Volatility forecasting using Double-Markov switching GARCH models under skewed Student-t distribution

 

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dc.contributor.advisor Clark, Allan en_ZA
dc.contributor.author Mazviona, Batsirai Winmore en_ZA
dc.date.accessioned 2015-02-03T18:30:35Z
dc.date.available 2015-02-03T18:30:35Z
dc.date.issued 2012 en_ZA
dc.identifier.citation Mazviona, B. 2012. Volatility forecasting using Double-Markov switching GARCH models under skewed Student-t distribution. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/12344
dc.description Includes bibliographical references. en_ZA
dc.description.abstract This thesis focuses on forecasting the volatility of daily returns using a double Markov switching GARCH model with a skewed Student-t error distribution. The model was applied to individual shares obtained from the Johannesburg Stock Exchange (JSE). The Bayesian approach which uses Markov Chain Monte Carlo was used to estimate the unknown parameters in the model. The double Markov switching GARCH model was compared to a GARCH(1,1) model. Value at risk thresholds and violations ratios were computed leading to the ranking of the GARCH and double Markov switching GARCH models. The results showed that double Markov switching GARCH model performs similarly to the GARCH model based on the ranking technique employed in this thesis. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Mathematical Finance en_ZA
dc.title Volatility forecasting using Double-Markov switching GARCH models under skewed Student-t distribution 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 MPhil en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Mazviona, B. W. (2012). <i>Volatility forecasting using Double-Markov switching GARCH models under skewed Student-t distribution</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/12344 en_ZA
dc.identifier.chicagocitation Mazviona, Batsirai Winmore. <i>"Volatility forecasting using Double-Markov switching GARCH models under skewed Student-t distribution."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2012. http://hdl.handle.net/11427/12344 en_ZA
dc.identifier.vancouvercitation Mazviona BW. Volatility forecasting using Double-Markov switching GARCH models under skewed Student-t distribution. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2012 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/12344 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Mazviona, Batsirai Winmore AB - This thesis focuses on forecasting the volatility of daily returns using a double Markov switching GARCH model with a skewed Student-t error distribution. The model was applied to individual shares obtained from the Johannesburg Stock Exchange (JSE). The Bayesian approach which uses Markov Chain Monte Carlo was used to estimate the unknown parameters in the model. The double Markov switching GARCH model was compared to a GARCH(1,1) model. Value at risk thresholds and violations ratios were computed leading to the ranking of the GARCH and double Markov switching GARCH models. The results showed that double Markov switching GARCH model performs similarly to the GARCH model based on the ranking technique employed in this thesis. DA - 2012 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2012 T1 - Volatility forecasting using Double-Markov switching GARCH models under skewed Student-t distribution TI - Volatility forecasting using Double-Markov switching GARCH models under skewed Student-t distribution UR - http://hdl.handle.net/11427/12344 ER - en_ZA


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