Time-varing effect of policy uncertainty on A-share industry returns- A novel Bayesian approach.
The impact of policy uncertainty on A-share industry returns shows significant time-varying characteristics, amplified by industry input-output relationships. Traditional TVP-VAR models overlook network structures, leading to unquantified spillover effects and imprecise systemic risk identification....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0326605 |
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| _version_ | 1849424704341803008 |
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| author | Tianxing Zhu Jinyang Liu Daixing Zeng Xuan Miao |
| author_facet | Tianxing Zhu Jinyang Liu Daixing Zeng Xuan Miao |
| author_sort | Tianxing Zhu |
| collection | DOAJ |
| description | The impact of policy uncertainty on A-share industry returns shows significant time-varying characteristics, amplified by industry input-output relationships. Traditional TVP-VAR models overlook network structures, leading to unquantified spillover effects and imprecise systemic risk identification. To address this problem, this study embeds industry input-output tables as matrices into Time-Varying Parameter Spatial Autoregressive Model, and Bayesian methods are innovatively introduced into this model to capture the parameters. Policy uncertainty is categorized into five dimensions-economic, fiscal, monetary, exchange rate, and trade. Empirical results reveal following key findings: On average, network spillover effects explain approximately 39% of the response of A-share industry returns to policy uncertainty. Group analysis reveal that economic and fiscal policy uncertainties exhibit positive network effects, indicating synergistic effect that amplify their impact across industries. In contrast, exchange rate and trade policy uncertainties generate negative network effects, reflecting competitive or substitution effects. Systemic risk is most pronounced in fiscal and trade policy uncertainty groups. Systemic risk increases across all policy uncertainty groups except trade, which shows a declining trend. This study provides a novel framework for understanding the dual nature of spillover effect in production networks, offering valuable insights for policymakers and investors to manage systemic risks and indentify synergistic and competitive effects in interconnected industries. |
| format | Article |
| id | doaj-art-a212043cf7d34eb2b64e05e61fdc60b2 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-a212043cf7d34eb2b64e05e61fdc60b22025-08-20T03:30:03ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01206e032660510.1371/journal.pone.0326605Time-varing effect of policy uncertainty on A-share industry returns- A novel Bayesian approach.Tianxing ZhuJinyang LiuDaixing ZengXuan MiaoThe impact of policy uncertainty on A-share industry returns shows significant time-varying characteristics, amplified by industry input-output relationships. Traditional TVP-VAR models overlook network structures, leading to unquantified spillover effects and imprecise systemic risk identification. To address this problem, this study embeds industry input-output tables as matrices into Time-Varying Parameter Spatial Autoregressive Model, and Bayesian methods are innovatively introduced into this model to capture the parameters. Policy uncertainty is categorized into five dimensions-economic, fiscal, monetary, exchange rate, and trade. Empirical results reveal following key findings: On average, network spillover effects explain approximately 39% of the response of A-share industry returns to policy uncertainty. Group analysis reveal that economic and fiscal policy uncertainties exhibit positive network effects, indicating synergistic effect that amplify their impact across industries. In contrast, exchange rate and trade policy uncertainties generate negative network effects, reflecting competitive or substitution effects. Systemic risk is most pronounced in fiscal and trade policy uncertainty groups. Systemic risk increases across all policy uncertainty groups except trade, which shows a declining trend. This study provides a novel framework for understanding the dual nature of spillover effect in production networks, offering valuable insights for policymakers and investors to manage systemic risks and indentify synergistic and competitive effects in interconnected industries.https://doi.org/10.1371/journal.pone.0326605 |
| spellingShingle | Tianxing Zhu Jinyang Liu Daixing Zeng Xuan Miao Time-varing effect of policy uncertainty on A-share industry returns- A novel Bayesian approach. PLoS ONE |
| title | Time-varing effect of policy uncertainty on A-share industry returns- A novel Bayesian approach. |
| title_full | Time-varing effect of policy uncertainty on A-share industry returns- A novel Bayesian approach. |
| title_fullStr | Time-varing effect of policy uncertainty on A-share industry returns- A novel Bayesian approach. |
| title_full_unstemmed | Time-varing effect of policy uncertainty on A-share industry returns- A novel Bayesian approach. |
| title_short | Time-varing effect of policy uncertainty on A-share industry returns- A novel Bayesian approach. |
| title_sort | time varing effect of policy uncertainty on a share industry returns a novel bayesian approach |
| url | https://doi.org/10.1371/journal.pone.0326605 |
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