Statistical arbitrage in South African equity markets

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dc.contributor.advisorLubbe, Sugnet; Kotze, Kevinen_ZA
dc.contributor.authorMasindi, Khuthadzoen_ZA
dc.date.accessioned2015-07-14T08:44:12Z
dc.date.available2015-07-14T08:44:12Z
dc.date.issued2014en_ZA
dc.description.abstractThe dissertation implements a model driven statistical arbitrage strategy that uses the principal components from Principal Component Analysis as factors in a multi-factor stock model, to isolate the idiosyncratic component of returns, which is then modelled as an Ornstein Uhlenbeck process. The idiosyncratic process (referred to as the residual process) is estimated in discrete-time by an auto-regressive process with one lag (or AR(1) process). Trading signals are generated based on the level of the residual process. This strategy is then evaluated over historical data for the South African equity market from 2001 to 2013 through backtesting. In addition the strategy is evaluated over data generated from Monte Carlo simulations as well as bootstrapped historical data. The results show that the strategy was able to significantly out-perform cash for most of the periods under consideration. The performance of the strategy over data that was generated from Monte Carlo simulations demonstrated that the strategy is not suitable for markets that are asymptotically efficient.en_ZA
dc.identifier.apacitationMasindi, K. (2014). <i>Statistical arbitrage in South African equity markets</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science. Retrieved from http://hdl.handle.net/11427/13427en_ZA
dc.identifier.chicagocitationMasindi, Khuthadzo. <i>"Statistical arbitrage in South African equity markets."</i> Thesis., University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2014. http://hdl.handle.net/11427/13427en_ZA
dc.identifier.citationMasindi, K. 2014. Statistical arbitrage in South African equity markets. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Masindi, Khuthadzo AB - The dissertation implements a model driven statistical arbitrage strategy that uses the principal components from Principal Component Analysis as factors in a multi-factor stock model, to isolate the idiosyncratic component of returns, which is then modelled as an Ornstein Uhlenbeck process. The idiosyncratic process (referred to as the residual process) is estimated in discrete-time by an auto-regressive process with one lag (or AR(1) process). Trading signals are generated based on the level of the residual process. This strategy is then evaluated over historical data for the South African equity market from 2001 to 2013 through backtesting. In addition the strategy is evaluated over data generated from Monte Carlo simulations as well as bootstrapped historical data. The results show that the strategy was able to significantly out-perform cash for most of the periods under consideration. The performance of the strategy over data that was generated from Monte Carlo simulations demonstrated that the strategy is not suitable for markets that are asymptotically efficient. DA - 2014 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - Statistical arbitrage in South African equity markets TI - Statistical arbitrage in South African equity markets UR - http://hdl.handle.net/11427/13427 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/13427
dc.identifier.vancouvercitationMasindi K. Statistical arbitrage in South African equity markets. [Thesis]. University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2014 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/13427en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDivision of Actuarial Scienceen_ZA
dc.publisher.facultyFaculty of Commerceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherMathematical Financeen_ZA
dc.titleStatistical arbitrage in South African equity marketsen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMPhilen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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