ROBUST ORDER IDENTIFICATION OF ARIMA AND GARCH MODELS: STATIONARY AND NON-STATIONARY PROCESS

Identification is the most important stage of all stages modeling process. This research identifies a suitable order for two different time series models ARIMA and GARCH. For GARCH distributions that GARCH-STD GARCH-GED with sample sizes in fitting forecasting stationary non-stationary data structures was considered. The study recommends use smallest information criterion like AIC BIC to select model.