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dc.contributor.advisorMontshiwa, T.V.
dc.contributor.advisorSeaketso, P.G.
dc.contributor.authorMakobea, O.S.
dc.date.accessioned2023-11-23T06:39:20Z
dc.date.available2023-11-23T06:39:20Z
dc.date.issued2023
dc.identifier.urihttps://orcid.org/0000-0003-1059-0051
dc.identifier.urihttp://hdl.handle.net/10394/42343
dc.descriptionMCom (Statistics with Business Statistics), North-West University, Mahikeng Campusen_US
dc.description.abstractThe study explored the three-phase approach of SARIMA following the Box-Jenkins methodology, GARCH, and hybrid SARIMA-GARCH models. These volatility forecasting models were used to model electricity consumption in South Africa. The SARIMA (1, 1, 2)(0, 1, 1)12 model was found to be adequate to model South African electricity consumption. However, the series exhibits the presence of the ARCH effect, which led to modelling a GARCH model. The GARCH (1, 1) was modelled and confirmed to be a good fit. Furthermore, the study fitted the residual of the SARIMA model into the GARCH model to make it a hybrid SARIMA (1, 1, 2)(0, 1, 1)12-GARCH (1,1) model. It was observed that the hybrid model is the best fit model, with the smallest AIC value, and the diagnostic checking also confirmed that the model is adequate for forecasting electricity consumption. The results of the hybrid SARIMA (1, 1, 2)(0, 1, 1)12-GARCH (1, 1) model showed that the electricity consumption series has the highest volatility persistence value, and unconditional volatility for the series is finite.en_US
dc.language.isoenen_US
dc.publisherNorth-West University (South Africa)en_US
dc.subjectElectricity consumptionen_US
dc.subjectGARCHen_US
dc.subjectHybrid SARIMA-GARCHen_US
dc.subjectSARIMA volatilityen_US
dc.titleAnalysing electricity consumption in South Africa using volatility forecasting modelsen_US
dc.typeThesisen_US
dc.description.thesistypeMastersen_US
dc.contributor.researchID22297812 - Montshiwa, Volition Tlhalitshi (Supervisor)
dc.contributor.researchID16151720 - Seaketso, Phemelo Godfrey (Supervisor)


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