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Estimating stochastic volatility models with student-t distributed errors
This dissertation aims to extend on the idea of Bollerslev (1987), estimating ARCH models with Student-t distributed errors, to estimating Stochastic Volatility (SV) models with Student-t distributed errors. It is unclear whether Gaussian distributed errors sufficiently account for the observed lept...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2020
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| _version_ | 1867614033749737472 |
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| access_status_str | Open Access |
| author | Rama, Vishal |
| author2 | Kulikova, Maria |
| author_browse | Kulikova, Maria Rama, Vishal |
| author_facet | Kulikova, Maria Rama, Vishal |
| author_sort | Rama, Vishal |
| collection | Thesis |
| description | This dissertation aims to extend on the idea of Bollerslev (1987), estimating ARCH models with Student-t distributed errors, to estimating Stochastic Volatility (SV) models with Student-t distributed errors. It is unclear whether Gaussian distributed errors sufficiently account for the observed leptokurtosis in financial time series and hence the extension to examine Student-t distributed errors for these models. The quasi-maximum likelihood estimation approach introduced by Harvey (1989) and the conventional Kalman filter technique are described so that the SV model with Gaussian distributed errors and SV model with Student-t distributed errors can be estimated. Estimation of GARCH (1,1) models is also described using the method maximum likelihood. The empirical study estimated four models using data on four different share return series and one index return, namely: Anglo American, BHP, FirstRand, Standard Bank Group and JSE Top 40 index. The GARCH and SV model with Student-t distributed errors both perform best on the series examined in this dissertation. The metric used to determine the best performing model was the Akaike information criterion (AIC). |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/32390 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:45:36.947Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/32390 Estimating stochastic volatility models with student-t distributed errors Rama, Vishal Kulikova, Maria Mavuso, Melusi Decision Sciences and Analytics This dissertation aims to extend on the idea of Bollerslev (1987), estimating ARCH models with Student-t distributed errors, to estimating Stochastic Volatility (SV) models with Student-t distributed errors. It is unclear whether Gaussian distributed errors sufficiently account for the observed leptokurtosis in financial time series and hence the extension to examine Student-t distributed errors for these models. The quasi-maximum likelihood estimation approach introduced by Harvey (1989) and the conventional Kalman filter technique are described so that the SV model with Gaussian distributed errors and SV model with Student-t distributed errors can be estimated. Estimation of GARCH (1,1) models is also described using the method maximum likelihood. The empirical study estimated four models using data on four different share return series and one index return, namely: Anglo American, BHP, FirstRand, Standard Bank Group and JSE Top 40 index. The GARCH and SV model with Student-t distributed errors both perform best on the series examined in this dissertation. The metric used to determine the best performing model was the Akaike information criterion (AIC). 2020-11-12T08:36:19Z 2020-11-12T08:36:19Z 2020 2020-11-12T08:35:24Z Master Thesis Masters MSc http://hdl.handle.net/11427/32390 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Decision Sciences and Analytics Rama, Vishal Estimating stochastic volatility models with student-t distributed errors |
| thesis_degree_str | Master's |
| title | Estimating stochastic volatility models with student-t distributed errors |
| title_full | Estimating stochastic volatility models with student-t distributed errors |
| title_fullStr | Estimating stochastic volatility models with student-t distributed errors |
| title_full_unstemmed | Estimating stochastic volatility models with student-t distributed errors |
| title_short | Estimating stochastic volatility models with student-t distributed errors |
| title_sort | estimating stochastic volatility models with student t distributed errors |
| topic | Decision Sciences and Analytics |
| url | http://hdl.handle.net/11427/32390 |
| work_keys_str_mv | AT ramavishal estimatingstochasticvolatilitymodelswithstudenttdistributederrors |