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Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market
Includes bibliographical references (leaves 39-40).
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| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Mathematics and Applied Mathematics
2015
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| _version_ | 1867613289708519425 |
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| access_status_str | Open Access |
| author | Savanhu, Richard |
| author2 | Becker, Ronald |
| author_browse | Becker, Ronald Savanhu, Richard |
| author_facet | Becker, Ronald Savanhu, Richard |
| author_sort | Savanhu, Richard |
| collection | Thesis |
| description | Includes bibliographical references (leaves 39-40). |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/11085 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:45.686Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Department of Mathematics and Applied Mathematics |
| publisherStr | Department of Mathematics and Applied Mathematics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/11085 Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market Savanhu, Richard Becker, Ronald Mathematics of Finance Includes bibliographical references (leaves 39-40). In this study we apply Markov Chain Monte Carlo methods in the Bayesian framework to estimate Stochastic Volatility models using South African financial market data. A single move Gibbs sampler is used to sample parameters from the posterior distribution. Volatility is used as measure of an asset's risk. It is particularly important in risk management, derivatives pricing, and portfolio selection. When pricing derivatives it is important to quote the correct volatility trading in the market, hence there is need for good estimates of volatility. To capture the stylised facts about asset returns we used the model extended for fat tails and correlated errors. To support this model against the basic model of Taylor (1986), we computed Bayes Factors of Jacquier, Polson and Ross (2004). The extended model was found to be far superior to the basic model. 2015-01-03T05:28:58Z 2015-01-03T05:28:58Z 2011 Master Thesis Masters MPhil http://hdl.handle.net/11427/11085 eng application/pdf Department of Mathematics and Applied Mathematics Faculty of Science University of Cape Town |
| spellingShingle | Mathematics of Finance Savanhu, Richard Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market |
| thesis_degree_str | Master's |
| title | Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market |
| title_full | Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market |
| title_fullStr | Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market |
| title_full_unstemmed | Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market |
| title_short | Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market |
| title_sort | bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the south african financial market |
| topic | Mathematics of Finance |
| url | http://hdl.handle.net/11427/11085 |
| work_keys_str_mv | AT savanhurichard bayesianestimationofstochasticvolatilitymodelswithfattailsandcorrelatederrorsappliedtothesouthafricanfinancialmarket |