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Volatility is one of the measures of risk within the financial markets. GARCH modelling involves important volatility forecasting methodology and is widely used in finance. It is important to be able to forecast volatility since volatility has an impact on financial portfolios and the risk hedging methodology followed by financial companies. This study investigates the behaviour of parameter estimates and volatility forecasts of GARCH models over time, using a rolling window estimation procedure. Three GARCH models, the Symmetric GARCH, GJR-GARCH and E-GARCH models, are compared. The dataset used in the study comprises of the JSE All-Share index. This index is divided into two different periods, namely, a tranquil financial period and a turbulent financial period. Different factors influence the performance of GARCH models and consequently determines which GARCH model is the most suited for certain circumstances. These factors are: the sample window period, forecasting horison, the financial period and the underlying distribution of the log returns.

The influence of different financial market regimes on the dynamic estimation of GARCH volatility model parameters and volatility forecasting