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Systematic asset allocation using flexible views for South African markets
We implement a systematic asset allocation model using the Historical Simulation with Flexible Probabilities (HS-FP) framework developed by Meucci [142, 144, 145]. The HS-FP framework is a flexible non-parametric estimation approach that considers future asset class behavior to be conditional on tim...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2022
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| _version_ | 1867613176861818880 |
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| access_status_str | Open Access |
| author | Sebastian, Ponni |
| author2 | Gebbie, Timothy |
| author_browse | Gebbie, Timothy Sebastian, Ponni |
| author_facet | Gebbie, Timothy Sebastian, Ponni |
| author_sort | Sebastian, Ponni |
| collection | Thesis |
| description | We implement a systematic asset allocation model using the Historical Simulation with Flexible Probabilities (HS-FP) framework developed by Meucci [142, 144, 145]. The HS-FP framework is a flexible non-parametric estimation approach that considers future asset class behavior to be conditional on time and market environments, and derives a forward-looking distribution that is consistent with this view while remaining as close as possible to the prior distribution. The framework derives the forward-looking distribution by applying unequal time and state conditioned probabilities to historical observations of asset class returns. This is achieved using relative entropy to find estimates with the least distortion to the prior distribution. Here, we use the HS-FP framework on South African financial market data for asset allocation purposes; by estimating expected returns, correlations and volatilities that are better represented through the measured market cycle. We demonstrate a range of state variables that can be useful towards understanding market environments. Concretely, we compare the out-of-sample performance for a specific configuration of the HS-FP model relative to classic Mean Variance Optimization(MVO) and Equally Weighted (EW) benchmark models. The framework displays low probability of backtest overfitting and the out-of-sample net returns and Sharpe ratio point estimates of the HS-FP model outperforms the benchmark models. However, the results are inconsistent when training windows are varied, the Sharpe ratio is seen to be inflated, and the method does not demonstrate statistically significant outperformance on a gross and net basis. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/36094 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:58.458Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/36094 Systematic asset allocation using flexible views for South African markets Sebastian, Ponni Gebbie, Timothy Statistical Sciences We implement a systematic asset allocation model using the Historical Simulation with Flexible Probabilities (HS-FP) framework developed by Meucci [142, 144, 145]. The HS-FP framework is a flexible non-parametric estimation approach that considers future asset class behavior to be conditional on time and market environments, and derives a forward-looking distribution that is consistent with this view while remaining as close as possible to the prior distribution. The framework derives the forward-looking distribution by applying unequal time and state conditioned probabilities to historical observations of asset class returns. This is achieved using relative entropy to find estimates with the least distortion to the prior distribution. Here, we use the HS-FP framework on South African financial market data for asset allocation purposes; by estimating expected returns, correlations and volatilities that are better represented through the measured market cycle. We demonstrate a range of state variables that can be useful towards understanding market environments. Concretely, we compare the out-of-sample performance for a specific configuration of the HS-FP model relative to classic Mean Variance Optimization(MVO) and Equally Weighted (EW) benchmark models. The framework displays low probability of backtest overfitting and the out-of-sample net returns and Sharpe ratio point estimates of the HS-FP model outperforms the benchmark models. However, the results are inconsistent when training windows are varied, the Sharpe ratio is seen to be inflated, and the method does not demonstrate statistically significant outperformance on a gross and net basis. 2022-03-15T11:54:26Z 2022-03-15T11:54:26Z 2021 2022-03-15T11:18:17Z Master Thesis Masters MSc http://hdl.handle.net/11427/36094 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistical Sciences Sebastian, Ponni Systematic asset allocation using flexible views for South African markets |
| thesis_degree_str | Master's |
| title | Systematic asset allocation using flexible views for South African markets |
| title_full | Systematic asset allocation using flexible views for South African markets |
| title_fullStr | Systematic asset allocation using flexible views for South African markets |
| title_full_unstemmed | Systematic asset allocation using flexible views for South African markets |
| title_short | Systematic asset allocation using flexible views for South African markets |
| title_sort | systematic asset allocation using flexible views for south african markets |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/36094 |
| work_keys_str_mv | AT sebastianponni systematicassetallocationusingflexibleviewsforsouthafricanmarkets |