An alternative model for multivariate stable distributions

 

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dc.contributor.advisor Guo, Renkuan en_ZA
dc.contributor.author Jama, Siphamandla en_ZA
dc.date.accessioned 2014-10-30T13:49:35Z
dc.date.available 2014-10-30T13:49:35Z
dc.date.issued 2009 en_ZA
dc.identifier.citation Jama, S. 2009. An alternative model for multivariate stable distributions. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/8959
dc.description Includes bibliographical references (leaves 52-55). en_ZA
dc.description.abstract As the title, "An Alternative Model for Multivariate Stable Distributions", depicts, this thesis draws from the methodology of [J36] and derives an alternative to the sub-Gaussian alpha-stable distribution as another model for multivariate stable data without using the spectral measure as a dependence structure. From our investigation, firstly, we echo that the assumption of "Gaussianity" must be rejected, as a model for, particularly, high frequency financial data based on evidence from the Johannesburg Stock Exchange (JSE). Secondly, the introduced technique adequately models bivariate return data far better than the Gaussian model. We argue that unlike the sub-Gaussian stable and the model involving a spectral measure this technique is not subject to estimation of a joint index of stability, as such it may remain a superior alternative in empirical stable distribution theory. Thirdly, we confirm that the Gaussian Value-at-Risk and Conditional Value-at-Risk measures are more optimistic and misleading while their stable counterparts are more informative and reasonable. Fourthly, our results confirm that stable distributions are more appropriate for portfolio optimization than the Gaussian framework. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Financial Mathematics en_ZA
dc.title An alternative model for multivariate stable distributions en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Science en_ZA
dc.publisher.department Department of Statistical Sciences en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Jama, S. (2009). <i>An alternative model for multivariate stable distributions</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/8959 en_ZA
dc.identifier.chicagocitation Jama, Siphamandla. <i>"An alternative model for multivariate stable distributions."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2009. http://hdl.handle.net/11427/8959 en_ZA
dc.identifier.vancouvercitation Jama S. An alternative model for multivariate stable distributions. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2009 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/8959 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Jama, Siphamandla AB - As the title, "An Alternative Model for Multivariate Stable Distributions", depicts, this thesis draws from the methodology of [J36] and derives an alternative to the sub-Gaussian alpha-stable distribution as another model for multivariate stable data without using the spectral measure as a dependence structure. From our investigation, firstly, we echo that the assumption of "Gaussianity" must be rejected, as a model for, particularly, high frequency financial data based on evidence from the Johannesburg Stock Exchange (JSE). Secondly, the introduced technique adequately models bivariate return data far better than the Gaussian model. We argue that unlike the sub-Gaussian stable and the model involving a spectral measure this technique is not subject to estimation of a joint index of stability, as such it may remain a superior alternative in empirical stable distribution theory. Thirdly, we confirm that the Gaussian Value-at-Risk and Conditional Value-at-Risk measures are more optimistic and misleading while their stable counterparts are more informative and reasonable. Fourthly, our results confirm that stable distributions are more appropriate for portfolio optimization than the Gaussian framework. DA - 2009 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2009 T1 - An alternative model for multivariate stable distributions TI - An alternative model for multivariate stable distributions UR - http://hdl.handle.net/11427/8959 ER - en_ZA


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