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GARCH models and distributions comparison for nonlinear time series with volatilities

The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is extensively used for handling volatilities. However, with numerous extensions to the standard GARCH model, selecting the most suitable model for forecasting price volatilities becomes challenging. This study aims to exam...

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Main Authors: Nur Haizum, Abd Rahman, Jia, Goh Hui, Hani Syahida, Zulkafli
Format: Article
Language:English
Published: UTM Press 2023
Subjects:
QA Mathematics
Online Access:http://umpir.ump.edu.my/id/eprint/42168/1/2023%20GARCH%20Modelsand%20DistributionsComparison%20for%20Nonlinear%20Time%20Series%20with%20Volatilities.pdf
http://umpir.ump.edu.my/id/eprint/42168/
https://doi.org/10.11113/mjfas.v19n6.3101
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spelling my.ump.umpir.421682024-09-05T04:22:47Z http://umpir.ump.edu.my/id/eprint/42168/ GARCH models and distributions comparison for nonlinear time series with volatilities Nur Haizum, Abd Rahman Jia, Goh Hui Hani Syahida, Zulkafli QA Mathematics The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is extensively used for handling volatilities. However, with numerous extensions to the standard GARCH model, selecting the most suitable model for forecasting price volatilities becomes challenging. This study aims to examine the performance of different GARCH models in forecasting crude oil price volatilities using West Texas Intermediate (WTI) data. The models considered are the standard GARCH, Integrated GARCH (IGARCH), Exponential GARCH (EGARCH), and Golsten, Jagannathan, and Runkle GARCH (GJR-GARCH), each with normal distribution, Student’s t-distribution, and Generalized Error Distribution (GED). To evaluate the performance of each model, the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) are used as the model selection criteria, along with forecast accuracy measures such as absolute error, root mean squared error (RMSE), and mean absolute error (MAE). Post-estimation tests, including the Autoregressive Conditional Heteroskedasticity Lagrange Multiplier (ARCH-LM) test and the Ljung-Box test, are conducted to ensure the adequacy of all models. The results reveal that all GARCH models are suitable for modeling the data, as indicated by statistically significant estimated parameters and satisfactory post-estimation outcomes. However, the EGARCH (1, 1) model, particularly with Student’s t-distribution, outperforms other models in both data fitting and accurate forecasting of nonlinear time series. UTM Press 2023 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/42168/1/2023%20GARCH%20Modelsand%20DistributionsComparison%20for%20Nonlinear%20Time%20Series%20with%20Volatilities.pdf Nur Haizum, Abd Rahman and Jia, Goh Hui and Hani Syahida, Zulkafli (2023) GARCH models and distributions comparison for nonlinear time series with volatilities. Malaysian Journal of Fundamental and Applied Sciences, 19 (6). pp. 989-1001. ISSN 2289-5981. (Published) https://doi.org/10.11113/mjfas.v19n6.3101 10.11113/mjfas.v19n6.3101
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Nur Haizum, Abd Rahman
Jia, Goh Hui
Hani Syahida, Zulkafli
GARCH models and distributions comparison for nonlinear time series with volatilities
description The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is extensively used for handling volatilities. However, with numerous extensions to the standard GARCH model, selecting the most suitable model for forecasting price volatilities becomes challenging. This study aims to examine the performance of different GARCH models in forecasting crude oil price volatilities using West Texas Intermediate (WTI) data. The models considered are the standard GARCH, Integrated GARCH (IGARCH), Exponential GARCH (EGARCH), and Golsten, Jagannathan, and Runkle GARCH (GJR-GARCH), each with normal distribution, Student’s t-distribution, and Generalized Error Distribution (GED). To evaluate the performance of each model, the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) are used as the model selection criteria, along with forecast accuracy measures such as absolute error, root mean squared error (RMSE), and mean absolute error (MAE). Post-estimation tests, including the Autoregressive Conditional Heteroskedasticity Lagrange Multiplier (ARCH-LM) test and the Ljung-Box test, are conducted to ensure the adequacy of all models. The results reveal that all GARCH models are suitable for modeling the data, as indicated by statistically significant estimated parameters and satisfactory post-estimation outcomes. However, the EGARCH (1, 1) model, particularly with Student’s t-distribution, outperforms other models in both data fitting and accurate forecasting of nonlinear time series.
format Article
author Nur Haizum, Abd Rahman
Jia, Goh Hui
Hani Syahida, Zulkafli
author_facet Nur Haizum, Abd Rahman
Jia, Goh Hui
Hani Syahida, Zulkafli
author_sort Nur Haizum, Abd Rahman
title GARCH models and distributions comparison for nonlinear time series with volatilities
title_short GARCH models and distributions comparison for nonlinear time series with volatilities
title_full GARCH models and distributions comparison for nonlinear time series with volatilities
title_fullStr GARCH models and distributions comparison for nonlinear time series with volatilities
title_full_unstemmed GARCH models and distributions comparison for nonlinear time series with volatilities
title_sort garch models and distributions comparison for nonlinear time series with volatilities
publisher UTM Press
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/42168/1/2023%20GARCH%20Modelsand%20DistributionsComparison%20for%20Nonlinear%20Time%20Series%20with%20Volatilities.pdf
http://umpir.ump.edu.my/id/eprint/42168/
https://doi.org/10.11113/mjfas.v19n6.3101
_version_ 1822924641781415936
score 13.235318

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