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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hikari Ltd.
2015
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/8976/ http://umpir.ump.edu.my/id/eprint/8976/ http://umpir.ump.edu.my/id/eprint/8976/ http://umpir.ump.edu.my/id/eprint/8976/1/Forecasting%20Malaysian%20Gold%20Using%20a%20Hybrid%20of%20ARIMA%20and%20GJR-GARCH%20Models.pdf |
| Summary: | 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. |
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