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A Monte Carlo-Based Framework for Two-Stage Stochastic Programming: Application to Bond Portfolio Optimization

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Pubblicato in:Entropy vol. 27, no. 11 (2025), p. 1118-1145
Autore principale: Hissah, Albaqami
Altri autori: Mrad Mehdi, Gharbi Anis, Subasi Munevver Mine
Pubblicazione:
MDPI AG
Soggetti:
Sample size
Monte Carlo simulation
Integer programming
Investments
Random variables
Bond portfolios
Sample variance
Optimization techniques
Decision making
Optimization
Approximation
Linear programming
Mixed integer
Interest rates
Expected values
Stochastic programming
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Abstract: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.
ISSN:1099-4300
DOI:10.3390/e27111118
Fonte:Engineering Database