Enhancing CVaR portfolio optimisation performance with GAM factor models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910284878184448 |
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| author | Lauria, Davide Lindquist, W. Brent Rachev, Svetlozar T. |
| author_facet | Lauria, Davide Lindquist, W. Brent Rachev, Svetlozar T. |
| contents | We propose a discrete-time econometric model that combines autoregressive filters with factor regressions to predict stock returns for portfolio optimisation purposes. In particular, we test both robust linear regressions and general additive models on two different investment universes composed of the Dow Jones Industrial Average and the Standard & Poor's 500 indexes, and we compare the out-of-sample performances of mean-CVaR optimal portfolios over a horizon of six years. The results show a substantial improvement in portfolio performances when the factor model is estimated with general additive models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00188 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Enhancing CVaR portfolio optimisation performance with GAM factor models Lauria, Davide Lindquist, W. Brent Rachev, Svetlozar T. Portfolio Management We propose a discrete-time econometric model that combines autoregressive filters with factor regressions to predict stock returns for portfolio optimisation purposes. In particular, we test both robust linear regressions and general additive models on two different investment universes composed of the Dow Jones Industrial Average and the Standard & Poor's 500 indexes, and we compare the out-of-sample performances of mean-CVaR optimal portfolios over a horizon of six years. The results show a substantial improvement in portfolio performances when the factor model is estimated with general additive models. |
| title | Enhancing CVaR portfolio optimisation performance with GAM factor models |
| topic | Portfolio Management |
| url | https://arxiv.org/abs/2401.00188 |