Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Kuala Lumpur composite index / Hasma Basyirah Bakar , Nik Sofiah Nik Rusdi and Nurul Athirah Rushdi

Time series modelling is an effective study that has engaged consideration of researcher society in excess of the past few periods. The purpose of the time series modelling is to wisely compile and precisely study the previous information of a time series to create an applicable model that defines t...

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Main Authors:	Bakar, Hasma Basyirah, Nik Rusdi, Nik Sofiah, Rushdi, Nurul Athirah
Format:	Thesis
Language:	English
Published:	2019
Subjects:	Study and teaching Research
Online Access:	https://ir.uitm.edu.my/id/eprint/32352/1/32352-Comparative%20Perfomance%20of%20arima.pdf
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id	my-uitm-ir.32352
record_format	uketd_dc
spelling	my-uitm-ir.323522020-07-16T07:33:08Z Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Kuala Lumpur composite index / Hasma Basyirah Bakar , Nik Sofiah Nik Rusdi and Nurul Athirah Rushdi 2019-12 Bakar, Hasma Basyirah Nik Rusdi, Nik Sofiah Rushdi, Nurul Athirah Study and teaching. Research Time series modelling is an effective study that has engaged consideration of researcher society in excess of the past few periods. The purpose of the time series modelling is to wisely compile and precisely study the previous information of a time series to create an applicable model that defines the necessary arrangement of the series and to generate forecast values for the series. It is well acknowledged that a time series are regularly affected with outliers. Outliers may impact the forecasting where the tendency in parameter estimates created by extreme observation will reduce its effectiveness because the optimum predictor for an Autoregressive Integrated Moving Average (ARIMA) model is determined by its parameters. This study used ARIMA and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) to compare the best model for forecasting Kuala Lumpur Composite Index (KLCI) when the outlier exists. The best models of ARIMA and GARCH were evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). It can be concluded that GARCH model performed better compared to Box-Jenkins ARIMA in forecasting KLCI 2019-12 Thesis https://ir.uitm.edu.my/id/eprint/32352/ https://ir.uitm.edu.my/id/eprint/32352/1/32352-Comparative%20Perfomance%20of%20arima.pdf text en public degree Universiti Teknologi MARA Cawangan Kelantan Faculty of Computer and Mathematical Sciences Ab. Aziz, Nasuhar
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Ab. Aziz, Nasuhar
topic	Study and teaching Research
spellingShingle	Study and teaching Research Bakar, Hasma Basyirah Nik Rusdi, Nik Sofiah Rushdi, Nurul Athirah Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Kuala Lumpur composite index / Hasma Basyirah Bakar , Nik Sofiah Nik Rusdi and Nurul Athirah Rushdi
description	Time series modelling is an effective study that has engaged consideration of researcher society in excess of the past few periods. The purpose of the time series modelling is to wisely compile and precisely study the previous information of a time series to create an applicable model that defines the necessary arrangement of the series and to generate forecast values for the series. It is well acknowledged that a time series are regularly affected with outliers. Outliers may impact the forecasting where the tendency in parameter estimates created by extreme observation will reduce its effectiveness because the optimum predictor for an Autoregressive Integrated Moving Average (ARIMA) model is determined by its parameters. This study used ARIMA and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) to compare the best model for forecasting Kuala Lumpur Composite Index (KLCI) when the outlier exists. The best models of ARIMA and GARCH were evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). It can be concluded that GARCH model performed better compared to Box-Jenkins ARIMA in forecasting KLCI
format	Thesis
qualification_level	Bachelor degree
author	Bakar, Hasma Basyirah Nik Rusdi, Nik Sofiah Rushdi, Nurul Athirah
author_facet	Bakar, Hasma Basyirah Nik Rusdi, Nik Sofiah Rushdi, Nurul Athirah
author_sort	Bakar, Hasma Basyirah
title	Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Kuala Lumpur composite index / Hasma Basyirah Bakar , Nik Sofiah Nik Rusdi and Nurul Athirah Rushdi
title_short	Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Kuala Lumpur composite index / Hasma Basyirah Bakar , Nik Sofiah Nik Rusdi and Nurul Athirah Rushdi
title_full	Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Kuala Lumpur composite index / Hasma Basyirah Bakar , Nik Sofiah Nik Rusdi and Nurul Athirah Rushdi
title_fullStr	Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Kuala Lumpur composite index / Hasma Basyirah Bakar , Nik Sofiah Nik Rusdi and Nurul Athirah Rushdi
title_full_unstemmed	Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Kuala Lumpur composite index / Hasma Basyirah Bakar , Nik Sofiah Nik Rusdi and Nurul Athirah Rushdi
title_sort	comparative performance of arima and garch models in modelling and forecasting volatility of kuala lumpur composite index / hasma basyirah bakar , nik sofiah nik rusdi and nurul athirah rushdi
granting_institution	Universiti Teknologi MARA Cawangan Kelantan
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2019
url	https://ir.uitm.edu.my/id/eprint/32352/1/32352-Comparative%20Perfomance%20of%20arima.pdf
_version_	1783734158352187392

Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Kuala Lumpur composite index / Hasma Basyirah Bakar , Nik Sofiah Nik Rusdi and Nurul Athirah Rushdi

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