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The efficiency analysis of the European CO2 futures market

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  • Bao-Jun Tang
  • Cheng Shen
  • Chao Gao

Abstract

The European Union Emissions Trading System (EU ETS) is the main international carbon trading market, in which European Union CO2 allowances (EUAs) are traded with increasing intensity. In order to help the market participants mitigate the market price risk, one possible way is to analyze the time range of market efficiency and the price discovery mechanism of EUA futures market and spot market. For this purpose, the paper provides the unit root test and the cointegration test for the EUA futures market during 2009-2011. Our result shows that the EUA futures market is efficient within 1 month. Furthermore, it illustrates that the impact of the price will continue for 3 months, examined by a vector error correction model (VECM).

Suggested Citation

  • Bao-Jun Tang & Cheng Shen & Chao Gao, 2012. "The efficiency analysis of the European CO2 futures market," CEEP-BIT Working Papers 36, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    • Handle: RePEc:biw:wpaper:36
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Federico Galán-Valdivieso & Elena Villar-Rubio & María-Dolores Huete-Morales, 2018. "The erratic behaviour of the EU ETS on the path towards consolidation and price stability," International Environmental Agreements: Politics, Law and Economics, Springer, vol. 18(5), pages 689-706, October.
    2. Zhao, Xin-gang & Jiang, Gui-wu & Nie, Dan & Chen, Hao, 2016. "How to improve the market efficiency of carbon trading: A perspective of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1229-1245.
    3. Jian Liu & Ziting Zhang & Lizhao Yan & Fenghua Wen, 2021. "Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH-MIDAS model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    4. Zhong, Meirui & Zhang, Rui & Ren, Xiaohang, 2023. "The time-varying effects of liquidity and market efficiency of the European Union carbon market: Evidence from the TVP-SVAR-SV approach," Energy Economics, Elsevier, vol. 123(C).
    5. Qiyun Cheng & Huiting Qiao & Yimiao Gu & Zhenxi Chen, 2023. "Price Dynamics and Interactions between the Chinese and European Carbon Emission Trading Markets," Energies, MDPI, vol. 16(4), pages 1-12, February.
    6. Liu, Xiaojia & An, Haizhong & Wang, Lijun & Jia, Xiaoliang, 2017. "An integrated approach to optimize moving average rules in the EUA futures market based on particle swarm optimization and genetic algorithms," Applied Energy, Elsevier, vol. 185(P2), pages 1778-1787.
    7. Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.
    8. Mu, Yaqian & Evans, Samuel & Wang, Can & Cai, Wenjia, 2018. "How will sectoral coverage affect the efficiency of an emissions trading system? A CGE-based case study of China," Applied Energy, Elsevier, vol. 227(C), pages 403-414.
    9. Wang, Xiao-Qing & Su, Chi-Wei & Lobonţ, Oana-Ramona & Li, Hao & Nicoleta-Claudia, Moldovan, 2022. "Is China's carbon trading market efficient? Evidence from emissions trading scheme pilots," Energy, Elsevier, vol. 245(C).
    10. Liangzheng Wu & Yan Huang & Yimiao Gu, 2023. "Fragmented or Unified? The State of China’s Carbon Emission Trading Market," Energies, MDPI, vol. 16(5), pages 1-11, March.
    11. Rujie Liu & Wei He & Hongwei Dong & Tao Han & Yuting Yang & Hongwei Yu & Zhu Li, 2024. "Application of Dynamic Weight Mixture Model Based on Dual Sliding Windows in Carbon Price Forecasting," Energies, MDPI, vol. 17(15), pages 1-18, July.
    12. Leme, Marcio Montagnana Vicente & Rocha, Mateus Henrique & Lora, Electo Eduardo Silva & Venturini, Osvaldo José & Lopes, Bruno Marciano & Ferreira, Cláudio Homero, 2014. "Techno-economic analysis and environmental impact assessment of energy recovery from Municipal Solid Waste (MSW) in Brazil," Resources, Conservation & Recycling, Elsevier, vol. 87(C), pages 8-20.
    13. Fan, Xinghua & Lv, Xiangxiang & Yin, Jiuli & Tian, Lixin & Liang, Jiaochen, 2019. "Multifractality and market efficiency of carbon emission trading market: Analysis using the multifractal detrended fluctuation technique," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    14. Zhao, Xin-gang & Wu, Lei & Li, Ang, 2017. "Research on the efficiency of carbon trading market in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1-8.
    15. Tan, Xue-Ping & Wang, Xin-Yu, 2017. "Dependence changes between the carbon price and its fundamentals: A quantile regression approach," Applied Energy, Elsevier, vol. 190(C), pages 306-325.
    16. Bao-jun Tang & Cheng Shen & Yi-fan Zhao, 2015. "Market risk in carbon market: an empirical analysis of the EUA and sCER," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 333-346, February.
    17. Onder Buberkoku, 2017. "Examining Energy Futures Market Efficiency Under Multiple Regime Shifts," International Journal of Energy Economics and Policy, Econjournals, vol. 7(6), pages 61-71.
    18. Yan, Kai & Zhang, Wei & Shen, Dehua, 2020. "Stylized facts of the carbon emission market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).

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    More about this item

    Keywords

    Market Efficiency; Cointegration; Vector Error Correction Model; European carbon futures market;
    All these keywords.

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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