Climate Risk Contagion and Financial Stability During the Low-Carbon Transition: A Multiscale Vine-Copula Analysis
低炭素移行期における気候リスクの伝染と金融安定性：マルチスケール・バイン・コピュラ分析 (AI 翻訳)
Li Zeng, Jinghui Huang
🤖 gxceed AI 要約
日本語
本研究は、低炭素移行期における気候関連金融リスクの市場間伝染効果を分析。2020年4月から2025年4月の日次データを用い、ウェーブレット分解・条件付きボラティリティ・バイン・コピュラを統合したテールリスクモデルを適用。国際気候市場と国内市場の非線形・非対称な依存関係や、上昇局面と下降局面で異なるリスク波及を発見。中央銀行・規制当局への気候ストレステスト等への示唆を提示。
English
This study analyzes contagion effects of climate financial risks across markets during the low-carbon transition. Using daily data from April 2020 to April 2025, it applies a multiscale tail risk framework combining wavelet decomposition, conditional volatility modeling, and vine-copula techniques. Results show volatility clustering, nonlinear dependence, asymmetric risk spillovers between upside and downside states, and weaker links from international climate markets. Provides implications for climate stress testing and macroprudential surveillance.
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
日本でも気候変動関連リスクが金融システムに与える影響が注目されており、日銀・金融庁による気候ストレステストの高度化に資する。中国新興市場を対象とするが、手法は日本の金融機関のリスク管理にも応用可能。
In the global GX context
This paper contributes to the global literature on climate financial risk contagion, offering a multiscale empirical framework applicable to both advanced and emerging economies. Its findings on asymmetric and time-varying risk spillovers are directly relevant for central banks and regulators implementing climate stress tests under NGFS scenarios.
👥 読者別の含意
🔬研究者:Provides a methodological framework for analyzing multiscale and asymmetric climate risk spillovers, useful for further empirical studies.
🏢実務担当者:Risk managers can apply the insights on cross-market contagion to enhance portfolio risk assessment and hedging strategies.
🏛政策担当者:Central banks and regulators should consider nonlinear and horizon-dependent risk spillovers when designing climate stress tests and early warning systems.
📄 Abstract（原文）
As the global economy accelerates toward low-carbon transformation, climate financial risks are emerging as a key challenge to monetary policy design and financial stability oversight. This study examines the contagion effects and dynamic interdependencies among domestic climate-sensitive industries, financial climate risk indices, and international climate markets. Using daily data from April 2020 to April 2025, we apply a multiscale tail risk modeling framework that integrates wavelet decomposition, conditional volatility modeling, and vine-copula techniques to capture time-varying and asymmetric dependence structures across markets. The results show that the three markets display volatility clustering, fat tails, and nonlinear dependence. The international climate market shows weaker and more volatile connections with the two domestic markets, suggesting that external climate expectations operate mainly through indirect dependence across market states. The risk spillover results further show that climate financial risk contagion differs between upside and downside states and varies across short and medium horizons. These findings have important implications for integrating climate risk into macroprudential surveillance. Central banks and regulators should strengthen early warning mechanisms, climate stress testing, and scenario analysis by considering market-specific, nonlinear, and multiscale risk spillovers. The main contribution of this study is to integrate multiscale decomposition, conditional volatility modelling, and vine-copula dependence analysis into a unified empirical framework for identifying climate financial risk contagion across markets. The findings offer useful evidence for climate stress testing, early warning systems, and financial stability monitoring in emerging markets.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18147344first seen 2026-07-22 04:59:46
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