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A Break-Regime Score-Driven Model for Tail-Risk Forecasting in China’s Carbon Market Under Policy Shifts

政策転換下における中国炭素市場のテールリスク予測のためのブレークレジームスコア駆動モデル (AI 翻訳)

Xinshu Gong, Bin Zheng

Mathematics📚 査読済 / ジャーナル2026-05-19#炭素価格Origin: CN経営インパクト: 資金調達対象セクター: finance
DOI: 10.3390/math14101745
原典: https://doi.org/10.3390/math14101745

🤖 gxceed AI 要約

日本語

本論文は、中国の炭素市場におけるテールリスク予測のためのブレークレジームGASモデル（BR-GAS-t）を提案する。政策転換による構造変化を明示的に組み込むことで、VaRとESの予測精度が向上することを実証。特に2.5%と1%の裾野で優位性が顕著であり、政策主導型炭素市場におけるリスク管理への示唆を与える。

English

This paper proposes a break-regime GAS model (BR-GAS-t) for tail-risk forecasting in China's carbon market, explicitly incorporating policy shifts. It demonstrates improved VaR and ES forecasting accuracy, especially at the 2.5% and 1% tails, offering insights for risk management in policy-driven carbon 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

日本の排出量取引市場はまだ発展途上であり、政策転換が価格リスクに与える影響を考慮したテールリスク管理手法は、今後の市場設計や企業のリスク管理に示唆を与える。特に、GX推進に伴う制度変更が価格変動に与える影響を予測する上で参考になる。

In the global GX context

This study provides a robust methodology for tail-risk forecasting in carbon markets under policy shifts, relevant for global carbon pricing mechanisms and climate risk management. It offers insights for regulators and market participants in designing resilient carbon markets, especially in the context of evolving climate policies.

👥 読者別の含意

🔬研究者:Provides a novel model for tail-risk forecasting in carbon markets, useful for extending to other policy-driven markets.

🏢実務担当者:Offers a tool for improving risk management in carbon trading, potentially applicable to compliance and voluntary markets.

🏛政策担当者:Highlights the importance of accounting for policy shifts in market risk assessment, informing market design and stability measures.

📄 Abstract（原文）

Accurate tail-risk measurement in carbon markets is challenging because carbon allowance prices are shaped not only by heavy-tailed return dynamics, but also by policy changes that can alter the underlying risk dynamics. Models that ignore such structural shifts may perform reasonably well in normal periods while still understating downside risk when market conditions change. To address this issue, this paper proposes a break-regime generalized autoregressive score model with Student-t innovations, denoted as BR-GAS-t, for one-step-ahead forecasting of Value-at-Risk and Expected Shortfall. Using daily spot data from China’s carbon market, we compare BR-GAS-t with historical simulation, GARCH-N, GARCH-t, and regime-free GAS-t benchmarks. The results show that carbon returns are strongly heavy-tailed and that the post-break regime is characterized by stronger shock sensitivity, lower persistence, and a higher long-run conditional scale. Out-of-sample evidence further indicates that BR-GAS-t delivers the strongest overall VaR backtesting performance and the lowest average Fissler–Ziegel (FZ) loss in joint VaR–ES evaluation. Its advantage is most pronounced at the 2.5% and 1% tails, where downside risk is hardest to forecast. Robustness checks based on alternative break dates, window lengths, recursive schemes, and distributional assumptions confirm that the main conclusion is stable. The findings suggest that explicitly incorporating observed policy breaks improves tail-risk forecasting in policy-driven carbon markets.

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