On Stochastic Volatility Models as an Alternative to GARCH Type Models

For the purpose of modelling and prediction of volatility, the family of Stochastic Volatility (SV) models is an alternative to the extensively used ARCH type models. SV models differ in their assumption that volatility itself follows a latent stochastic process. This reformulation of the volatility process makes however model estimation distinctly more complicated for the SV type models, which in this paper is conducted through Markov Chain Monte Carlo methods. The aim of this paper is to assess the standard SV model and the SV model assuming t-distributed errors and compare the results with their corresponding GARCH(1,1) counterpart. The data examined cover daily closing prices of the Swedish stock index OMXS30 for the period 2010-01-05 to 2016- 03-02. The evaluation show that both SV models outperform the two GARCH(1,1) models, where the SV model with assumed t-distributed error distribution give the smallest forecast errors