Show simple item record

dc.contributor.advisorRamroop, Shaun.
dc.contributor.advisorMwambi, Henry Godwell.
dc.creatorKaseke, Forbes.
dc.date.accessioned2016-09-26T07:43:45Z
dc.date.available2016-09-26T07:43:45Z
dc.date.created2015
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10413/13388
dc.descriptionMaster of Science in Statistics.en_US
dc.description.abstractFor investors and policy makers such as governments, the uncertainty of returns on investments is a major problem. The aim of this paper is to study volatility models for financial data for both univariate and multivariate case. The data to be used is monthly and daily asset returns of three different companies. For the univariate case, the main focus is on GARCH models and their subsequent derivatives. ARCH and GARCH models of different orders are fit. For the monthly data, the GARCH(1,1)outperformed the ARCH and higher order GARCH models. For the daily data, the GARCH(1,1) preceded by an appropriate AR model was the best fit. For the Multivariate volatility models, models such as the DCC-GARCH, EMWA and Go-GARCH were used. All three gave similar results. Various distributional assumptions such as the normal and Student t distributions were assumed for the innovations. Student t and Skewed Student t distributions were more effective because of their ability to capture fat tails of the distributions. Fundamental finance terms and concepts are also discussed.en_US
dc.language.isoen_ZAen_US
dc.subjectJohannesburg Stock Exchange.en_US
dc.subjectTrading rooms (Finance) -- South Africa -- Johannesburg -- Mathematical models.en_US
dc.subjectStock exchanges -- Ratings and rankings -- South Africa -- Johannesburg.en_US
dc.subjectTheses -- Statistics.en_US
dc.subjectGARCH models.en_US
dc.subjectARCH models.en_US
dc.titleModelling volatility in stock exchange data : a case study of three Johannesburg Stock Exchange (JSE) companies.en_US
dc.typeThesisen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record