Robust portfolio optimization with covariates
In this project, we propose ARIMA regression as a methodology for the inclusion of covariate information into a robust CVaR minimization portfolio as a method to improve the performance of the portfolio optimization model. This methodology is compared with a robust CVaR minimization portfolio and an...
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/156906 |
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| 總結: | In this project, we propose ARIMA regression as a methodology for the inclusion of covariate information into a robust CVaR minimization portfolio as a method to improve the performance of the portfolio optimization model. This methodology is compared with a robust CVaR minimization portfolio and an equal weights portfolio and is found to have poor performance in terms of Sharpe ratio and certainty-equivalent return but exhibits better performance when it comes to maximum drawdown. This suggests that while the methodology is flawed, it still holds promise in certain niche applications. |
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