Technical Analysis and Regime Shifts in EUA Carbon Futures
This study investigates the price dynamics of European Union Allowances (EUAs) using a comprehensive battery of technical indicators. Covering the period from April 2005 to September 2025, we document the transition from a low-volatility, range-bound market to a high-volatility trending regime after...
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| Format: | Article ou chapitre numérique |
| Język: | Français |
| Wydane: |
2026
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| Hasła przedmiotowe: | |
| Dostęp online: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |
| Streszczenie: | This study investigates the price dynamics of European Union Allowances (EUAs) using a
comprehensive battery of technical indicators. Covering the period from April 2005 to
September 2025, we document the transition from a low-volatility, range-bound market
to a high-volatility trending regime after 2018. We argue that this structural break is primarily driven by the 2019 activation of the Market Stability Reserve (MSR) mechanism
and the anticipation of Phase 4 of the EU ETS, which fundamentally altered the supply-demand balance of allowances and transformed EUA futures into a directionally trending, institutionally traded asset class. Eleven classic indicators—including moving averages, Bollinger Bands, Ichimoku, DMI, MACD, and oscillators—are computed and
visually examined. The indicators consistently signal the structural break, extreme over-extensions during the 2021–2022 price spike, and subsequent correction. A zoomed
candlestick chart of the post-peak period illustrates the increased intraday volatility and
uncertainty. Our central contribution is threefold: (i) we provide the first systematic chartist audit of EUA futures across the full history of the EU ETS (2005-2025), spanning all
four compliance phases; (ii) we demonstrate that standard technical tools—originally designed for equities and currencies—retain their diagnostic power in a regulated, policy
driven commodity market; and (iii) we document the visual evidence base that can serve
as inputs to image-based machine learning models for carbon price forecasting. The
analysis demonstrates that technical tools can capture regime shifts and provide early
warnings in carbon markets, complementing fundamental and econometric approaches. |
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