A Comparative Analysis of Green and Brown Stocks: The Impact of Uncertainty Indices on Tail-Risk Forecasting
グリーン株とブラウン株の比較分析：不確実性指数がテールリスク予測に与える影響 (AI 翻訳)
Antonio Naimoli, Giuseppe Storti
🤖 gxceed AI 要約
日本語
気候関連の不確実性指数がグリーン株とブラウン株のテールリスク予測に与える影響を分析。遷移リスクが1％水準で両資産クラスに支配的であることを発見。地政学リスクと経済政策不確実性は2.5％水準で異なる影響を示す。低炭素移行期のリスク管理と規制監督への示唆。
English
This paper analyzes whether climate, geopolitical, and economic policy uncertainty indices improve tail-risk forecasts for green and brown stocks. It finds that transition climate risk dominates at the 1% level for both asset classes, while geopolitical risk and economic policy uncertainty lead at the 2.5% level. The results have implications for risk management and regulatory oversight during the low-carbon transition.
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
日本の金融機関や機関投資家にとって、低炭素移行に伴うポートフォリオのテールリスク評価は喫緊の課題。本論文の分析フレームワークは、グリーン・ブラウン双方の資産に対するリスク管理手法の高度化に貢献し、SSBJ開示やTCFD対応にも有用な示唆を与える。
In the global GX context
This paper provides empirical evidence on how different uncertainty shocks propagate into financial tail-risk, distinguishing between green and brown assets. For global regulators and risk managers, it underscores the need to incorporate transition and geopolitical risks into stress testing and capital adequacy frameworks, aligning with TCFD and ISSB disclosure expectations.
👥 読者別の含意
🔬研究者:Provides methodological extension of Realized-ES-CAViaR with climate risk indices, useful for financial econometrics researchers.
🏢実務担当者:Helps risk managers and portfolio managers calibrate tail-risk models using climate and geopolitical uncertainty factors.
🏛政策担当者:Demonstrates the differential impact of uncertainty shocks across asset classes, informing macroprudential policy for the low-carbon transition.
📄 Abstract（原文）
This paper examines whether climate, geopolitical and economic policy uncertainty indices improve Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts for green and brown stocks. We extend the Realized-ES-CAViaR framework by incorporating physical and transition climate risk, geopolitical risk and economic policy uncertainty indices alongside a high-low range volatility estimator. Using daily data for the iShares Global Clean Energy ETF (ICLN) and the iShares Global Energy ETF (IXC) over the period January 2012–December 2024, we evaluate alternative model specifications at the 1% and 2.5% risk levels through backtesting procedures, strictly consistent scoring rules and the Model Confidence Set methodology. Results reveal a pronounced asymmetry in the predictive content of risk indices across asset classes and quantile levels. Transition climate risk dominates tail-risk forecasting at the 1% level for both asset classes, while geopolitical risk and economic policy uncertainty emerge as the leading factors at the 2.5% level for green and brown stocks, respectively. These findings highlight the heterogeneous channels through which uncertainty shocks propagate into financial tail-risk, with direct implications for risk management and regulatory oversight during the low-carbon transition.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/forecast8020031first seen 2026-06-29 08:40:44
🔔 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。