Causal analysis of extreme risk in a network of industry portfolios
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909807721578496 |
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| author | Klüppelberg, Claudia Krali, Mario |
| author_facet | Klüppelberg, Claudia Krali, Mario |
| contents | We provide a comprehensive review of causal dependence through a max-linear structural equation model. Such models express each node variable as a max-linear function of its parental node variables in a directed acyclic graph and some exogenous innovation. We reformulate results on structure learning and estimation, which we apply to a network of financial data. A new method, based on hard-thresholding and on the Hamming distance, estimates a sparse DAG for extreme risk~propagation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00523 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal analysis of extreme risk in a network of industry portfolios Klüppelberg, Claudia Krali, Mario Risk Management 91G45, 91G70, 62A09, 62G32 We provide a comprehensive review of causal dependence through a max-linear structural equation model. Such models express each node variable as a max-linear function of its parental node variables in a directed acyclic graph and some exogenous innovation. We reformulate results on structure learning and estimation, which we apply to a network of financial data. A new method, based on hard-thresholding and on the Hamming distance, estimates a sparse DAG for extreme risk~propagation. |
| title | Causal analysis of extreme risk in a network of industry portfolios |
| topic | Risk Management 91G45, 91G70, 62A09, 62G32 |
| url | https://arxiv.org/abs/2504.00523 |