Robust portfolio optimisation with filtering uncertainty
<p>This thesis focuses on how portfolio optimisation can be carried out under different types of uncertainty, which we often measure through the use of filters. Chapter 1 motivates the problem, gives an overview of the thesis and covers some necessary background material. Chapter 2 deals with...
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| Other Authors: | |
| Format: | Thesis |
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2017
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| _version_ | 1797057843286769664 |
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| author | Simões, G |
| author2 | Hauser, R |
| author_facet | Hauser, R Simões, G |
| author_sort | Simões, G |
| collection | OXFORD |
| description | <p>This thesis focuses on how portfolio optimisation can be carried out under different types of uncertainty, which we often measure through the use of filters. Chapter 1 motivates the problem, gives an overview of the thesis and covers some necessary background material. Chapter 2 deals with uncertainty in the covariance matrix and how by identifying different regimes we can solve optimisation problems of interest to practitioners.</p> <p>Chapter 3 focuses on the uncertainty over tail events and how we can not only extract relevant information by filtering the data but also how we can use that information to construct a portfolio optimisation problem that acts on it. In Chapter 4 we address the lack of tractability for general relative robust portfolio optimisation problems and how one can overcome this so as to make it a viable tool.</p> <p>Chapter 5 considers the problem of uncertainty in the filter itself and how this uncertainty can be fully incorporated in the portfolio optimisation problem. Finally in Chapter 6 we conclude and propose topics for future research.</p> |
| first_indexed | 2024-03-06T19:42:08Z |
| format | Thesis |
| id | oxford-uuid:210c56b6-d005-4bf7-a3ee-9aa85416f908 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T19:42:08Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | oxford-uuid:210c56b6-d005-4bf7-a3ee-9aa85416f9082022-03-26T11:31:08ZRobust portfolio optimisation with filtering uncertaintyThesishttp://purl.org/coar/resource_type/c_db06uuid:210c56b6-d005-4bf7-a3ee-9aa85416f908ORA Deposit2017Simões, GHauser, R<p>This thesis focuses on how portfolio optimisation can be carried out under different types of uncertainty, which we often measure through the use of filters. Chapter 1 motivates the problem, gives an overview of the thesis and covers some necessary background material. Chapter 2 deals with uncertainty in the covariance matrix and how by identifying different regimes we can solve optimisation problems of interest to practitioners.</p> <p>Chapter 3 focuses on the uncertainty over tail events and how we can not only extract relevant information by filtering the data but also how we can use that information to construct a portfolio optimisation problem that acts on it. In Chapter 4 we address the lack of tractability for general relative robust portfolio optimisation problems and how one can overcome this so as to make it a viable tool.</p> <p>Chapter 5 considers the problem of uncertainty in the filter itself and how this uncertainty can be fully incorporated in the portfolio optimisation problem. Finally in Chapter 6 we conclude and propose topics for future research.</p> |
| spellingShingle | Simões, G Robust portfolio optimisation with filtering uncertainty |
| title | Robust portfolio optimisation with filtering uncertainty |
| title_full | Robust portfolio optimisation with filtering uncertainty |
| title_fullStr | Robust portfolio optimisation with filtering uncertainty |
| title_full_unstemmed | Robust portfolio optimisation with filtering uncertainty |
| title_short | Robust portfolio optimisation with filtering uncertainty |
| title_sort | robust portfolio optimisation with filtering uncertainty |
| work_keys_str_mv | AT simoesg robustportfoliooptimisationwithfilteringuncertainty |