A study of conditional volatility of hybrid Arima, and Figarch model

Abstract

This study is to discuss the techniques that will be employed by the researcher’s when conducting the study on modelling and predicting financial Time Series data. The hybridization between ARIMA Model and Fractionally Integrated Generalized Autoregressive Conditional Heteroscedastic (FIGARCH) processes. With will be used to develop the most appropriate model for forecasting financial Time Series data. However, same as the main weakness of the ARIMA cannot handle volatility clustering with the persister of long -memory. Unfortunately, FIGARCH can handle. Many studies have suggested that structural breaks should be combined into the long memory models to properly fit financial return volatility (Baillie and Morana, 2009; Belkhouja and Boutahary, 2011).