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

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Détails bibliographiques
Publié dans:	Entropy vol. 27, no. 11 (2025), p. 1118-1145
Auteur principal:	Hissah, Albaqami
Autres auteurs:	Mrad Mehdi, Gharbi Anis, Subasi Munevver Mine
Publié:	MDPI AG
Sujets:	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
Accès en ligne:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Description
Résumé:	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
Source:	Engineering Database