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Geopolitical Risk and the Risk Spillover on the US Technology Firms: A Quantile Perspective
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Wang, Deyong | - |
| dc.contributor.author | Sun, Yizhong | - |
| dc.contributor.author | Wang, Yishuo | - |
| dc.contributor.author | Gao, Nan | - |
| dc.date.accessioned | 2025-07-11T06:30:09Z | - |
| dc.date.available | 2025-07-11T06:30:09Z | - |
| dc.date.issued | 2025-00 | - |
| dc.identifier.issn | 0424-267X | - |
| dc.identifier.issn | 1842-3264 | - |
| dc.identifier.uri | https://scholarworks.gnu.ac.kr/handle/sw.gnu/79368 | - |
| dc.description.abstract | This article employs the quantile vector autoregression (QVAR) Connectedness method to investigate the impact of geopolitical risk on major US technology firms under various market conditions. The findings reveal that: 1) the overall Connectedness index peaks at 63.76% during market uptrends and reaches a low of 38.61% at the median state; 2) Microsoft and Nvidia act as net risk transmitters, whereas Apple and the geopolitical risk index serve as net risk receivers; and 3) during the COVID-19 period in 2020, Connectedness significantly increased across all quantile levels, while a differentiated pattern emerged during the Russia-Ukraine war in 2022. The main contributions of this study include: firstly, it is the first to examine the asymmetric risk linkages between geopolitical risk and US technology firms; secondly, it enriches existing theories through both static and dynamic association analyses; and thirdly, it offers valuable risk management insights for investors. These results have important implications for portfolio management and policy formulation. © 2024 The Authors. Published by Editura ASE. | - |
| dc.format.extent | 17 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Editura Academia de studii economice | - |
| dc.title | Geopolitical Risk and the Risk Spillover on the US Technology Firms: A Quantile Perspective | - |
| dc.type | Article | - |
| dc.publisher.location | 루마니아 | - |
| dc.identifier.doi | 10.24818/18423264/59.2.25.09 | - |
| dc.identifier.scopusid | 2-s2.0-105009004018 | - |
| dc.identifier.wosid | 001530455900009 | - |
| dc.identifier.bibliographicCitation | Economic Computation and Economic Cybernetics Studies and Research, v.59, no.2, pp 142 - 158 | - |
| dc.citation.title | Economic Computation and Economic Cybernetics Studies and Research | - |
| dc.citation.volume | 59 | - |
| dc.citation.number | 2 | - |
| dc.citation.startPage | 142 | - |
| dc.citation.endPage | 158 | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | ssci | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Business & Economics | - |
| dc.relation.journalResearchArea | Mathematics | - |
| dc.relation.journalWebOfScienceCategory | Economics | - |
| dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
| dc.subject.keywordAuthor | Connectedness Analysis | - |
| dc.subject.keywordAuthor | geopolitical risk | - |
| dc.subject.keywordAuthor | Quantile Vector Autoregression | - |
| dc.subject.keywordAuthor | technology firms | - |
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