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

Authors

Keywords:

Stationary, Non-stationary, Time series, Unit root test, ARIMA model, GARCH model

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.

Dimensions

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Published

07-07-2023

How to Cite

ROBUST ORDER IDENTIFICATION OF ARIMA AND GARCH MODELS: STATIONARY AND NON-STATIONARY PROCESS. (2023). FUDMA JOURNAL OF SCIENCES, 7(3), 10-15. https://doi.org/10.33003/fjs-2023-0703-1847

Issue

Vol. 7 No. 3 (2023): FUDMA Journal of Sciences - Vol. 7 No. 3 (Special Issue)

Section

Research Articles
Copyright & Licensing

FUDMA Journal of Sciences

How to Cite

ROBUST ORDER IDENTIFICATION OF ARIMA AND GARCH MODELS: STATIONARY AND NON-STATIONARY PROCESS. (2023). FUDMA JOURNAL OF SCIENCES, 7(3), 10-15. https://doi.org/10.33003/fjs-2023-0703-1847

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