Robust tracking error portfolio selection with worst-case downside risk measures
This paper proposes downside risk measure models in portfolio selection that captures uncertainties both in distribution and in parameters. The worst-case distribution with given information on the mean value and the covariance matrix is used, together with ellipsoidal and polytopic uncertainty sets...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Elsevier BV
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/11097 |
| _version_ | 1848747714262597632 |
|---|---|
| author | Ling, A. Sun, Jie Yang, X. |
| author_facet | Ling, A. Sun, Jie Yang, X. |
| author_sort | Ling, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper proposes downside risk measure models in portfolio selection that captures uncertainties both in distribution and in parameters. The worst-case distribution with given information on the mean value and the covariance matrix is used, together with ellipsoidal and polytopic uncertainty sets, to build-up this type of downside risk model. As an application of the models, the tracking error portfolio selection problem is considered. By lifting the vector variables to positive semidefinite matrix variables, we obtain semidefinite programming formulations of the robust tracking portfolio models. Numerical results are presented in tracking SSE50 of the Shanghai Stock Exchange. Compared with the tracking error variance portfolio model and the equally weighted strategy, the proposed models are more stable, have better accumulated wealth and have much better Sharpe ratio in the investment period for the majority of observed instances. |
| first_indexed | 2025-11-14T06:53:32Z |
| format | Journal Article |
| id | curtin-20.500.11937-11097 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:53:32Z |
| publishDate | 2014 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-110972017-09-13T14:56:21Z Robust tracking error portfolio selection with worst-case downside risk measures Ling, A. Sun, Jie Yang, X. This paper proposes downside risk measure models in portfolio selection that captures uncertainties both in distribution and in parameters. The worst-case distribution with given information on the mean value and the covariance matrix is used, together with ellipsoidal and polytopic uncertainty sets, to build-up this type of downside risk model. As an application of the models, the tracking error portfolio selection problem is considered. By lifting the vector variables to positive semidefinite matrix variables, we obtain semidefinite programming formulations of the robust tracking portfolio models. Numerical results are presented in tracking SSE50 of the Shanghai Stock Exchange. Compared with the tracking error variance portfolio model and the equally weighted strategy, the proposed models are more stable, have better accumulated wealth and have much better Sharpe ratio in the investment period for the majority of observed instances. 2014 Journal Article http://hdl.handle.net/20.500.11937/11097 10.1016/j.jedc.2013.11.011 Elsevier BV restricted |
| spellingShingle | Ling, A. Sun, Jie Yang, X. Robust tracking error portfolio selection with worst-case downside risk measures |
| title | Robust tracking error portfolio selection with worst-case downside risk measures |
| title_full | Robust tracking error portfolio selection with worst-case downside risk measures |
| title_fullStr | Robust tracking error portfolio selection with worst-case downside risk measures |
| title_full_unstemmed | Robust tracking error portfolio selection with worst-case downside risk measures |
| title_short | Robust tracking error portfolio selection with worst-case downside risk measures |
| title_sort | robust tracking error portfolio selection with worst-case downside risk measures |
| url | http://hdl.handle.net/20.500.11937/11097 |