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...
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2018
|
| Subjects: | |
| 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 |
| _version_ | 1848821953665695744 |
|---|---|
| author | Siti Roslindar, Yaziz Roslinazairimah, Zakaria |
| author_facet | Siti Roslindar, Yaziz Roslinazairimah, Zakaria |
| author_sort | Siti Roslindar, Yaziz |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T02:33:32Z |
| format | Conference or Workshop Item |
| id | ump-24110 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:33:32Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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) (Unpublished) |
| 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 |
| title | 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_short | 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 |
| topic | QA Mathematics T Technology (General) |
| 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 |