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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9407553 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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