Modelling gold price using ARIMA-TGARCH

Statistical models can be used to characterize numerical data so as to understand its behavior and pattern. Gold price model, for example, can give signals to investors as to when they should enter and/or exit the market. To find an appropriate gold price model, it is crucial to choose a model that...

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Main Authors: Siti Roslindar, Yaziz, Noor Azlinna, Azizan, Maizah Hura, Ahmad, Roslinazairimah, Zakaria
Format: Article
Language:English
Published: Hikari 2016
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/14660/1/Modelling%20Gold%20Price%20using%20ARIMA%20%E2%80%93%20TGARCH.pdf
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author Siti Roslindar, Yaziz
Noor Azlinna, Azizan
Maizah Hura, Ahmad
Roslinazairimah, Zakaria
author_facet Siti Roslindar, Yaziz
Noor Azlinna, Azizan
Maizah Hura, Ahmad
Roslinazairimah, Zakaria
author_sort Siti Roslindar, Yaziz
collection UMP
description Statistical models can be used to characterize numerical data so as to understand its behavior and pattern. Gold price model, for example, can give signals to investors as to when they should enter and/or exit the market. To find an appropriate gold price model, it is crucial to choose a model that reflects the pattern of the price movement so as to make the model fit and adequate. This study examines the performances of ARIMA-TGARCH with five innovations in modeling and forecasting gold prices. The innovations considered include Gaussian, Student’s-t, skewed Student’s-t, generalized error distribution and skewed generalized error distribution. Using daily gold price data from the years 2003 to 2014, this study concluded that a hybrid ARIMA(0,1,0)-TGARCH(1,1) with t-innovation was the best model due to the existence of leverage effect and heavier tail characteristics in the data.
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spelling UMPir146602018-10-03T06:24:28Z http://umpir.ump.edu.my/id/eprint/14660/ Modelling gold price using ARIMA-TGARCH Siti Roslindar, Yaziz Noor Azlinna, Azizan Maizah Hura, Ahmad Roslinazairimah, Zakaria Q Science (General) Statistical models can be used to characterize numerical data so as to understand its behavior and pattern. Gold price model, for example, can give signals to investors as to when they should enter and/or exit the market. To find an appropriate gold price model, it is crucial to choose a model that reflects the pattern of the price movement so as to make the model fit and adequate. This study examines the performances of ARIMA-TGARCH with five innovations in modeling and forecasting gold prices. The innovations considered include Gaussian, Student’s-t, skewed Student’s-t, generalized error distribution and skewed generalized error distribution. Using daily gold price data from the years 2003 to 2014, this study concluded that a hybrid ARIMA(0,1,0)-TGARCH(1,1) with t-innovation was the best model due to the existence of leverage effect and heavier tail characteristics in the data. Hikari 2016 Article PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/14660/1/Modelling%20Gold%20Price%20using%20ARIMA%20%E2%80%93%20TGARCH.pdf Siti Roslindar, Yaziz and Noor Azlinna, Azizan and Maizah Hura, Ahmad and Roslinazairimah, Zakaria (2016) Modelling gold price using ARIMA-TGARCH. Applied Mathematical Sciences, 10 (28). pp. 1391-1402. ISSN 1314-7552 (print); 1312-885X (online). (Published) http://dx.doi.org/10.12988/ams.2016.511716 doi: 10.12988/ams.2016.511716
spellingShingle Q Science (General)
Siti Roslindar, Yaziz
Noor Azlinna, Azizan
Maizah Hura, Ahmad
Roslinazairimah, Zakaria
Modelling gold price using ARIMA-TGARCH
title Modelling gold price using ARIMA-TGARCH
title_full Modelling gold price using ARIMA-TGARCH
title_fullStr Modelling gold price using ARIMA-TGARCH
title_full_unstemmed Modelling gold price using ARIMA-TGARCH
title_short Modelling gold price using ARIMA-TGARCH
title_sort modelling gold price using arima tgarch
topic Q Science (General)
url http://umpir.ump.edu.my/id/eprint/14660/1/Modelling%20Gold%20Price%20using%20ARIMA%20%E2%80%93%20TGARCH.pdf
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