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Causal analysis of extreme risk in a network of industry portfolios

Claudia Klüppelberg & Mario Krali

Canadian Journal of Statistics2026https://doi.org/10.1002/cjs.70041article
ABDC A
Weight
0.37

Abstract

We provide a detailed review of causal dependence within the framework of max‐linear structural models. Such models express each node variable as a max‐linear function of its parental node variables in a directed acyclic graph (DAG) 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.

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https://doi.org/https://doi.org/10.1002/cjs.70041

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@article{claudia2026,
  title        = {{Causal analysis of extreme risk in a network of industry portfolios}},
  author       = {Claudia Klüppelberg & Mario Krali},
  journal      = {Canadian Journal of Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/cjs.70041},
}

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Causal analysis of extreme risk in a network of industry portfolios

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.