Tail mean-variance portfolio selection with estimation risk
Tail Mean-Variance (TMV) has emerged from the actuarial community as a criterion for risk management and portfolio selection, with a focus on extreme losses. The existing literature on portfolio optimization under the TMV criterion relies on the plug-in approach that substitutes the unknown mean vec...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/10356/174709 |
| _version_ | 1811682296292442112 |
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| author | Huang, Zhenzhen Wei, P.engyu Weng, Chengguo |
| author2 | Nanyang Business School |
| author_facet | Nanyang Business School Huang, Zhenzhen Wei, P.engyu Weng, Chengguo |
| author_sort | Huang, Zhenzhen |
| collection | NTU |
| description | Tail Mean-Variance (TMV) has emerged from the actuarial community as a criterion for risk management and portfolio selection, with a focus on extreme losses. The existing literature on portfolio optimization under the TMV criterion relies on the plug-in approach that substitutes the unknown mean vector and covariance matrix of asset returns in the optimal portfolio weights with their sample counterparts. However, the plug-in method inevitably introduces estimation risk and usually leads to poor out-of-sample portfolio performance. To address this issue, we propose a combination of the plug-in and 1/N rules and optimize its expected out-of-sample performance. Our study is based on the Mean-Variance-Standard-deviation (MVS) performance measure, which encompasses the TMV, classical Mean-Variance, and Mean-Standard-Deviation (MStD) as special cases. The MStD criterion is particularly relevant to mean-risk portfolio selection when risk is measured by quantile-based risk measures. Our proposed combined portfolio consistently outperforms both the plug-in MVS and 1/N portfolios in simulated and real-world datasets. |
| first_indexed | 2024-10-01T03:54:35Z |
| format | Journal Article |
| id | ntu-10356/174709 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:54:35Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1747092024-04-11T15:37:22Z Tail mean-variance portfolio selection with estimation risk Huang, Zhenzhen Wei, P.engyu Weng, Chengguo Nanyang Business School Business and Management Tail mean-variance Portfolio selection Tail Mean-Variance (TMV) has emerged from the actuarial community as a criterion for risk management and portfolio selection, with a focus on extreme losses. The existing literature on portfolio optimization under the TMV criterion relies on the plug-in approach that substitutes the unknown mean vector and covariance matrix of asset returns in the optimal portfolio weights with their sample counterparts. However, the plug-in method inevitably introduces estimation risk and usually leads to poor out-of-sample portfolio performance. To address this issue, we propose a combination of the plug-in and 1/N rules and optimize its expected out-of-sample performance. Our study is based on the Mean-Variance-Standard-deviation (MVS) performance measure, which encompasses the TMV, classical Mean-Variance, and Mean-Standard-Deviation (MStD) as special cases. The MStD criterion is particularly relevant to mean-risk portfolio selection when risk is measured by quantile-based risk measures. Our proposed combined portfolio consistently outperforms both the plug-in MVS and 1/N portfolios in simulated and real-world datasets. Nanyang Technological University Submitted/Accepted version Both Wei and Weng acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC, with grant numbers RGPIN-2020-07013 and DGECR-2020-00370 for Wei, and RGPIN-2023-03335 for Weng). Huang thanks the funding support from both NSERC grants and the Department of Statistics and Actuarial Science, University of Waterloo. Wei also acknowledges financial support through a start-up grant at Nanyang Technological University. 2024-04-08T04:43:25Z 2024-04-08T04:43:25Z 2024 Journal Article Huang, Z., Wei, P. & Weng, C. (2024). Tail mean-variance portfolio selection with estimation risk. Insurance: Mathematics and Economics, 116, 218-234. https://dx.doi.org/10.1016/j.insmatheco.2024.03.001 0167-6687 https://hdl.handle.net/10356/174709 10.1016/j.insmatheco.2024.03.001 2-s2.0-85188419480 116 218 234 en NTU-SUG Insurance: Mathematics and Economics © 2024 Elsevier B.V. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1016/j.insmatheco.2024.03.001. application/pdf |
| spellingShingle | Business and Management Tail mean-variance Portfolio selection Huang, Zhenzhen Wei, P.engyu Weng, Chengguo Tail mean-variance portfolio selection with estimation risk |
| title | Tail mean-variance portfolio selection with estimation risk |
| title_full | Tail mean-variance portfolio selection with estimation risk |
| title_fullStr | Tail mean-variance portfolio selection with estimation risk |
| title_full_unstemmed | Tail mean-variance portfolio selection with estimation risk |
| title_short | Tail mean-variance portfolio selection with estimation risk |
| title_sort | tail mean variance portfolio selection with estimation risk |
| topic | Business and Management Tail mean-variance Portfolio selection |
| url | https://hdl.handle.net/10356/174709 |
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