A hybrid framework for mean-CVaR portfolio selection under jump-diffusion processes: Combining cross-entropy method with beluga whale optimization

Guocheng Li; Pan Zhao; Minghua Shi; Gensheng Li; Guocheng Li; Pan Zhao; Minghua Shi; Gensheng Li

doi:10.3934/math.2024972

AIMS Mathematics

2024, Volume 9, Issue 8: 19911-19942. doi: 10.3934/math.2024972

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A hybrid framework for mean-CVaR portfolio selection under jump-diffusion processes: Combining cross-entropy method with beluga whale optimization

1.
School of Finance and Mathematics, West Anhui University, Lu'an 237012, China
2.
Anhui Provincial Key Laboratory of Philosophy and Social Sciences for Data Intelligence and Rural Revitalization of Dabie Mountains, Lu'an 237012, China

Received: 09 March 2024 Revised: 04 June 2024 Accepted: 12 June 2024 Published: 19 June 2024
MSC : 68T20, 91G10

In this paper, a new hybrid meta-heuristic algorithm called CEBWO (cross-entropy method and beluga whale optimization) is presented to solve the mean-CVaR portfolio optimization problem based on jump-diffusion processes. The proposed CEBWO algorithm combines the advantages of the cross-entropy method and beluga whale optimization algorithm with the help of co-evolution technology to enhance the performance of portfolio selection. The method is evaluated on 29 unconstrained benchmark functions from CEC 2017, where its performance is compared against several state-of-the-art algorithms. The results demonstrate the superiority of the hybrid method in terms of solution quality and convergence speed. Finally, Monte Carlo simulation is employed to generate scenario paths based on the jump-diffusion model. Empirical results further confirm the effectiveness of the hybrid meta-heuristic algorithm for mean-CVaR portfolio selection, highlighting its potential for real-world applications.
- portfolio selection,
- conditional value-at-risk,
- jump-diffusion process,
- cross-entropy method,
- beluga whale optimization
Citation: Guocheng Li, Pan Zhao, Minghua Shi, Gensheng Li. A hybrid framework for mean-CVaR portfolio selection under jump-diffusion processes: Combining cross-entropy method with beluga whale optimization[J]. AIMS Mathematics, 2024, 9(8): 19911-19942. doi: 10.3934/math.2024972

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Abstract

In this paper, a new hybrid meta-heuristic algorithm called CEBWO (cross-entropy method and beluga whale optimization) is presented to solve the mean-CVaR portfolio optimization problem based on jump-diffusion processes. The proposed CEBWO algorithm combines the advantages of the cross-entropy method and beluga whale optimization algorithm with the help of co-evolution technology to enhance the performance of portfolio selection. The method is evaluated on 29 unconstrained benchmark functions from CEC 2017, where its performance is compared against several state-of-the-art algorithms. The results demonstrate the superiority of the hybrid method in terms of solution quality and convergence speed. Finally, Monte Carlo simulation is employed to generate scenario paths based on the jump-diffusion model. Empirical results further confirm the effectiveness of the hybrid meta-heuristic algorithm for mean-CVaR portfolio selection, highlighting its potential for real-world applications.

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