Short-term forecast of gold price using generalized autoregressive conditional heteroscedastic models

Gold is used in many industries and it is popular as a good investment. However, its price can fluctuate widely. There are many mathematical models that can be used to forecast gold prices. In this study, the Generalised Autoregressive Conditional Heteroscedastic (GARCH) and Autoregressive Integrate...

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Main Author:	Mohamed, Siti Nor Hazanah
Format:	Thesis
Language:	English
Published:	2012
Subjects:	Q Science (General)
Online Access:	http://eprints.utm.my/id/eprint/31515/1/SitiNorHazanahMohamedMFS2011.pdf
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id	my-utm-ep.31515
record_format	uketd_dc
spelling	my-utm-ep.315152020-09-30T06:36:18Z Short-term forecast of gold price using generalized autoregressive conditional heteroscedastic models 2012-12 Mohamed, Siti Nor Hazanah Q Science (General) Gold is used in many industries and it is popular as a good investment. However, its price can fluctuate widely. There are many mathematical models that can be used to forecast gold prices. In this study, the Generalised Autoregressive Conditional Heteroscedastic (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models are developed to produce short term forecasts of gold prices. GARCH model is developed due to it is ability to capture the volatility by the nonconstant of conditional variance while forecasts produced by the ARIMA model are used as a benchmark. Comparison of forecasts produced by GARCH and ARIMA models are based on two performance measures: mean absolute percentage error (MAPE) and root mean square error (RMSE). In this study, analyses are done by using Minitab and E-Views software. In general, it can be concluded that the GARCH model is a potential method for forecasting trading day data of gold prices. 2012-12 Thesis http://eprints.utm.my/id/eprint/31515/ http://eprints.utm.my/id/eprint/31515/1/SitiNorHazanahMohamedMFS2011.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Mohamed, Siti Nor Hazanah Short-term forecast of gold price using generalized autoregressive conditional heteroscedastic models
description	Gold is used in many industries and it is popular as a good investment. However, its price can fluctuate widely. There are many mathematical models that can be used to forecast gold prices. In this study, the Generalised Autoregressive Conditional Heteroscedastic (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models are developed to produce short term forecasts of gold prices. GARCH model is developed due to it is ability to capture the volatility by the nonconstant of conditional variance while forecasts produced by the ARIMA model are used as a benchmark. Comparison of forecasts produced by GARCH and ARIMA models are based on two performance measures: mean absolute percentage error (MAPE) and root mean square error (RMSE). In this study, analyses are done by using Minitab and E-Views software. In general, it can be concluded that the GARCH model is a potential method for forecasting trading day data of gold prices.
format	Thesis
qualification_level	Master's degree
author	Mohamed, Siti Nor Hazanah
author_facet	Mohamed, Siti Nor Hazanah
author_sort	Mohamed, Siti Nor Hazanah
title	Short-term forecast of gold price using generalized autoregressive conditional heteroscedastic models
title_short	Short-term forecast of gold price using generalized autoregressive conditional heteroscedastic models
title_full	Short-term forecast of gold price using generalized autoregressive conditional heteroscedastic models
title_fullStr	Short-term forecast of gold price using generalized autoregressive conditional heteroscedastic models
title_full_unstemmed	Short-term forecast of gold price using generalized autoregressive conditional heteroscedastic models
title_sort	short-term forecast of gold price using generalized autoregressive conditional heteroscedastic models
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2012
url	http://eprints.utm.my/id/eprint/31515/1/SitiNorHazanahMohamedMFS2011.pdf
_version_	1747815822560067584

Short-term forecast of gold price using generalized autoregressive conditional heteroscedastic models

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