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Spillover Network Features from the Industry Chain View in Multi-Time Scales

Financial stocks in the industry chain interact notably because of close economic and technical relationships. Some participants pay particular attention to one industry chain and are concerned with different investment horizons. The motivation for this study is to offer more targeted information to...

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Detalles Bibliográficos
Autores principales: Feng, Sida, Sun, Qingru, Liu, Xueyong, Xu, Tianran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407553/
https://www.ncbi.nlm.nih.gov/pubmed/36010772
http://dx.doi.org/10.3390/e24081108
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author Feng, Sida
Sun, Qingru
Liu, Xueyong
Xu, Tianran
author_facet Feng, Sida
Sun, Qingru
Liu, Xueyong
Xu, Tianran
author_sort Feng, Sida
collection PubMed
description Financial stocks in the industry chain interact notably because of close economic and technical relationships. Some participants pay particular attention to one industry chain and are concerned with different investment horizons. The motivation for this study is to offer more targeted information to various market participants who focus on different time scales in one industry chain from a systematic perspective by combining the GARCH-BEKK, heterogeneous network, and wavelet analysis methods. The findings are as follows: (1) For parties who prefer to take more risks to gain higher returns, scale 2 (4–8 days) is a good option, while long-term investment (32–128 days) is suitable for conservative investors. (2) In most cases, some links in the industry chain are particularly sensitive to changes in stocks in other links. (3) The influence, sensitivity, and intermediary of stocks in the industry chain on different time scales were explored, and participants could use the resulting information to monitor the market or select stocks. (4) The structures, key players, and industry chain attributes of the main transmission paths differ on multi-time scales. Risk transmission can be controlled by intercepting important spillover relations within the paths.
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spelling pubmed-94075532022-08-26 Spillover Network Features from the Industry Chain View in Multi-Time Scales Feng, Sida Sun, Qingru Liu, Xueyong Xu, Tianran Entropy (Basel) Article Financial stocks in the industry chain interact notably because of close economic and technical relationships. Some participants pay particular attention to one industry chain and are concerned with different investment horizons. The motivation for this study is to offer more targeted information to various market participants who focus on different time scales in one industry chain from a systematic perspective by combining the GARCH-BEKK, heterogeneous network, and wavelet analysis methods. The findings are as follows: (1) For parties who prefer to take more risks to gain higher returns, scale 2 (4–8 days) is a good option, while long-term investment (32–128 days) is suitable for conservative investors. (2) In most cases, some links in the industry chain are particularly sensitive to changes in stocks in other links. (3) The influence, sensitivity, and intermediary of stocks in the industry chain on different time scales were explored, and participants could use the resulting information to monitor the market or select stocks. (4) The structures, key players, and industry chain attributes of the main transmission paths differ on multi-time scales. Risk transmission can be controlled by intercepting important spillover relations within the paths. MDPI 2022-08-12 /pmc/articles/PMC9407553/ /pubmed/36010772 http://dx.doi.org/10.3390/e24081108 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Feng, Sida
Sun, Qingru
Liu, Xueyong
Xu, Tianran
Spillover Network Features from the Industry Chain View in Multi-Time Scales
title Spillover Network Features from the Industry Chain View in Multi-Time Scales
title_full Spillover Network Features from the Industry Chain View in Multi-Time Scales
title_fullStr Spillover Network Features from the Industry Chain View in Multi-Time Scales
title_full_unstemmed Spillover Network Features from the Industry Chain View in Multi-Time Scales
title_short Spillover Network Features from the Industry Chain View in Multi-Time Scales
title_sort spillover network features from the industry chain view in multi-time scales
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407553/
https://www.ncbi.nlm.nih.gov/pubmed/36010772
http://dx.doi.org/10.3390/e24081108
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