Risk management in multi-objective portfolio optimization under uncertainty
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914235384070144 |
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| author | Becker, Yannick Halffmann, Pascal Schöbel, Anita |
| author_facet | Becker, Yannick Halffmann, Pascal Schöbel, Anita |
| contents | In portfolio optimization, decision makers face difficulties from uncertainties inherent in real-world scenarios. These uncertainties significantly influence portfolio outcomes in both classical and multi-objective Markowitz models. To address these challenges, our research explores the power of robust multi-objective optimization. Since portfolio managers frequently measure their solutions against benchmarks, we enhance the multi-objective min-regret robustness concept by incorporating these benchmark comparisons.
This approach bridges the gap between theoretical models and real-world investment scenarios, offering portfolio managers more reliable and adaptable strategies for navigating market uncertainties. Our framework provides a more nuanced and practical approach to portfolio optimization under real-world conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19936 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Risk management in multi-objective portfolio optimization under uncertainty Becker, Yannick Halffmann, Pascal Schöbel, Anita Portfolio Management Optimization and Control In portfolio optimization, decision makers face difficulties from uncertainties inherent in real-world scenarios. These uncertainties significantly influence portfolio outcomes in both classical and multi-objective Markowitz models. To address these challenges, our research explores the power of robust multi-objective optimization. Since portfolio managers frequently measure their solutions against benchmarks, we enhance the multi-objective min-regret robustness concept by incorporating these benchmark comparisons. This approach bridges the gap between theoretical models and real-world investment scenarios, offering portfolio managers more reliable and adaptable strategies for navigating market uncertainties. Our framework provides a more nuanced and practical approach to portfolio optimization under real-world conditions. |
| title | Risk management in multi-objective portfolio optimization under uncertainty |
| topic | Portfolio Management Optimization and Control |
| url | https://arxiv.org/abs/2407.19936 |