Forecasting and Pricing in the Carbon Credits Market
炭素クレジット市場の予測と価格設定 (AI 翻訳)
Yiyang Chen, Rogemar Mamon, Fabio Spagnolo, Nicola Spagnolo, Heng Xiong
🤖 gxceed AI 要約
日本語
本研究は、EU排出量取引制度（EU-ETS）における炭素価格の動学を4つの確率過程とその隠れマルコフモデル拡張を用いて分析。レジームスイッチングにより跳散逸過程の精度が向上し、多期間予測・密度較正・ヘッジ性能評価に貢献。実務的なリスク管理と政策設計への示唆を提供。
English
This paper analyzes EUA spot-price dynamics using four stochastic processes and their hidden Markov model extensions. Regime switching enhances jump-diffusion accuracy, improving multi-horizon forecasting, density calibration, and hedging performance. Practical insights for carbon market risk management and policy design.
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
日本でもカーボンプライシングの導入が進む中、EU-ETSの価格変動リスクを高度にモデル化した本手法は、今後の日本市場の制度設計やリスク管理に参考となる。ただし、直接的な日本市場分析は含まれない。
In the global GX context
As global carbon pricing expands (EU ETS, China ETS, etc.), this paper provides advanced pricing and hedging models that can inform derivative markets and risk management for emissions trading systems worldwide.
👥 読者別の含意
🔬研究者:Advanced quantitative modeling of carbon prices with regime-switching processes for forecasting and hedging.
🏢実務担当者:Methods to improve carbon credit valuation and risk management for trading desks and compliance teams.
🏛政策担当者:Insights into how carbon market dynamics affect price stability and hedging effectiveness.
📄 Abstract（原文）
ABSTRACT The European Union Emissions Trading System has emerged as a cornerstone of climate policy, with carbon allowance prices exhibiting complex dynamics influenced by policy reforms, market shocks, and structural transitions. This study examines European Union Allowances (EUA) spot‐price dynamics through four canonical processes and their regime‐switching hidden Markov model (HMM) extensions, estimated using rolling windows to capture time‐varying parameters. Our framework integrates in‐sample fitting, multi‐horizon forecasting, density calibration, and hedging performance evaluation, revealing that regime switching systematically enhances model flexibility and accuracy, particularly for jump‐diffusion specifications. By incorporating maturity‐matched risk‐free rates and statistical tests, we demonstrate that regime‐switching models better capture state‐dependent behaviors and provide more reliable derivatives pricing and risk management insights. These findings offer practical value for market participants and policymakers in navigating carbon market risks and designing effective hedging strategies.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.1002/for.70168first seen 2026-05-26 05:20:31 · last seen 2026-05-27 05:11:23
- openalex https://doi.org/10.1002/for.70168first seen 2026-05-27 04:53:47
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