Uncovering the network geometry of green bonds
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909776535879680 |
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| author | Zhang, Xinyue Kartun-Giles, Alexander P. |
| author_facet | Zhang, Xinyue Kartun-Giles, Alexander P. |
| contents | With the rapid growth of the green bond market amid increasing emphasis on sustainable development, understanding its structural properties and potential systemic risks has become essential. This study applies Minimum Spanning Tree (MST) and Hierarchical Tree (HT) methods introduced by Mantegna, which gives a minimum spanning tree of bonds whose mutual distance corresponds to the correlation coefficient of the synchronous time evolution of the difference of the logarithm of their monthly price. Our analysis reveals that green bonds exhibit tighter ultrametric distances on average compared to stocks and conventional bonds, indicating a highly interconnected market structure. We also identify strong sectorial clustering, with green bonds in the utilities sector emerging as the most central nodes by average total betweenness centrality, suggesting their critical role in potential risk propagation. These findings highlight both the opportunities and vulnerabilities inherent in the green bond market, offering insights for investors and policymakers on monitoring concentration, enhancing transparency, and mitigating systemic risks as the market continues to evolve. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05168 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncovering the network geometry of green bonds Zhang, Xinyue Kartun-Giles, Alexander P. Physics and Society With the rapid growth of the green bond market amid increasing emphasis on sustainable development, understanding its structural properties and potential systemic risks has become essential. This study applies Minimum Spanning Tree (MST) and Hierarchical Tree (HT) methods introduced by Mantegna, which gives a minimum spanning tree of bonds whose mutual distance corresponds to the correlation coefficient of the synchronous time evolution of the difference of the logarithm of their monthly price. Our analysis reveals that green bonds exhibit tighter ultrametric distances on average compared to stocks and conventional bonds, indicating a highly interconnected market structure. We also identify strong sectorial clustering, with green bonds in the utilities sector emerging as the most central nodes by average total betweenness centrality, suggesting their critical role in potential risk propagation. These findings highlight both the opportunities and vulnerabilities inherent in the green bond market, offering insights for investors and policymakers on monitoring concentration, enhancing transparency, and mitigating systemic risks as the market continues to evolve. |
| title | Uncovering the network geometry of green bonds |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2509.05168 |