Financial uncertainty, climate risks, and climate-exposed assets: Evidence from time-varying and quantile-based analysis
金融不確実性、気候リスク、気候エクスポージャー資産：時変および分位数ベースの分析からのエビデンス (AI 翻訳)
Rim Khoury, Zhuhua Jiang, Oguzhan Ozcelebi, Seong-Min Yoon
🤖 gxceed AI 要約
日本語
本研究は、ESG株式、クリーンエネルギー株、炭素クレジット、ブロックチェーン投資などの気候関連資産に対する金融不確実性と気候リスクの影響を分析する。DCC-GARCHと分位数回帰を用いた結果、これらの資産は市場変動時の分散効果が弱まること、物理的気候リスクの価格付けに非対称性があることなどが示された。
English
This study examines how financial uncertainty and climate risks transmit to climate-exposed assets including ESG equities, clean energy stocks, carbon allowances, and blockchain investments. Using DCC-GARCH and quantile-based methods, it finds time-varying correlations, asymmetric effects across return distributions, and divergence in physical risk pricing between clean energy and carbon-intensive assets.
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
日本の機関投資家や金融機関にとって、ESG・クリーンエネルギー資産のリスク特性を非線形な視点で理解することは、ポートフォリオの気候レジリエンス向上に寄与する。SSBJ開示が進む中、物理的リスクと移行リスクの金融市場への波及経路を定量的に示した点が重要。
In the global GX context
For global investors and policymakers, this paper provides quantitative evidence on how climate risks are priced in different asset classes, highlighting the need for nonlinear risk models. It supports the TCFD/ISSB emphasis on scenario analysis and tail risks, and informs transition finance strategies by distinguishing green and brown technology exposures.
👥 読者別の含意
🔬研究者:Methodological contribution on time-varying and quantile dependence between financial uncertainty, climate risks, and climate-exposed assets.
🏢実務担当者:Portfolio managers can use the findings to reassess diversification benefits and tail-risk exposure in ESG and clean energy investments.
📄 Abstract（原文）
This study examines how financial uncertainty and climate risks transmit to climate-exposed investment assets, with particular attention to time-varying dependence, distributional asymmetry, and regime-specific predictability. Using weekly data on ESG equities (ESGU, SUSA), clean energy stocks (ICLN), carbon allowance assets (KRBN), and blockchain technology investments (BLOK), we employ an integrated empirical framework combining a Dynamic Conditional Correlation (DCC)-GARCH model, quantile-on-quantile (QQ) regression, and quantile-on-quantile Granger causality (QQGC). The results show that these assets are not insulated from systemic uncertainty, as their correlations with financial volatility are time-varying and often highly persistent, implying that diversification benefits can weaken in turbulent market conditions when they are most needed. Quantile-based evidence reveals pronounced asymmetry and asset-specific heterogeneity: for broad ESG equities, effects are relatively uniform across return quantiles, while for clean energy and blockchain assets, the strongest effects appear in specific tail combinations. Notably, we identify a divergence in physical climate risk pricing: while low-level risk news supports clean energy valuations, extreme physical risk news disproportionately penalizes high-energy-intensity blockchain assets. Overall, the findings underscore the importance of accounting for nonlinear dynamics and the "green–brown" technology spectrum when evaluating asset resilience.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.2139/ssrn.6478229first seen 2026-06-29 04:55:33
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