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Forecasting Malaysian gold using a hybrid of ARIMA and GJR-GARCH models

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

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Official URL: http://dx.doi.org/10.12988/ams.2015.5124

Abstract

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

Item Type:Article
Uncontrolled Keywords:hybrid model, TARCH, volatility clustering
Subjects:Q Science > QA Mathematics
Divisions:Science
ID Code:55337
Deposited By: Fazli Masari
Deposited On:24 Aug 2016 03:42
Last Modified:15 Feb 2017 07:07

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