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

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 metho...

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Bibliographic Details
Published in:AIMS Mathematics
Main Authors: Guocheng Li, Pan Zhao, Minghua Shi, Gensheng Li
Format: Article in Journal/Newspaper
Language:English
Published: AIMS Press 2024
Subjects:
portfolio selection
conditional value-at-risk
jump-diffusion process
cross-entropy method
beluga whale optimization
Mathematics
QA1-939
Beluga
Beluga whale
Beluga*
Online Access:https://doi.org/10.3934/math.2024972
https://doaj.org/article/abae3f9eb75d4df58a2c069959c9edb0
Description
Summary: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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