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

Abstract

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