Multistep forecasting for highly volatile data using a new box-Jenkins and GARCH procedure
The study of the multistep ahead forecast is significant for practical application purposes using the proposed statistical model. This study proposes a new procedure of Box-Jenkins and GARCH (or BJG) in evaluating the multistep forecasting performance for a highly volatile time series data. The prom...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
Akademi Sains Malaysia
2020
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/46122/1/Multistep%20forecasting%20for%20highly%20volatile%20data.pdf https://doi.org/10.32802/asmscj.2020.sm26(1.14) https://umpir.ump.edu.my/id/eprint/46122/ |
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| Summary: | The study of the multistep ahead forecast is significant for practical application purposes using the proposed statistical model. This study proposes a new procedure of Box-Jenkins and GARCH (or BJG) in evaluating the multistep forecasting performance for a 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 procedure of multistep ahead forecast for BJ-G model is proposed using R language. In evaluating the performance of the multistep ahead forecast, the proposed procedure is employed to daily world gold price series of 5-year data. Based on the empirical results, the proposed procedure of multistep ahead forecast enhances the existing procedure of BJ-G which is able to provide a promising procedure to assess the performance of the BJ-G model in forecasting a highly volatile time series data. The procedure adds the value of BJ-G model since it allows the model to describe efficiently the characteristics of the volatile series up to n-step ahead forecast |
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