Hedge fund of funds investment process : a South African perspective
Includes bibliographical references. === The objective of this dissertation is to develop and test an investment process for hedge fund of funds (HFoFs) in South Africa. The dissertation proposes a three tiered process, adapted from the works of Lo (2008). Step one of the proccess involves the ca...
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ndltd-netd.ac.za-oai-union.ndltd.org-uct-oai-localhost-11427-85282020-10-06T05:10:53Z Hedge fund of funds investment process : a South African perspective Hossain, Mahzabeen Natasha Lubbe, Sugnet Mathematical Finance Includes bibliographical references. The objective of this dissertation is to develop and test an investment process for hedge fund of funds (HFoFs) in South Africa. The dissertation proposes a three tiered process, adapted from the works of Lo (2008). Step one of the proccess involves the categorisation of hedge funds into broadly defined groups based on predefined factors. Two classification methodologies are examined herein to determine optimal category definitions. These are 1) an adaption of the classification developed by Schneeweis and Spurgin (2000), based on the correlation of hedge funds to an appropriate benchmark and the returns offered by these hedge funds, and 2) classification by cluster analysis. Once a finite set of classification is defined, step two of the process uses a minimum variance optimisation, based on forward-looking parameter estimates of return and co-variance to compute the optimal capital allocation to these categories. The final stage of the process employs a mixture of quantitative and qualitative analysis to allocate capital within categories to individual hedge funds. 2014-10-17T10:09:56Z 2014-10-17T10:09:56Z 2014 Master Thesis Masters MPhil http://hdl.handle.net/11427/8528 eng application/pdf University of Cape Town Faculty of Commerce Division of Actuarial Science |
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English |
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Dissertation |
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Mathematical Finance |
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Mathematical Finance Hossain, Mahzabeen Natasha Hedge fund of funds investment process : a South African perspective |
description |
Includes bibliographical references. === The objective of this dissertation is to develop and test an investment process for hedge fund of funds (HFoFs) in South Africa. The dissertation proposes a three tiered process, adapted from the works of Lo (2008). Step one of the proccess involves the categorisation of hedge funds into broadly defined groups based on predefined factors. Two classification methodologies are examined herein to determine optimal category definitions. These are 1) an adaption of the classification developed by Schneeweis and Spurgin (2000), based on the correlation of hedge funds to an appropriate benchmark and the returns offered by these hedge funds, and 2) classification by cluster analysis. Once a finite set of classification is defined, step two of the process uses a minimum variance optimisation, based on forward-looking parameter estimates of return and co-variance to compute the optimal capital allocation to these categories. The final stage of the process employs a mixture of quantitative and qualitative analysis to allocate capital within categories to individual hedge funds. |
author2 |
Lubbe, Sugnet |
author_facet |
Lubbe, Sugnet Hossain, Mahzabeen Natasha |
author |
Hossain, Mahzabeen Natasha |
author_sort |
Hossain, Mahzabeen Natasha |
title |
Hedge fund of funds investment process : a South African perspective |
title_short |
Hedge fund of funds investment process : a South African perspective |
title_full |
Hedge fund of funds investment process : a South African perspective |
title_fullStr |
Hedge fund of funds investment process : a South African perspective |
title_full_unstemmed |
Hedge fund of funds investment process : a South African perspective |
title_sort |
hedge fund of funds investment process : a south african perspective |
publisher |
University of Cape Town |
publishDate |
2014 |
url |
http://hdl.handle.net/11427/8528 |
work_keys_str_mv |
AT hossainmahzabeennatasha hedgefundoffundsinvestmentprocessasouthafricanperspective |
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1719347982247133184 |