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A comparative study of interval forecasting using GARCH models under symmetric and asymmetric distributional assumptions

Abstract: Interval forecasting provides decision-makers with a range of possible future values, along with associated probabilities, which allows for a more informed decision-making process. Although GARCH models under different distributional assumptions are commonly compared for their volatility f...

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Main Authors: Arasan, Jayanthi, Chong, Choo W. E. I., Zhang, Zhe
Format: Article
Published: Interscience Publishers 2023
Online Access:http://psasir.upm.edu.my/id/eprint/106393/
https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijads
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spelling my.upm.eprints.1063932024-08-07T02:25:56Z http://psasir.upm.edu.my/id/eprint/106393/ A comparative study of interval forecasting using GARCH models under symmetric and asymmetric distributional assumptions Arasan, Jayanthi Chong, Choo W. E. I. Zhang, Zhe Abstract: Interval forecasting provides decision-makers with a range of possible future values, along with associated probabilities, which allows for a more informed decision-making process. Although GARCH models under different distributional assumptions are commonly compared for their volatility forecasting performance, their performance in interval forecasting is rarely discussed. This study aims to fill this gap by comparing the interval forecasting accuracy of GARCH models under symmetric and asymmetric distributions. SGARCH, EGARCH, and GJR-GARCH models under normal, student-t, GED distributions, and their skewed extensions are applied for one-day-ahead rolling interval forecasting on five major European and American stock indices: S&P 500, FTSE 100, CAC 40, DAX 30 and AEX. The average Winkler score (AWS) is used to measure the accuracy of interval forecasting. The conclusions of this study can be summarised as follows: In pairwise comparisons, the GARCH models under asymmetric distributional assumptions have better interval forecasting accuracy than the GARCH models under symmetric distributional assumptions. In comparisons among GARCH-type models, GJR-GARCH has better interval forecasting accuracy than SGARCH and EGARCH, while SGARCH and EGARCH exhibit similar interval forecasting performance. Interscience Publishers 2023 Article PeerReviewed Arasan, Jayanthi and Chong, Choo W. E. I. and Zhang, Zhe (2023) A comparative study of interval forecasting using GARCH models under symmetric and asymmetric distributional assumptions. International Journal of Applied Decision Sciences, 1 (1). pp. 1-20. ISSN 1755-8077; ESSN: 1755-8085 https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijads 10.1504/ijads.2024.10056334
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Abstract: Interval forecasting provides decision-makers with a range of possible future values, along with associated probabilities, which allows for a more informed decision-making process. Although GARCH models under different distributional assumptions are commonly compared for their volatility forecasting performance, their performance in interval forecasting is rarely discussed. This study aims to fill this gap by comparing the interval forecasting accuracy of GARCH models under symmetric and asymmetric distributions. SGARCH, EGARCH, and GJR-GARCH models under normal, student-t, GED distributions, and their skewed extensions are applied for one-day-ahead rolling interval forecasting on five major European and American stock indices: S&P 500, FTSE 100, CAC 40, DAX 30 and AEX. The average Winkler score (AWS) is used to measure the accuracy of interval forecasting. The conclusions of this study can be summarised as follows: In pairwise comparisons, the GARCH models under asymmetric distributional assumptions have better interval forecasting accuracy than the GARCH models under symmetric distributional assumptions. In comparisons among GARCH-type models, GJR-GARCH has better interval forecasting accuracy than SGARCH and EGARCH, while SGARCH and EGARCH exhibit similar interval forecasting performance.
format Article
author Arasan, Jayanthi
Chong, Choo W. E. I.
Zhang, Zhe
spellingShingle Arasan, Jayanthi
Chong, Choo W. E. I.
Zhang, Zhe
A comparative study of interval forecasting using GARCH models under symmetric and asymmetric distributional assumptions
author_facet Arasan, Jayanthi
Chong, Choo W. E. I.
Zhang, Zhe
author_sort Arasan, Jayanthi
title A comparative study of interval forecasting using GARCH models under symmetric and asymmetric distributional assumptions
title_short A comparative study of interval forecasting using GARCH models under symmetric and asymmetric distributional assumptions
title_full A comparative study of interval forecasting using GARCH models under symmetric and asymmetric distributional assumptions
title_fullStr A comparative study of interval forecasting using GARCH models under symmetric and asymmetric distributional assumptions
title_full_unstemmed A comparative study of interval forecasting using GARCH models under symmetric and asymmetric distributional assumptions
title_sort comparative study of interval forecasting using garch models under symmetric and asymmetric distributional assumptions
publisher Interscience Publishers
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/106393/
https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijads
_version_ 1806701225468493824
score 13.211869

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