Risk Measurement of Chinese Carbon Emissions Trading Market Based on DCS-Type Models
DCS型モデルに基づく中国炭素排出権取引市場のリスク測定 (AI 翻訳)
Aijun Yang, Tian Lan, Chunying Zhou, Ying Hu
🤖 gxceed AI 要約
日本語
本研究は、中国湖北省の炭素排出権取引市場の価格リスクをVaRとESで定量化し、動的条件付きスコア（DCS）フレームワークと歪んだスチューデントt分布を組み合わせたモデル群を構築した。2014年4月から2024年12月までのHBEAスポット価格を用いた実証分析の結果、DCS-STモデルが最適な適合性能を示し、炭素市場のテールリスク管理に有効な手法を提供することを明らかにした。
English
This study uses VaR and ES to measure price risk in Hubei's carbon emissions trading market, developing DCS-type models with skewed Student-t distribution. Empirical analysis on daily HBEA spot prices from April 2014 to December 2024 shows the DCS-ST model provides the best fit and effectively captures tail risk, offering a reliable risk management tool for China's carbon market.
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
中国炭素市場のリスク計測手法を提示。日本でも炭素価格メカニズムの設計や市場リスク管理に応用可能性がある。
In the global GX context
Provides a robust risk measurement framework for carbon markets, relevant for global carbon pricing schemes (e.g., EU ETS, China ETS). The DCS modeling approach can be adapted to other emissions trading systems.
👥 読者別の含意
🔬研究者:Demonstrates the application of DCS-type models for carbon price risk, offering a methodological contribution to carbon finance literature.
🏢実務担当者:Carbon market participants can use the VaR/ES approach for risk management and hedging strategies.
🏛政策担当者:Regulators can consider the model for monitoring market stability and setting risk buffers.
📄 Abstract（原文）
The Hubei carbon emissions trading market presents significant price volatility driven by energy price fluctuations, macroeconomic conditions and policy changes. Accurate price risk measurement is critically important for market participants. This study adopts Value at Risk (VaR) and Expected Shortfall (ES) to quantify market risk, and constructs a set of DCS-type models by combining the dynamic conditional score framework with the skewed Student-t distribution. Model evaluation covers unconditional coverage test, conditional coverage test, dynamic quantile test, the Actual-to-Expected ratio, the mean and the maximum absolute deviation, quantile loss and FZ loss. Empirical analysis based on daily HBEA spot prices from 3 April 2014 to 4 December 2024 shows that: (1) The DCS-ST model provides better data fitting performance and can effectively measure the market risk of China’s carbon trading market. (2) The parameter updating frequency has little impact on the prediction accuracy of the model. The results enriches the quantitative methodology for carbon market risk measurement and provide a reliable technical scheme for tail risk management in China’s carbon emissions trading market.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/math14081313first seen 2026-05-05 19:13:40
- semanticscholar https://doi.org/10.3390/math14081313first seen 2026-05-05 22:45:17
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