炭素価格予測のための単調多出力混合頻度分位点回帰ニューラルネットワーク
A monotonic multi-output mixed-frequency quantile regression neural network for carbon price prediction (原題)
Xiwen Qin, Liping Yuan, Xiaogang Dong, Siqi Zhang, Hongyu Shi
🤖 gxceed AI 要約
日本語
本研究は、炭素価格予測の精度向上と不確実性の定量化を目的に、混合頻度データを直接扱う新モデルMMQRGRU-MIDASを提案する。LASSOで主要因を選別し、MIDASで周波数混在データを補間せずに活用、単調性制約付き多出力分位点回帰とGRUで分位点交差問題を解消しつつ非線形動学を捉える。広州・湖北のパイロット炭素市場での実証で、点予測・区間予測ともに既存モデルを上回り、分位点交差をゼロに抑えた。炭素市場のリスク管理・市場分析に有効な分析ツールを提供する。
English
This study proposes MMQRGRU-MIDAS, a monotonic multi-output mixed-frequency quantile regression neural network for carbon price prediction. Using LASSO for factor selection, a MIDAS module to model raw mixed-frequency data without interpolation, monotonicity-constrained multi-output quantile regression, and a GRU to capture nonlinear dynamics, it resolves quantile crossing. Empirical tests on China's Guangzhou and Hubei pilot carbon markets show superior point and interval forecasting with zero crossing loss, offering a tool for carbon market risk management.
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
日本ではGX-ETSや炭素価格制度の議論が進む中、炭素価格の不確実性を区間予測で捉える手法は、企業のカーボンコスト見積りや投資判断、政策設計に示唆を与える。中国パイロット市場の実証だが、手法自体は日本市場データにも応用可能。
In the global GX context
As carbon pricing expands globally (EU ETS, CBAM, emerging Asian ETS), robust carbon price forecasting with uncertainty quantification supports transition finance, hedging, and policy design. The mixed-frequency ML approach is transferable to other carbon markets beyond China's pilots.
👥 読者別の含意
🔬研究者:混合頻度・分位点回帰・単調性制約を組み合わせた炭素価格予測の手法論として、時系列ML研究に有用。
🏢実務担当者:炭素価格の区間予測により、カーボンコストのリスク評価やヘッジ・調達戦略の精度向上に活用可能。
🏛政策担当者:炭素市場の価格変動と不確実性を定量化する手法は、ETS設計や市場安定化策の検討に参考となる。
📄 Abstract（原文）
Accurate carbon price prediction is crucial for market trading and policy-making. Existing research is mostly based on point prediction using single-frequency data, which makes it difficult to utilize high-frequency information and effectively characterize price uncertainty. Therefore, this study proposes a novel monotonic multi-output mixed-frequency quantile regression neural network model (MMQRGRU-MIDAS). Firstly, this study employs the Least Absolute Shrinkage and Selection Operator (LASSO) method to select key influencing factors from three major categories of variables: energy commodities, financial market indicators, macroeconomic indicators. And introduce the Mixed Data Sampling Regression (MIDAS) module to directly model the original mixed-frequency data, avoiding information loss caused by interpolation or co frequency processing. Furthermore, multi-output structure with monotonicity constraints is designed, and regularization term is added to the loss function to solve the quantile crossing problem in multi quantile joint prediction. Finally, the Gated Recurrent Unit (GRU) module is combined to capture the nonlinear dynamic characteristics of carbon price time series. Empirical studies on two pilot carbon markets in Guangzhou and Hubei have shown that the proposed MMQRGRU-MIDAS model outperforms other comparative models in both point and interval prediction tasks. And the model solves the quantile crossing problem with zero cross loss and cross rate. The results validate the superiority of the proposed model in forecasting accuracy, interval quality, and computational efficiency, providing an effective analytical tool for carbon market risk management and market analysis.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.engappai.2026.116160first seen 2026-09-20 04:40:15
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