Innovations in the ARIMA - GARCH Modeling in Forecasting Gold Price

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 innov...

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Main Authors:	Siti Roslindar, Yaziz, Roslinazairimah, Zakaria, Noor Azlinna, Azizan, Maizah Hura, Ahmad, Agrawal, Manju, Boland, John
Format:	Conference or Workshop Item
Language:	English
Published:	2014
Subjects:	Q Science (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/8613/ http://umpir.ump.edu.my/id/eprint/8613/ http://umpir.ump.edu.my/id/eprint/8613/1/fist-2014-roslinazairimah-Innovations_in_the_ARIMA.pdf

id	ump-8613
recordtype	eprints
spelling	ump-86132018-05-02T06:23:03Z http://umpir.ump.edu.my/id/eprint/8613/ Innovations in the ARIMA - GARCH Modeling in Forecasting Gold Price Siti Roslindar, Yaziz Roslinazairimah, Zakaria Noor Azlinna, Azizan Maizah Hura, Ahmad Agrawal, Manju Boland, John Q Science (General) 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. 2014 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/8613/1/fist-2014-roslinazairimah-Innovations_in_the_ARIMA.pdf 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.. https://drive.google.com/file/d/0B02jW7Y1R3ICX0s0dG9XN3I1dVU/view?usp=sharing
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Siti Roslindar, Yaziz Roslinazairimah, Zakaria Noor Azlinna, Azizan Maizah Hura, Ahmad Agrawal, Manju Boland, John Innovations in the ARIMA - GARCH Modeling in Forecasting Gold Price
description	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.
format	Conference or Workshop Item
author	Siti Roslindar, Yaziz Roslinazairimah, Zakaria Noor Azlinna, Azizan Maizah Hura, Ahmad Agrawal, Manju Boland, John
author_facet	Siti Roslindar, Yaziz Roslinazairimah, Zakaria Noor Azlinna, Azizan Maizah Hura, Ahmad Agrawal, Manju Boland, John
author_sort	Siti Roslindar, Yaziz
title	Innovations in the ARIMA - GARCH Modeling in Forecasting Gold Price
title_short	Innovations in the ARIMA - GARCH Modeling in Forecasting Gold Price
title_full	Innovations in the ARIMA - GARCH Modeling in Forecasting Gold Price
title_fullStr	Innovations in the ARIMA - GARCH Modeling in Forecasting Gold Price
title_full_unstemmed	Innovations in the ARIMA - GARCH Modeling in Forecasting Gold Price
title_sort	innovations in the arima - garch modeling in forecasting gold price
publishDate	2014
url	http://umpir.ump.edu.my/id/eprint/8613/ http://umpir.ump.edu.my/id/eprint/8613/ http://umpir.ump.edu.my/id/eprint/8613/1/fist-2014-roslinazairimah-Innovations_in_the_ARIMA.pdf
first_indexed	2023-09-18T22:06:22Z
last_indexed	2023-09-18T22:06:22Z
_version_	1777414735660056576

Innovations in the ARIMA - GARCH Modeling in Forecasting Gold Price

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