Scenario wise distributionally robust optimization for Conditional Value-at-Risk
Throughout the years, investors have been formulating investment strategies to best allocate their portfolio through optimization to get consistent returns. In year 1996, Bridgewater launched its All Weather Strategy to provide investors with consistent returns across a wide range of economic enviro...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2019
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| Online Access: | https://hdl.handle.net/10356/136489 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Throughout the years, investors have been formulating investment strategies to best allocate their portfolio through optimization to get consistent returns. In year 1996, Bridgewater launched its All Weather Strategy to provide investors with consistent returns across a wide range of economic environments via a risk-balanced portfolio of asset classes. This has gained significant traction and many asset managers have utilized this strategy in developing their portfolios. However, there is no existing literature that further improves this strategy and optimizes the parameters via machine learning. The purpose of this research is to construct a methodology that enables us to improve on the existing strategy and optimize the parameters via machine learning, in particular, using Hidden Markov Model. |
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