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Long range dependence in South African Platinum prices under heavy tailed error distributions

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dc.contributor.advisor Ranganai, E. Kubheka, Sihle 2017-04-13T09:16:07Z 2017-04-13T09:16:07Z 2016-11
dc.identifier.citation Kubheka, Sihle (2016) Long range dependence in South African Platinum prices under heavy tailed error distributions, University of South Africa, Pretoria, <>
dc.description.abstract South Africa is rich in platinum group metals (PGMs) and these metals are important in providing jobs as well as investments some of which have been seen in the Johannesburg Securities Exchange (JSE). In this country this sector has experienced some setbacks in recent times. The most notable ones are the 2008/2009 global nancial crisis and the 2012 major nationwide labour unrest. Worrisomely, these setbacks keep simmering. These events usually introduce jumps and breaks in data which changes the structure of the underlying information thereby inducing spurious long memory (long range dependence). Thus it is recommended that these two phenomena must be addressed together. Further, it is well-known that nancial returns are dominated by stylized facts. In this thesis we carried out an investigation on distributional properties of platinum returns, structural changes, long memory and stylized facts in platinum returns and volatility series. To understand the distributional properties of the returns, we used two classes of heavy tailed distributions namely the alpha-Stable distributions and generalized hyperbolic distributions. We then investigated structural changes in the platinum return series and changes in long range dependence and volatility. Using Akaike information criterion, the ARFIMA-FIAPARCH under the Student distribution was selected as the best model for platinum although the ARCH e ects were slightly signi cant, while using the Schwarz information criteria the ARFIMA-FIAPARCH under the Normal distribution. Further, ARFIMA-FIEGARCH under the skewed Student distribution and ARFIMA-HYGARCH under the Normal distribution models were able to capture the ARCH effects. The best models with respect to prediction excluded the ARFIMA-FIGARCH model and were dominated by ARFIMA-FIAPARCH model with non-Normal error distributions which indicates the importance of asymmetry and heavy tailed error distributions. en
dc.format.extent 1 online resource (144 leaves) : illustrations en
dc.language.iso en en
dc.subject.ddc 338.47546630968
dc.subject.lcsh Platinum -- Prices -- South Africa -- Forecasting en
dc.subject.lcsh Forecasting -- Statistical Methods en
dc.subject.lcsh Gaussian distribution en
dc.subject.lcsh Differential equations, Hyperbolic en
dc.title Long range dependence in South African Platinum prices under heavy tailed error distributions en
dc.type Dissertation en
dc.description.department Statistics en M. Sc. (Statistics) en

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