. [7] B. Aouni, F. Ben Abdelaziz, and D. La Torre, (2012) “The stochastic goal programming model: theory and applications" Journal of Multi-Criteria Decision Analysis 19(5-6): 185–200.
  8. [8] D. L. Olson and S. R. Swenseth, (2017) “A Linear Approximation for Chance-Constrained Programming" The Journal of the Operational Research Society 38(3): 261–267. DOI: 10.1057/JORS.1987.42.
  9. [9] K. Baker and A. Bernstein, (2019) “Joint chance constraints in AC optimal power flow: Improving bounds through learning" IEEE Transactions on Smart Grid 10(6): 6376–6385.
  10. [10] M. Tavana, R. Khanjani Shiraz, and D. Di Caprio, (2019) “A chance-constrained portfolio selection model with random-rough variables" Neural Computing and Applications 31: 931–945.
  11. [11] X. Geng and L. Xie, (2019) “Data-driven decision making in power systems with probabilistic guarantees: Theory and applications of chance-constrained optimization" Annual reviews in control 47: 341–363.
  12. [12] K. Oguri, M. Ono, and J. W. McMahon. “Convex optimization over sequential linear feedback policies with continuous-time chance constraints”. In: 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE. 2019, 6325–6331.
  13. [13] Z. Chen, S. Peng, and A. Lisser, (2020) “A sparse chance constrained portfolio selection model with multiple constraints" Journal of Global Optimization 77: 825–852.
  14. [14] A. Zhou, M. Yang, M. Wang, and Y. Zhang, (2020) “A linear programming approximation of distributionally robust chance-constrained dispatch with Wasserstein distance" IEEE Transactions on Power Systems 35(5): 3366–3377.
  15. [15] C. Mark and S. Liu, (2020) “Stochastic MPC with distributionally robust chance constraints" IFACPapersOnLine 53(2): 7136–7141.
  16. [16] B. Zhou, G. Chen, Q. Song, and Z. Y. Dong, (2020) “Robust chance-constrained programming approach for the planning of fast-charging stations in electrified transportation networks" Applied Energy 262: 114480.
  17. [17] C. Ordoudis, V. A. Nguyen, D. Kuhn, and P. Pinson, (2021) “Energy and reserve dispatch with distributionally robust joint chance constraints" Operations Research Letters 49(3): 291–299.
  18. [18] J. H. Kim, Y. Lee, W. C. Kim, and F. J. Fabozzi, (2022) “Goal-based investing based on multi-stage robust portfolio optimization" Annals of Operations Research 313(2): 1141–1158.
  19. [19] A. Arrigo, C. Ordoudis, J. Kazempour, Z. De Grève, J.-F. Toubeau, and F. Vallée, (2022) “Wasserstein distributionally robust chance-constrained optimization for energy and reserve dispatch: An exact and physicallybounded formulation" European Journal of Operational Research 296(1): 304–322.
  20. [20] Y. Dvorkin, (2020) “A Chance-Constrained Stochastic Electricity Market" IEEE Transactions on Power Systems 35(4): 2993–3003. DOI: 10.1109/TPWRS.2019.2961231.
  21. [21] T. Muhlpfordt, L. Roald, V. Hagenmeyer, T. Faulwasser, and S. Misra, (2019) “Chance-Constrained AC Optimal Power Flow: A Polynomial Chaos Approach" IEEE Transactions on Power Systems 34(6): 4806–4816. DOI: 10.1109/TPWRS.2019.2918363. arXiv: 1903.11337.