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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Hikari Ltd.
2015
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| 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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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 |
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Q Science (General) |
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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 |
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