Does the optimal model always perform the best? a combined approach for interval forecasting
Interval forecasting is widely applied by decision makers for it can provide more comprehensive information. In the literature, GARCH models under different distributional assumptions are applied and evaluated to find the optimal interval forecasting model for the experimental data. However, the opt...
| Main Authors: | , , |
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| Format: | Article |
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Interscience Publishers
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/107397/ |
| _version_ | 1848864884794589184 |
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| author | Arasan, Jayanthi Zhang, Zhe Chong, Choo W.E.I. |
| author_facet | Arasan, Jayanthi Zhang, Zhe Chong, Choo W.E.I. |
| author_sort | Arasan, Jayanthi |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Interval forecasting is widely applied by decision makers for it can provide more comprehensive information. In the literature, GARCH models under different distributional assumptions are applied and evaluated to find the optimal interval forecasting model for the experimental data. However, the optimal model selected based on sample data from a specific period may not always perform the best in future periods. Therefore, this study employs GARCH models based on different distributional assumptions for interval forecasting of the daily return data of the Nasdaq Composite Index. The results show that the forecasting performance of some models exhibits significant differences across different periods. To address this issue, this study proposes a Monte Carlo-based non-parametric interval forecasting combination method. The results demonstrate that this method can effectively avoid the risk of forecasting inaccuracies caused by relying on a single model. |
| first_indexed | 2025-11-15T13:55:55Z |
| format | Article |
| id | upm-107397 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:55:55Z |
| publishDate | 2023 |
| publisher | Interscience Publishers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1073972024-11-04T02:54:46Z http://psasir.upm.edu.my/id/eprint/107397/ Does the optimal model always perform the best? a combined approach for interval forecasting Arasan, Jayanthi Zhang, Zhe Chong, Choo W.E.I. Interval forecasting is widely applied by decision makers for it can provide more comprehensive information. In the literature, GARCH models under different distributional assumptions are applied and evaluated to find the optimal interval forecasting model for the experimental data. However, the optimal model selected based on sample data from a specific period may not always perform the best in future periods. Therefore, this study employs GARCH models based on different distributional assumptions for interval forecasting of the daily return data of the Nasdaq Composite Index. The results show that the forecasting performance of some models exhibits significant differences across different periods. To address this issue, this study proposes a Monte Carlo-based non-parametric interval forecasting combination method. The results demonstrate that this method can effectively avoid the risk of forecasting inaccuracies caused by relying on a single model. Interscience Publishers 2023 Article PeerReviewed Arasan, Jayanthi and Zhang, Zhe and Chong, Choo W.E.I. (2023) Does the optimal model always perform the best? a combined approach for interval forecasting. International Journal of Applied Decision Sciences. pp. 1-20. ISSN 1755-8077; eISSN: 1755-8085 (In Press) https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijads 10.1504/ijads.2025.10058959 |
| spellingShingle | Arasan, Jayanthi Zhang, Zhe Chong, Choo W.E.I. Does the optimal model always perform the best? a combined approach for interval forecasting |
| title | Does the optimal model always perform the best? a combined approach for interval forecasting |
| title_full | Does the optimal model always perform the best? a combined approach for interval forecasting |
| title_fullStr | Does the optimal model always perform the best? a combined approach for interval forecasting |
| title_full_unstemmed | Does the optimal model always perform the best? a combined approach for interval forecasting |
| title_short | Does the optimal model always perform the best? a combined approach for interval forecasting |
| title_sort | does the optimal model always perform the best? a combined approach for interval forecasting |
| url | http://psasir.upm.edu.my/id/eprint/107397/ http://psasir.upm.edu.my/id/eprint/107397/ http://psasir.upm.edu.my/id/eprint/107397/ |