Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH

The study of the multistep ahead forecast is significant for practical application purposes using the proposed statistical model. This study is proposing a new algorithm of Box-Jenkins and GARCH (or BJ-G) in evaluating the multistep forecasting performance of the BJ-G model for highly volatile time...

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Main Authors:	Siti Roslindar, Yaziz, Roslinazairimah, Zakaria
Format:	Conference or Workshop Item
Language:	English
Published:	2018
Subjects:	QA Mathematics T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/24110/ http://umpir.ump.edu.my/id/eprint/24110/1/50.1%20Multistep%20forecasting%20for%20highly%20volatile%20data.pdf

id	ump-24110
recordtype	eprints
spelling	ump-241102019-02-13T07:55:55Z http://umpir.ump.edu.my/id/eprint/24110/ Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH Siti Roslindar, Yaziz Roslinazairimah, Zakaria QA Mathematics T Technology (General) The study of the multistep ahead forecast is significant for practical application purposes using the proposed statistical model. This study is proposing a new algorithm of Box-Jenkins and GARCH (or BJ-G) in evaluating the multistep forecasting performance of the BJ-G model for highly volatile time series data. The promising results from one-step ahead out-of-sample forecast series using the BJ-G model has motivated the extension to multiple step ahead forecast. In order to achieve the objective, the algorithm of multistep ahead forecast for BJ-G model is proposed using R language. In evaluating the performance of the multistep ahead forecast, the proposed algorithm is employed to daily world gold price series of 5-year data. Based on the empirical results, the proposed algorithm of multistep ahead forecast to the algorithm of BJ-G provides a promising procedure to assess the performance of the BJ-G model in forecasting a highly volatile time series data. The algorithm adds the value of BJ-G model since it allows the model to explain more about the characteristics of the volatile series up to n-step ahead forecast. 2018 Conference or Workshop Item NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24110/1/50.1%20Multistep%20forecasting%20for%20highly%20volatile%20data.pdf Siti Roslindar, Yaziz and Roslinazairimah, Zakaria (2018) Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH. In: Simposium Kebangsaan Sains Matematik Ke 26 (SKSM26) 2018, 28 - 29 November 2018 , Universiti Malaysia Sabah, Kota Kinabalu Sabah. p. 1.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA Mathematics T Technology (General)
spellingShingle	QA Mathematics T Technology (General) Siti Roslindar, Yaziz Roslinazairimah, Zakaria Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH
description	The study of the multistep ahead forecast is significant for practical application purposes using the proposed statistical model. This study is proposing a new algorithm of Box-Jenkins and GARCH (or BJ-G) in evaluating the multistep forecasting performance of the BJ-G model for highly volatile time series data. The promising results from one-step ahead out-of-sample forecast series using the BJ-G model has motivated the extension to multiple step ahead forecast. In order to achieve the objective, the algorithm of multistep ahead forecast for BJ-G model is proposed using R language. In evaluating the performance of the multistep ahead forecast, the proposed algorithm is employed to daily world gold price series of 5-year data. Based on the empirical results, the proposed algorithm of multistep ahead forecast to the algorithm of BJ-G provides a promising procedure to assess the performance of the BJ-G model in forecasting a highly volatile time series data. The algorithm adds the value of BJ-G model since it allows the model to explain more about the characteristics of the volatile series up to n-step ahead forecast.
format	Conference or Workshop Item
author	Siti Roslindar, Yaziz Roslinazairimah, Zakaria
author_facet	Siti Roslindar, Yaziz Roslinazairimah, Zakaria
author_sort	Siti Roslindar, Yaziz
title	Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH
title_short	Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH
title_full	Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH
title_fullStr	Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH
title_full_unstemmed	Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH
title_sort	multistep forecasting for highly volatile data using new algorithm of box-jenkins and garch
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/24110/ http://umpir.ump.edu.my/id/eprint/24110/1/50.1%20Multistep%20forecasting%20for%20highly%20volatile%20data.pdf
first_indexed	2023-09-18T22:36:19Z
last_indexed	2023-09-18T22:36:19Z
_version_	1777416619428937728

Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH

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