FORECASTING THE FUZZY HYBRID ARIMA-GARCH MODEL OF STOCK PRICES IN THE IRAQI STOCK EXCHANGE.

The research includes the application of a hybrid model by integrating between the Fuzzy Autoregressive Integrated Moving Average (FARIMA) model and the Conditional Autoregressive of Generalized Variance Heterogeneity (GARCH) model, using the residuals of the FARIMA model as inputs to the GARCH model on the weekly time series data of stock prices in the Iraqi Stock Exchange. A number of models were proposed and then a comparison was made between them using evaluation criteria. It was found that the FARIMA (1,1,0) - GARCH (1,1) hybrid model is the most appropriate model for analyzing the data under study and the most efficient in the accuracy of future prediction compared to the FARIMA model because it has less Values for prediction accuracy criteria (MSE, MAE, ME, RMSE, MAPE). [ABSTRACT FROM AUTHOR]