Optimal investment of defined contribution pension plan with environmental, social, and governance (ESG) factors in regime-switching jump diffusion models

In this article, we address the optimal investment problem for a defined contribution pension plan, taking into account both financial performance and environmental, social, and governance (ESG) factors. We model the surplus of the pension fund using a regime-switching jump diffusion process. To capture the pension managers’ preference toward different kinds of assets, such as green and brown stocks, we utilize the multiple constant relative risk aversion utility. By applying the dynamic programming principle, we derive the optimal investment strategy and the corresponding value function. We also discuss the existence and uniqueness of the optimal solution. Through numerical analysis, we demonstrate that increasing salary and contribution rates, as well as implementing policies related to sustainable development, can effectively encourage pension managers to allocate more of their investments toward green stocks. These findings contribute to a better understanding of the optimal investment decisions in defined contribution pension plans, considering both financial and ESG aspects. In summary, this article can provide valuable insights for both policymakers and practitioners interested in enhancing sustainable investment and aligning pension funds with ESG objectives.

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Publication Details

Journal
Communication in Statistics- Theory and Methods
Published
2024-09-16
DOI
https://doi.org/10.1080/03610926.2024.2395883
Citations
2
Primary Topic
Financial Literacy, Pension, Retirement Analysis
Type
article
Field-Weighted Citation Impact
1.89

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article

Optimal investment of defined contribution pension plan with environmental, social, and governance (ESG) factors in regime-switching jump diffusion models

Fanyi Peng, Ming Yan, Shuhua Zhang
2 citations
Communication in Statistics- Theory and Methods
Financial Literacy, Pension, Retirement Analysis
1.89
article

Optimal investment of defined contribution pension plan with environmental, social, and governance (ESG) factors in regime-switching jump diffusion models

Fanyi Peng, Ming Yan, Shuhua Zhang
article en
2 citations

Abstract

In this article, we address the optimal investment problem for a defined contribution pension plan, taking into account both financial performance and environmental, social, and governance (ESG) factors. We model the surplus of the pension fund using a regime-switching jump diffusion process. To capture the pension managers’ preference toward different kinds of assets, such as green and brown stocks, we utilize the multiple constant relative risk aversion utility. By applying the dynamic programming principle, we derive the optimal investment strategy and the corresponding value function. We also discuss the existence and uniqueness of the optimal solution. Through numerical analysis, we demonstrate that increasing salary and contribution rates, as well as implementing policies related to sustainable development, can effectively encourage pension managers to allocate more of their investments toward green stocks. These findings contribute to a better understanding of the optimal investment decisions in defined contribution pension plans, considering both financial and ESG aspects. In summary, this article can provide valuable insights for both policymakers and practitioners interested in enhancing sustainable investment and aligning pension funds with ESG objectives.

Communication in Statistics- Theory and MethodsVol. 54(12)
South China Agricultural University (CN), Tianjin University of Finance and Economics (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 12%
Financial Literacy, Pension, Retirement Analysis
1.89
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Optimal investment of defined contribution pension plan with environmental, social, and governance (ESG) factors in regime-switching jump diffusion models — Fanyi Peng, Ming Yan, et al. · Communication in Statistics- Theory and Methods (2024) | TGRS Research Map | TGRS