Volatility forecasting using Double-Markov switching GARCH models under skewed Student-t distribution
Volatility forecasting using Double-Markov switching GARCH models under skewed Student-t distribution
This thesis focuses on forecasting the volatility of daily returns using a double Markov switching GARCH model with a skewed Student-t error distribution. The model was applied to individual shares obtained from the Johannesburg Stock Exchange (JSE). The Bayesian approach which uses Markov Chain Monte Carlo was used to estimate the unknown parameters in the model. The double Markov switching GARCH model was compared to a GARCH(1,1) model. Value at risk thresholds and violations ratios were computed leading to the ranking of the GARCH and double Markov switching GARCH models. The results showed that double Markov switching GARCH model performs similarly to the GARCH model based on the ranking technique employed in this thesis.
Includes bibliographical references.
- University of Cape Town South Africa
Mathematical Finance
Mathematical Finance
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