A network autoregressive model with GARCH effects and its applications.

In this study, a network autoregressive model with GARCH effects, denoted by NAR-GARCH, is proposed to depict the return dynamics of stock market indices. A GARCH filter is employed to marginally remove the GARCH effects of each index, and the NAR model with the Granger causality test and Pearson�...

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Autores principales:	Shih-Feng Huang, Hsin-Han Chiang, Yu-Jun Lin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6194f0a76dc647c4bb7773293a3f3553
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spelling	oai:doaj.org-article:6194f0a76dc647c4bb7773293a3f35532021-12-02T20:08:53ZA network autoregressive model with GARCH effects and its applications.1932-620310.1371/journal.pone.0255422https://doaj.org/article/6194f0a76dc647c4bb7773293a3f35532021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255422https://doaj.org/toc/1932-6203In this study, a network autoregressive model with GARCH effects, denoted by NAR-GARCH, is proposed to depict the return dynamics of stock market indices. A GARCH filter is employed to marginally remove the GARCH effects of each index, and the NAR model with the Granger causality test and Pearson's correlation test with sharp price movements is used to capture the joint effects caused by other indices with the most updated market information. The NAR-GARCH model is designed to depict the joint effects of nonsynchronous multiple time series in an easy-to-implement and effective way. The returns of 20 global stock indices from 2006 to 2020 are employed for our empirical investigation. The numerical results reveal that the NAR-GARCH model has satisfactory performance in both fitting and prediction for the 20 stock indices, especially when a market index has strong upward or downward movements.Shih-Feng HuangHsin-Han ChiangYu-Jun LinPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0255422 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shih-Feng Huang Hsin-Han Chiang Yu-Jun Lin A network autoregressive model with GARCH effects and its applications.
description	In this study, a network autoregressive model with GARCH effects, denoted by NAR-GARCH, is proposed to depict the return dynamics of stock market indices. A GARCH filter is employed to marginally remove the GARCH effects of each index, and the NAR model with the Granger causality test and Pearson's correlation test with sharp price movements is used to capture the joint effects caused by other indices with the most updated market information. The NAR-GARCH model is designed to depict the joint effects of nonsynchronous multiple time series in an easy-to-implement and effective way. The returns of 20 global stock indices from 2006 to 2020 are employed for our empirical investigation. The numerical results reveal that the NAR-GARCH model has satisfactory performance in both fitting and prediction for the 20 stock indices, especially when a market index has strong upward or downward movements.
format	article
author	Shih-Feng Huang Hsin-Han Chiang Yu-Jun Lin
author_facet	Shih-Feng Huang Hsin-Han Chiang Yu-Jun Lin
author_sort	Shih-Feng Huang
title	A network autoregressive model with GARCH effects and its applications.
title_short	A network autoregressive model with GARCH effects and its applications.
title_full	A network autoregressive model with GARCH effects and its applications.
title_fullStr	A network autoregressive model with GARCH effects and its applications.
title_full_unstemmed	A network autoregressive model with GARCH effects and its applications.
title_sort	network autoregressive model with garch effects and its applications.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/6194f0a76dc647c4bb7773293a3f3553
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A network autoregressive model with GARCH effects and its applications.

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