Tail Dependence in EU Carbon Markets: Graphical Models of Extremes for EUA Futures
EU炭素市場におけるテール依存：EUA先物の極値グラフィカルモデル (AI 翻訳)
Jan Maciejowski, Manuele Leonelli
🤖 gxceed AI 要約
日本語
本論文は、EU ETSのフェーズ3および4（2013～2025年）における20変数システムにHüsler-Reiss極値グラフィカルモデルを適用し、テールネットワークが平均依存ネットワークと構造的に異なることを示した。EUA先物は平均依存では周辺的だがテールネットワークで中心性が最も高く、株価指数や為替は逆の軌跡をたどる。フェーズ移行によりテールネットワークは縮小せず、局所的伝染から拡散的伝染へと変化する。これらの知見は、コンプライアンス企業のヘッジ構築、規制当局のストレステスト、EU ETSのシステミックリスク監視に直接的な示唆を与える。
English
This paper applies Hüsler-Reiss graphical models of extremes to a 20-variable system centered on EUA futures across EU ETS Phases 3 and 4 (2013-2025). Tail networks are structurally distinct from average dependence: EUA futures become central in tails, while equities and FX become peripheral. The phase transition preserves tail density but shifts crash contagion from clustered to diffuse propagation. Findings inform hedge construction, stress-test calibration, and systemic-risk monitoring for EU ETS markets.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本企業がEU ETSに関連する事業を行う際、本論文の知見は炭素価格の急激な変動リスクに対するヘッジ戦略やストレステストに活用できる。また、日本独自の排出量取引制度設計においても、フェーズ移行による伝染構造の変化は参考になる。
In the global GX context
This paper advances global understanding of carbon market risk by revealing that tail dependence in the EU ETS persists even as average dependence declines, with phase transitions altering contagion patterns. Regulators and compliance entities worldwide can apply these graphical modeling approaches to stress-testing and systemic risk monitoring in carbon markets.
👥 読者別の含意
🔬研究者:Extreme value theory and graphical models applied to carbon finance: a benchmark for tail-risk analysis in emissions trading.
🏢実務担当者:Direct implications for hedge construction and stress-testing strategies for EUA futures and related portfolios.
🏛政策担当者:Insights for designing systemic-risk monitoring tools and understanding phase-transition effects in carbon market regulation.
📄 Abstract（原文）
Understanding how extreme price movements propagate across financial and energy markets is critical for risk management and regulatory design in the EU Emissions Trading System (EU ETS). We apply H\"{u}sler-Reiss graphical models of extremes to a system of 20 daily variables centred on EU allowances futures across Phases 3 and 4 of the EU ETS (2013--2025), with a Gaussian graphical model as the average-dependence baseline. The tail networks are structurally distinct from the average dependence network: substantially denser, organized around different central nodes, and governed by within-sector homophily that binds sector boundaries more tightly than at the average-dependence level. EU allowances futures are peripheral in the standard graphical model but achieve the highest centrality in the tail networks, while equity indices and major FX pairs follow the opposite trajectory. Exponential random graph models confirm equity and FX peripherality in tail networks across all sample periods and identify triadic closure during market downturns as a Phase~3 phenomenon that vanishes in Phase~4. The phase transition restructures the tail network without thinning it: average dependence contracts sharply while tail dependence persists, and crash contagion shifts from clustered to diffuse propagation. These findings have direct implications for hedge construction by compliance entities, stress-test calibration by regulators, and the design of systemic-risk monitoring tools for EU ETS markets.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.semanticscholar.org/paper/31f74d727bd54c9470ebfa579e8f1c539f1ca806first seen 2026-06-29 05:58:43
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。