Markov-Switching GARCH and Mixture of GARCH-type Models for Accuracy in Forecasting

The stock markets all over the world have been experiencing fluctuations. These fluctuations are due to some political and administrative decisions. For example, in Tanzania, structural transformations in the economic sectors have been happening time after time, which resulted in fluctuations in the stock market. In this paper, the stock market's volatility was modelled using Markov-Switching GARCH (MS GARCH) and the mixture of GARCH type models. The Bayesian Information Criterion (BIC) was employed to get the best GARCH type models with respective conditional distributions. The GARCH (1, 1) with skewed normal distribution, EGARCH (1, 1) with student's t-distribution and Glosten, Jagannathan and Runkle-GARCH (GJR GARCH) (1, 1) with generalized error distribution selected for further analysis. The study found that the three-state heterogeneous regime MS GARCH and Mixture of the selected GARCH type models provide the best fit and the dynamic feedback between components for the DSEI All-share stock data. The Bayesian Markov Chain Monte Carlo (MCMC) method resulted in an acceptance rate of 28.7%, which lies between 20% and 50% as the requirement of the rule of thumb. The different sample sizes employed on the Bayesian MCMC technique have also proven the fitted model's powerfulness since all acceptance sampler rate falls within the range. Furthermore, the forecasting results for the next 30, 60, 90, and 120 days have shown a continuous fluctuation in the DSEI All-share Stock Index.