Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market

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.

Grantor dc:publisher.institution

Department of Mathematics and Applied Mathematics

Year dc:date.issued

2011

Author dc:creator

• Savanhu, Richard

Advisor dc:contributor.advisor

• Becker, Ronald

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/11085

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/11085