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Innovations in the ARIMA - GARCH Modeling in Forecasting Gold Price

Siti Roslindar, Yaziz and Roslinazairimah, Zakaria and Noor Azlinna, Azizan and Maizah Hura, Ahmad and Agrawal, Manju and Boland, John (2014) Innovations in the ARIMA - GARCH Modeling in Forecasting Gold Price. In: Proceedings of the 10th IMT‐GT International Conference On Mathematics, Statistics And Its Applications (ICMSA 2014), 14-16 October 2014 , Kuala Terengganu. pp. 650-658..

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Abstract

Gold has been the most popular commodity as a healthy return investment due to its unique properties as a safe haven asset. Therefore, it is crucial to develop a model that reflects the pattern of the gold price movement since it become very significant to investors. In developing a model, the innovations for the standardized error in diagnostic checking should be chosen appropriately to make the model fit and adequate to the data. Previous study showed that hybrid of ARIMA-GARCH is a promising approach in modeling and forecasting gold price. In this study, we employ different innovations to the ARIMAGARCH model to provide a better understanding in the modeling of gold price series. The innovations in this study are Gaussian, t, skewed t, generalized error distribution and skewed generalized error distribution. By applying the hybrid model to daily gold price data from year 2003 to 2014, empirical results indicate that the ARIMA-GARCH with t innovations was found to perform better and fits the data reasonably well due to the heavier tails characteristics in the data series.

Item Type: Conference or Workshop Item (Speech)
Subjects: Q Science > Q Science (General)
Faculty/Division: Faculty of Engineering Technology
Faculty of Industrial Sciences And Technology
Depositing User: Noorul Farina Arifin
Date Deposited: 18 Feb 2015 06:41
Last Modified: 02 May 2018 06:23
URI: http://umpir.ump.edu.my/id/eprint/8613
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