Intertemporal constrained portfolio optimization problem with tracking-error
Mitigating portfolio risk and enhancing performance are key goals in portfolio optimization. Investors prioritize the long-term implications of wealth accumulation and the benchmark strategies employed to mitigate risk. This paper presents a multi-period mean-variance model which includes intertempo...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Applied Mathematics in Science and Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/27690911.2025.2463456 |
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| Summary: | Mitigating portfolio risk and enhancing performance are key goals in portfolio optimization. Investors prioritize the long-term implications of wealth accumulation and the benchmark strategies employed to mitigate risk. This paper presents a multi-period mean-variance model which includes intertemporal constraints and tracking-error terms. We systematically tackle the multi-period mean-variance optimization problem, integrating these constraints and considerations. By employing dynamic programming and the Linear Quadratic control framework, we derive the optimal policy for our model. Using a numerical example, we show that the dynamic mean-variance model effectively increases returns while reducing variance and partial risk. This model helps investors maintain wealth stability, lower the chance of significant losses, and enhance risky returns during the investment period. |
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| ISSN: | 2769-0911 |