Empirical modelling of high-frequency foreign exchange rates

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dc.contributor.advisorGuo, Renkuanen_ZA
dc.contributor.authorPackirisamy, Someshinien_ZA
dc.date.accessioned2014-08-02T14:48:03Z
dc.date.available2014-08-02T14:48:03Z
dc.date.issued2004en_ZA
dc.descriptionIncludes bibliographical references (leaves 213-219).
dc.description.abstractThere is a wealth of information available on modelling foreign exchange time series data, however, research studies on modelling and predicting high frequency foreign exchange data is less prominent. Furthermore, there does not appear to be much evidence supporting work on the modelling and prediction of high frequency South African Rand/United States Dollar (ZAR/USD) exchange rates. A fair amount of noise is embedded in high frequency time series data, especially the ZAR/USD exchange rates, and the modelling of these time series requires the use of specialized models. In addition, lengthy high frequency foreign exchange data is largely unavailable for the South African market. This dissertation undertakes empirical explorations to model high frequency foreign exchange time series (primarily the ZAR/USD time series), through the use of multi-agent neural networks, linear Kalman filters and fuzzy Markov chain theory.en_ZA
dc.identifier.apacitationPackirisamy, S. (2004). <i>Empirical modelling of high-frequency foreign exchange rates</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Mathematics and Applied Mathematics. Retrieved from http://hdl.handle.net/11427/5963en_ZA
dc.identifier.chicagocitationPackirisamy, Someshini. <i>"Empirical modelling of high-frequency foreign exchange rates."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 2004. http://hdl.handle.net/11427/5963en_ZA
dc.identifier.citationPackirisamy, S. 2004. Empirical modelling of high-frequency foreign exchange rates. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Packirisamy, Someshini AB - There is a wealth of information available on modelling foreign exchange time series data, however, research studies on modelling and predicting high frequency foreign exchange data is less prominent. Furthermore, there does not appear to be much evidence supporting work on the modelling and prediction of high frequency South African Rand/United States Dollar (ZAR/USD) exchange rates. A fair amount of noise is embedded in high frequency time series data, especially the ZAR/USD exchange rates, and the modelling of these time series requires the use of specialized models. In addition, lengthy high frequency foreign exchange data is largely unavailable for the South African market. This dissertation undertakes empirical explorations to model high frequency foreign exchange time series (primarily the ZAR/USD time series), through the use of multi-agent neural networks, linear Kalman filters and fuzzy Markov chain theory. DA - 2004 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2004 T1 - Empirical modelling of high-frequency foreign exchange rates TI - Empirical modelling of high-frequency foreign exchange rates UR - http://hdl.handle.net/11427/5963 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/5963
dc.identifier.vancouvercitationPackirisamy S. Empirical modelling of high-frequency foreign exchange rates. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 2004 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/5963en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Mathematics and Applied Mathematicsen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherMathematics of Financeen_ZA
dc.titleEmpirical modelling of high-frequency foreign exchange ratesen_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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