New model outperforms traditional method in forecasting volatile time series data.
The Autoregressive Integrated Moving Average model is not always the best for forecasting time series data with clustering volatility. The Generalized Autoregressive Conditional Heteroscedasticity model is more efficient and accurate for this type of data.