Forecasting carbon returns under structural breaks and model uncertainty: a time-weighted regularized combination approach
構造的ブレークとモデルの不確実性下での炭素リターンの予測：時系列加重正則化組み合わせアプローチ (AI 翻訳)
Zhikai Zhang, Yaojie Zhang, Yudong Wang, Qunwei Wang
🤖 gxceed AI 要約
日本語
炭素排出権取引システムは構造的変化を経験しており、炭素価格リターンの予測は困難です。本研究では、構造的変化を識別しモデルの不確実性を低減する時系列加重正則化組み合わせ（TWRC）手法を提案しています。実証分析では、TWRCが他の競合手法よりも優れた予測精度を示し、炭素先物を用いたポートフォリオ演習で経済的に有意な効用を生み出すことが示されました。
English
This paper proposes a time-weighted regularized combination (TWRC) method to forecast carbon price returns under structural breaks and model uncertainty. The TWRC identifies structural changes and mitigates uncertainty among factor-based forecasts. Empirical results show that TWRC outperforms other combination methods in accuracy and generates economically significant utility in a portfolio exercise with carbon futures. It improves forecasts by reducing both variance and bias.
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
日本の炭素市場（東証カーボン・クレジット市場など）も複数のフェーズを経て制度変更が行われており、本論文の手法は予測モデルの頑健性向上に寄与する可能性があります。日本の排出量取引制度の設計や投資家のリスク管理に示唆を与えるでしょう。
In the global GX context
Carbon pricing markets globally are undergoing structural shifts (e.g., EU ETS Phase IV, China's national ETS expansions). This paper's TWRC method offers a robust forecasting approach that can be applied to any carbon market facing policy uncertainty, aiding traders, risk managers, and policymakers in making informed decisions.
👥 読者別の含意
🔬研究者:The TWRC method provides a novel technique for forecasting under structural breaks, applicable to carbon markets and other commodity prices.
🏢実務担当者:For corporate sustainability teams, the paper offers insights into forecasting carbon price returns, which is crucial for carbon risk management and investment in carbon allowances.
🏛政策担当者:The findings highlight the importance of accounting for structural breaks in carbon market design and regulation.
📄 Abstract（原文）
The carbon emission trading systems undergo a series of phases and changes in their associated mechanisms and policies. The movement of carbon price returns is accordingly affected by structural breaks and fundamental shifts, which are empirically confirmed by the statistical tests. In this paper, we propose a time-weighted regularized combination (TWRC), which identifies the structural changes and mitigates the uncertainty among extensive factor-based forecasts. Our empirical findings suggest that the TWRC method outperforms other competitive combinations in forecasting carbon price returns. The forecasts of TWRC are also economically significant, which generate high utility in a portfolio exercise with carbon futures. The TWRC improves the forecast accuracy by lowering both the variance and bias components of the forecast error. The mechanism of the TWRC reveals that when structural changes are more frequent, indicated by the heavier weights assigned to recent forecasts, stronger shrinkages are imposed on individual models. This implies a trade-off between the recency of information and model dimensions in predicting carbon returns under structural changes and model uncertainty.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1080/14697688.2025.2602783first seen 2026-05-15 17:15:49
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