Enhanced minimum variance optimisation: a pragmatic approach

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dc.contributor.advisorBradfield, Daviden_ZA
dc.contributor.advisorBrandt, Tobiasen_ZA
dc.contributor.authorLakhoo, Lala Bernisha Jantien_ZA
dc.date.accessioned2017-01-31T09:11:46Z
dc.date.available2017-01-31T09:11:46Z
dc.date.issued2016en_ZA
dc.description.abstractSince the establishment of Markowitz's theory, numerous studies have been carried out over the past six decades or so that cover the benefits, limitations, modifications and enhancements of Mean Variance (MV) optimisation. This study endeavours to extend on this, by means of adding factors to the minimum variance framework, which would increase the likelihood of outperforming both the market and the minimum variance portfolio (MVP). An analysis of the impact of these factor tilts on the MVP is carried out in the South African environment, represented by the FTSE-JSE Shareholder weighted Index as the benchmark portfolio. The main objective is to examine if the systematic and robust methods employed, which involve the incorporation of factor tilts into the multicriteria problem, together with covariance shrinkage – improve the performance of the MVP. The factor tilts examined include Active Distance, Concentration and Volume. Additionally, the constant correlation model is employed in the estimation of the shrinkage intensity, structured covariance target and shrinkage estimator. The results of this study showed that with specific levels of factor tilting, one can generally improve both absolute and risk-adjusted performance and lower concentration levels in comparison to both the MVP and benchmark. Additionally, lower turnover levels were observed across all tilted portfolios, relative to the MVP. Furthermore, covariance shrinkage enhanced all portfolio statistics examined, but significant improvement was noted on drawdown levels, capture ratios and risk. This is in contrast to the results obtained when the standard sample covariance matrix was employed.en_ZA
dc.identifier.apacitationLakhoo, L. B. J. (2016). <i>Enhanced minimum variance optimisation: a pragmatic approach</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/23764en_ZA
dc.identifier.chicagocitationLakhoo, Lala Bernisha Janti. <i>"Enhanced minimum variance optimisation: a pragmatic approach."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2016. http://hdl.handle.net/11427/23764en_ZA
dc.identifier.citationLakhoo, L. 2016. Enhanced minimum variance optimisation: a pragmatic approach. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Lakhoo, Lala Bernisha Janti AB - Since the establishment of Markowitz's theory, numerous studies have been carried out over the past six decades or so that cover the benefits, limitations, modifications and enhancements of Mean Variance (MV) optimisation. This study endeavours to extend on this, by means of adding factors to the minimum variance framework, which would increase the likelihood of outperforming both the market and the minimum variance portfolio (MVP). An analysis of the impact of these factor tilts on the MVP is carried out in the South African environment, represented by the FTSE-JSE Shareholder weighted Index as the benchmark portfolio. The main objective is to examine if the systematic and robust methods employed, which involve the incorporation of factor tilts into the multicriteria problem, together with covariance shrinkage – improve the performance of the MVP. The factor tilts examined include Active Distance, Concentration and Volume. Additionally, the constant correlation model is employed in the estimation of the shrinkage intensity, structured covariance target and shrinkage estimator. The results of this study showed that with specific levels of factor tilting, one can generally improve both absolute and risk-adjusted performance and lower concentration levels in comparison to both the MVP and benchmark. Additionally, lower turnover levels were observed across all tilted portfolios, relative to the MVP. Furthermore, covariance shrinkage enhanced all portfolio statistics examined, but significant improvement was noted on drawdown levels, capture ratios and risk. This is in contrast to the results obtained when the standard sample covariance matrix was employed. DA - 2016 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2016 T1 - Enhanced minimum variance optimisation: a pragmatic approach TI - Enhanced minimum variance optimisation: a pragmatic approach UR - http://hdl.handle.net/11427/23764 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/23764
dc.identifier.vancouvercitationLakhoo LBJ. Enhanced minimum variance optimisation: a pragmatic approach. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2016 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/23764en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Statistical Sciencesen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherStatistical Sciencesen_ZA
dc.subject.otherAdvanced Analytics And Decision Sciencesen_ZA
dc.titleEnhanced minimum variance optimisation: a pragmatic approachen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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