IQTN: An Interpretable Quantile Temporal Network for Systems-Oriented Tail-Risk Forecasting and Early Warning in Carbon Allowance Market
IQTN: 炭素排出権市場におけるシステム指向テールリスク予測と早期警戒のための解釈可能な分位時系列ネットワーク (AI 翻訳)
Tianli Huang, Grace T. R. Lin
🤖 gxceed AI 要約
日本語
中国の炭素排出権（CEA）市場向けに、解釈可能な分位時系列ネットワーク（IQTN）を提案。特徴量ゲーティング、因果的畳み込みエンコーダ、非交差分位出力層を統合し、多期間のVaR・CVaRを予測。95%VaRピンボール損失で比較手法を上回り、解釈性分析によりボラティリティや流動性が主要リスク因子と判明。短期市場情報への感応性が高いことを示し、早期警戒システムとして有効。
English
Proposes an Interpretable Quantile Temporal Network (IQTN) for the Chinese carbon allowance (CEA) market, integrating feature gating, causal temporal convolution, and non-crossing quantile layers to forecast multi-horizon VaR and CVaR. Achieves lowest pinball loss for 95% VaR across all horizons (1-, 5-, 10-day) compared to benchmarks. Interpretability analysis identifies volatility, liquidity, and loss magnitude as key risk drivers, confirming short-term market sensitivity. Supports use as an early warning tool for carbon market tail risk.
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（東京GX排出権取引所等）や排出量取引制度のリスク管理体制構築に示唆。SSBJ対応では市場リスクの開示が求められており、本手法は企業のカーボンアカウンティングやリスク管理高度化に貢献可能。中国データを基にした結果だが、日本市場への適用可能性も期待される。
In the global GX context
Demonstrates an AI-driven risk monitoring framework for emissions trading, directly relevant to TCFD/ISSB climate risk disclosures. As global carbon markets expand (EU ETS, China, emerging systems), deep quantile networks offer robust tail-risk forecasting essential for transition finance and portfolio management. The interpretability aspect addresses the demand for transparent, explainable risk models in regulatory contexts.
👥 読者別の含意
🔬研究者:Provides a novel deep learning framework (IQTN) combining causal temporal convolution with quantile regression for carbon market tail risk, with thorough benchmarking and interpretability analysis.
🏢実務担当者:Offers a deployable early warning system for carbon allowance trading desks and risk managers, integrating liquidity and volatility metrics into VaR/CVaR forecasts.
🏛政策担当者:Demonstrates how advanced ML can enhance market stability monitoring and systemic risk surveillance in emissions trading schemes, informing regulatory early warning design.
📄 Abstract（原文）
The carbon emission allowance (CEA) market is a complex socio-technical and environmental-management system in which regulatory design, trading activity, liquidity conditions, and price volatility interact dynamically. Accurate systems-level tail-risk forecasting and early warning remain challenging because carbon-market losses are affected by nonlinear dependence, episodic liquidity stress, and time-varying volatility. This study proposes an Interpretable Quantile Temporal Network (IQTN) as a systems-oriented risk-monitoring framework for China’s national CEA market. By integrating a feature-gating mechanism, a causal temporal convolutional encoder, and a non-crossing quantile output layer, IQTN directly models the conditional tail distribution of future carbon-market losses. The framework produces multi-horizon Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) forecasts for 1-day, 5-day, and 10-day horizons and converts predicted tail risk into operational early-warning signals. Compared with historical simulation, EWMA, GARCH-type models, machine-learning quantile models, and deep temporal benchmarks, IQTN achieved the lowest 95% VaR pinball loss across all horizons, with values of 0.1765, 0.3958, and 0.5732. VaR backtesting showed empirical exceedance rates of 5.23%, 6.04%, and 6.94%, closest to the nominal 5% level. Interpretability analysis identified rolling volatility, maximum loss, intraday range, trading value, and illiquidity as key risk drivers. The temporal importance results also show that recent observations dominated the risk forecasts, suggesting that the risk state of the CEA market is highly sensitive to short-term market information. This supports the use of a short-horizon temporal network as a systems-oriented tool for carbon-market tail-risk monitoring and early warning.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.mdpi.com/2079-8954/14/7/734/pdf?version=1782969226first seen 2026-07-13 06:37:06
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