A Monte Carlo-Based Framework for Two-Stage Stochastic Programming: Application to Bond Portfolio Optimization
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| Vydáno v: | Entropy vol. 27, no. 11 (2025), p. 1118-1145 |
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MDPI AG
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| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231460 |2 nlm | ||
| 100 | 1 | |a Hissah, Albaqami |u Department of Mathematics and Systems Engineering, Florida Institute of Technology, Melbourne, FL 32901, USA; h.hassh@tu.edu.sa | |
| 245 | 1 | |a A Monte Carlo-Based Framework for Two-Stage Stochastic Programming: Application to Bond Portfolio Optimization | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper presents a Monte Carlo simulation-based approach for solving stochastic two-stage bond portfolio optimization problems. The main objective is to optimize the cost of the bond portfolio while making decisions on bond purchases, holdings, and sales under random market conditions such as interest rate fluctuations and liabilities. The proposed algorithm identifies the number of randomly generated scenarios required to convert the stochastic problem into a deterministic one, subsequently solving it as a Mixed-Integer Linear Program. The practical relevance of this research is shown through an application of the proposed method to a real-world bond market. The results indicate that the proposed approach successfully minimizes costs and meets liabilities, providing a robust solution for bond portfolio optimization. | |
| 653 | |a Sample size | ||
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Integer programming | ||
| 653 | |a Investments | ||
| 653 | |a Random variables | ||
| 653 | |a Bond portfolios | ||
| 653 | |a Sample variance | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Decision making | ||
| 653 | |a Optimization | ||
| 653 | |a Approximation | ||
| 653 | |a Linear programming | ||
| 653 | |a Mixed integer | ||
| 653 | |a Interest rates | ||
| 653 | |a Expected values | ||
| 653 | |a Stochastic programming | ||
| 700 | 1 | |a Mrad Mehdi |u Essect School of Business, University of Tunis, Tunis 1089, Tunisia; mehdi.mrad@essect.u-tunis.tn | |
| 700 | 1 | |a Gharbi Anis |u Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia; a.gharbi@ksu.edu.sa | |
| 700 | 1 | |a Subasi Munevver Mine |u Department of Mathematics and Systems Engineering, Florida Institute of Technology, Melbourne, FL 32901, USA; h.hassh@tu.edu.sa | |
| 773 | 0 | |t Entropy |g vol. 27, no. 11 (2025), p. 1118-1145 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275511986/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3275511986/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275511986/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |