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

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Detalhes bibliográficos
Publicado no:	Entropy vol. 27, no. 11 (2025), p. 1118-1145
Autor principal:	Hissah, Albaqami
Outros Autores:	Mrad Mehdi, Gharbi Anis, Subasi Munevver Mine
Publicado em:	MDPI AG
Assuntos:	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
Acesso em linha:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descrição
Resumo:	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