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Forecasting Malaysian Gold Using a Hybrid of ARIMA and GJR-GARCH Models

An effective way to improve forecast accuracy is to use a hybrid model. This paper proposes a hybrid model of linear autoregressive moving average (ARIMA) and non-linear GJR-GARCH model also known as TARCH in modeling and forecasting Malaysian gold. The goodness of fit of the model is measured usin...

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Main Authors: Siti Roslindar, Yaziz, Maizah Hura, Ahmad, Pung, Yean Ping, Nor Hamizah, Miswan
Format: Article
Language:English
Published: Hikari Ltd. 2015
Subjects:
Q Science (General)
Online Access:http://umpir.ump.edu.my/id/eprint/8976/1/Forecasting%20Malaysian%20Gold%20Using%20a%20Hybrid%20of%20ARIMA%20and%20GJR-GARCH%20Models.pdf
http://umpir.ump.edu.my/id/eprint/8976/
http://dx.doi.org/10.12988/ams.2015.5124
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id my.ump.umpir.8976
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spelling my.ump.umpir.89762018-06-28T04:01:41Z http://umpir.ump.edu.my/id/eprint/8976/ Forecasting Malaysian Gold Using a Hybrid of ARIMA and GJR-GARCH Models Siti Roslindar, Yaziz Maizah Hura, Ahmad Pung, Yean Ping Nor Hamizah, Miswan Q Science (General) An effective way to improve forecast accuracy is to use a hybrid model. This paper proposes a hybrid model of linear autoregressive moving average (ARIMA) and non-linear GJR-GARCH model also known as TARCH in modeling and forecasting Malaysian gold. The goodness of fit of the model is measured using Akaike information criteria (AIC) while the forecasting performance is assessed using mean absolute percentage error (MAPE), bias proportion, variance proportion and covariance proportion. Hikari Ltd. 2015 Article PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/8976/1/Forecasting%20Malaysian%20Gold%20Using%20a%20Hybrid%20of%20ARIMA%20and%20GJR-GARCH%20Models.pdf Siti Roslindar, Yaziz and Maizah Hura, Ahmad and Pung, Yean Ping and Nor Hamizah, Miswan (2015) Forecasting Malaysian Gold Using a Hybrid of ARIMA and GJR-GARCH Models. Applied Mathematical Sciences, 9 (30). pp. 1491-1501. ISSN 1314-7552 (print); 1312-885X (online) http://dx.doi.org/10.12988/ams.2015.5124 DOI: 10.12988/ams.2015.5124
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Siti Roslindar, Yaziz
Maizah Hura, Ahmad
Pung, Yean Ping
Nor Hamizah, Miswan
Forecasting Malaysian Gold Using a Hybrid of ARIMA and GJR-GARCH Models
description An effective way to improve forecast accuracy is to use a hybrid model. This paper proposes a hybrid model of linear autoregressive moving average (ARIMA) and non-linear GJR-GARCH model also known as TARCH in modeling and forecasting Malaysian gold. The goodness of fit of the model is measured using Akaike information criteria (AIC) while the forecasting performance is assessed using mean absolute percentage error (MAPE), bias proportion, variance proportion and covariance proportion.
format Article
author Siti Roslindar, Yaziz
Maizah Hura, Ahmad
Pung, Yean Ping
Nor Hamizah, Miswan
author_facet Siti Roslindar, Yaziz
Maizah Hura, Ahmad
Pung, Yean Ping
Nor Hamizah, Miswan
author_sort Siti Roslindar, Yaziz
title Forecasting Malaysian Gold Using a Hybrid of ARIMA and GJR-GARCH Models
title_short Forecasting Malaysian Gold Using a Hybrid of ARIMA and GJR-GARCH Models
title_full Forecasting Malaysian Gold Using a Hybrid of ARIMA and GJR-GARCH Models
title_fullStr Forecasting Malaysian Gold Using a Hybrid of ARIMA and GJR-GARCH Models
title_full_unstemmed Forecasting Malaysian Gold Using a Hybrid of ARIMA and GJR-GARCH Models
title_sort forecasting malaysian gold using a hybrid of arima and gjr-garch models
publisher Hikari Ltd.
publishDate 2015
url http://umpir.ump.edu.my/id/eprint/8976/1/Forecasting%20Malaysian%20Gold%20Using%20a%20Hybrid%20of%20ARIMA%20and%20GJR-GARCH%20Models.pdf
http://umpir.ump.edu.my/id/eprint/8976/
http://dx.doi.org/10.12988/ams.2015.5124
_version_ 1643666003205292032
score 13.209306

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