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 using...
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my.utm.553372017-02-15T07:07:27Z http://eprints.utm.my/id/eprint/55337/ Forecasting Malaysian gold using a hybrid of ARIMA and GJR-GARCH models Ahmad, Maizah Hura Pung, Yean Ping Yaziz, Siti Roslindar Miswan, Nor Hamizah QA Mathematics 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 2015 Article PeerReviewed Ahmad, Maizah Hura and Pung, Yean Ping and Yaziz, Siti Roslindar and Miswan, Nor Hamizah (2015) Forecasting Malaysian gold using a hybrid of ARIMA and GJR-GARCH models. Applied Mathematical Sciences, 9 (29-32). pp. 1491-1501. ISSN 1312-885X http://dx.doi.org/10.12988/ams.2015.5124 DOI:10.12988/ams.2015.5124 |
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QA Mathematics Ahmad, Maizah Hura Pung, Yean Ping Yaziz, Siti Roslindar Miswan, Nor Hamizah 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 |
Ahmad, Maizah Hura Pung, Yean Ping Yaziz, Siti Roslindar Miswan, Nor Hamizah |
| author_facet |
Ahmad, Maizah Hura Pung, Yean Ping Yaziz, Siti Roslindar Miswan, Nor Hamizah |
| author_sort |
Ahmad, Maizah Hura |
| 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 |
| publishDate |
2015 |
| url |
http://eprints.utm.my/id/eprint/55337/ http://dx.doi.org/10.12988/ams.2015.5124 |
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