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Portfolio optimisation using alternative risk measures

Dissertation (MPhil (Finance and Economics))--University of Pretoria, 2023.

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Other Authors: Van Schalkwyk, Cornelis Hendrik
Format: Thesis
Language:English
Published: University of Pretoria 2024
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access_status_str Open Access
author2 Van Schalkwyk, Cornelis Hendrik
author_browse Van Schalkwyk, Cornelis Hendrik
author_facet Van Schalkwyk, Cornelis Hendrik
collection Thesis
dc_rights_str_mv © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
description Dissertation (MPhil (Finance and Economics))--University of Pretoria, 2023.
format Thesis
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institution University of Pretoria (South Africa)
language English
last_indexed 2026-06-10T12:38:39.945Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository
publishDate 2024
publishDateRange 2024
publishDateSort 2024
publisher University of Pretoria
publisherStr University of Pretoria
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source_str UPSpace — University of Pretoria Institutional Repository
spelling oai:repository.up.ac.za:2263/95533 Portfolio optimisation using alternative risk measures Van Schalkwyk, Cornelis Hendrik doug@verdigris.co.za Szczygielski, Jan Jakub Lorimer, Douglas UCTD Modern portfolio theory Risk measures Portfolio optimisation Investment strategy Quantitative Dissertation (MPhil (Finance and Economics))--University of Pretoria, 2023. Markowitz’ Modern Portfolio Theory (MPT) optimises the ratio of mean portfolio returns and portfolio risk in the form of the variance of returns, giving rise to criticism relating to, inter alia, minimising upside risk, the assumption of normally-distributed returns, and a failure to recognise heteroskedasticity. In addressing these criticisms, this research investigates the use of alternative risk measures to optimise risk and return in MPT investment strategies using non-parametric numerical methods to optimise portfolios comprising assets from the S&P 1200 and MSCI GICS world indices. It investigates, in particular, downside semivariance, downside semideviation, mean absolute deviation, semi-absolute deviation, value at risk and conditional value at risk. In addition, the study investigates optimisation using backward-looking and forward-looking risk measures through exponentially-weighted moving average forecasts of risk measures and return. In general, all the alternative risk measures investigated result in investment strategies with higher returns than traditional MPT variance-optimised strategies, with semi-absolute deviation-optimised strategies performing best of all. The introduction of risk and return forecasting does not materially impact on strategy performance. Economics MPhil (Finance and Economics) Unrestricted Faculty of Economic And Management Sciences 2024-04-15T14:42:53Z 2024-04-15T14:42:53Z 2024-04 2023-06 Dissertation * A2024 http://hdl.handle.net/2263/95533 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria
spellingShingle UCTD
Modern portfolio theory
Risk measures
Portfolio optimisation
Investment strategy
Quantitative
Portfolio optimisation using alternative risk measures
title Portfolio optimisation using alternative risk measures
title_full Portfolio optimisation using alternative risk measures
title_fullStr Portfolio optimisation using alternative risk measures
title_full_unstemmed Portfolio optimisation using alternative risk measures
title_short Portfolio optimisation using alternative risk measures
title_sort portfolio optimisation using alternative risk measures
topic UCTD
Modern portfolio theory
Risk measures
Portfolio optimisation
Investment strategy
Quantitative
url http://hdl.handle.net/2263/95533