Cvar-Based Robust Models For Portfolio Selection
This study relaxes the distributional assumption of the return of the risky asset, to arrive at the optimal portfolio. Studies of portfolio selection models have typically assumed that stock returns conform to the normal distribution. The application of robust optimization techniques means that only...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
AMER INST MATHEMATICAL SCIENCES-AIMS
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/91432 |
| _version_ | 1848765519005483008 |
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| author | Sun, Y. Aw, E.L.G. Li, Bin Teo, Kok Lay Sun, Jie |
| author_facet | Sun, Y. Aw, E.L.G. Li, Bin Teo, Kok Lay Sun, Jie |
| author_sort | Sun, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This study relaxes the distributional assumption of the return of the risky asset, to arrive at the optimal portfolio. Studies of portfolio selection models have typically assumed that stock returns conform to the normal distribution. The application of robust optimization techniques means that only the historical mean and variance of asset returns are required instead of distributional information. We show that the method results in an optimal portfolio that has comparable return and yet equivalent risk, to one that assumes normality of asset returns. |
| first_indexed | 2025-11-14T11:36:32Z |
| format | Journal Article |
| id | curtin-20.500.11937-91432 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:36:32Z |
| publishDate | 2020 |
| publisher | AMER INST MATHEMATICAL SCIENCES-AIMS |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-914322023-05-03T07:14:24Z Cvar-Based Robust Models For Portfolio Selection Sun, Y. Aw, E.L.G. Li, Bin Teo, Kok Lay Sun, Jie Science & Technology Technology Physical Sciences Engineering, Multidisciplinary Operations Research & Management Science Mathematics, Interdisciplinary Applications Engineering Mathematics Portfolio optimization risk measure CVaR distributionally robust optimization conic optimization OPTIMIZATION This study relaxes the distributional assumption of the return of the risky asset, to arrive at the optimal portfolio. Studies of portfolio selection models have typically assumed that stock returns conform to the normal distribution. The application of robust optimization techniques means that only the historical mean and variance of asset returns are required instead of distributional information. We show that the method results in an optimal portfolio that has comparable return and yet equivalent risk, to one that assumes normality of asset returns. 2020 Journal Article http://hdl.handle.net/20.500.11937/91432 10.3934/jimo.2019032 English AMER INST MATHEMATICAL SCIENCES-AIMS fulltext |
| spellingShingle | Science & Technology Technology Physical Sciences Engineering, Multidisciplinary Operations Research & Management Science Mathematics, Interdisciplinary Applications Engineering Mathematics Portfolio optimization risk measure CVaR distributionally robust optimization conic optimization OPTIMIZATION Sun, Y. Aw, E.L.G. Li, Bin Teo, Kok Lay Sun, Jie Cvar-Based Robust Models For Portfolio Selection |
| title | Cvar-Based Robust Models For Portfolio Selection |
| title_full | Cvar-Based Robust Models For Portfolio Selection |
| title_fullStr | Cvar-Based Robust Models For Portfolio Selection |
| title_full_unstemmed | Cvar-Based Robust Models For Portfolio Selection |
| title_short | Cvar-Based Robust Models For Portfolio Selection |
| title_sort | cvar-based robust models for portfolio selection |
| topic | Science & Technology Technology Physical Sciences Engineering, Multidisciplinary Operations Research & Management Science Mathematics, Interdisciplinary Applications Engineering Mathematics Portfolio optimization risk measure CVaR distributionally robust optimization conic optimization OPTIMIZATION |
| url | http://hdl.handle.net/20.500.11937/91432 |