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On the Realized Volatility of the ECX CO2 Emissions 2008 Futures Contract: Distribution, Dynamics and Forecasting

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  • Chevallier, Julien
  • Benoit, Sevi

Abstract

The recent implementation of the EU Emissions Trading Scheme (EU ETS) in January 2005 created new financial risks for emitting firms. To deal with these risks, options are traded since October 2006. Because the EU ETS is a new market, the relevant underlying model for option pricing is still a controversial issue. This article improves our understanding of this issue by characterizing the conditional and unconditional distributions of the realized volatility for the 2008 futures contract in the European Climate Exchange (ECX), which is valid during Phase II (2008-2012) of the EU ETS. The realized volatility measures from naive, kernel-based and subsampling estimators are used to obtain inferences about the distributional and dynamic properties of the ECX emissions futures volatility. The distribution of the daily realized volatility in logarithmic form is shown to be close to normal. The mixture-of-distributions hypothesis is strongly rejected, as the returns standardized using daily measures of volatility clearly departs from normality. A simplified HAR-RV model (Corsi, 2009) with only a weekly component, which reproduces long memory properties of the series, is then used to model the volatility dynamics. Finally, the predictive accuracy of the HAR-RV model is tested against GARCH specifications using one-step-ahead forecasts, which confirms the HAR-RV superior ability. Our conclusions indicate that (i) the standard Brownian motion is not an adequate tool for option pricing in the EU ETS, and (ii) a jump component should be included in the stochastic process to price options, thus providing more efficient tools for risk-management activities.

Suggested Citation

  • Chevallier, Julien & Benoit, Sevi, 2009. "On the Realized Volatility of the ECX CO2 Emissions 2008 Futures Contract: Distribution, Dynamics and Forecasting," Sustainable Development Papers 55834, Fondazione Eni Enrico Mattei (FEEM).
    • Handle: RePEc:ags:feemdp:55834
    DOI: 10.22004/ag.econ.55834
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    Cited by:

    1. Oscar Carchano & Vicente Medina Martínez & Ángel Pardo Tornero, 2012. "Rolling over EUAs and CERs," Working Papers. Serie AD 2012-15, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    2. Reckling, Dennis, 2016. "Variance risk premia in CO2 markets: A political perspective," Energy Policy, Elsevier, vol. 94(C), pages 345-354.
    3. Guo, Kun & Liu, Yu & Cao, Shanwei & Zhai, Xiangyang & Ji, Qiang, 2025. "Can climate factors improve the forecasting of electricity price volatility? Evidence from Australia," Energy, Elsevier, vol. 315(C).
    4. Chevallier, Julien, 2011. "A model of carbon price interactions with macroeconomic and energy dynamics," Energy Economics, Elsevier, vol. 33(6), pages 1295-1312.
    5. Julien Chevallier & Benoît Sévi, 2014. "On the Stochastic Properties of Carbon Futures Prices," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 58(1), pages 127-153, May.
    6. Rittler, Daniel, 2009. "Price Discovery, Causality and Volatility Spillovers in European Union Allowances Phase II: A High Frequency Analysis," Working Papers 0492, University of Heidelberg, Department of Economics.
    7. Julien Chevallier, 2010. "Modelling the convenience yield in carbon prices using daily and realized measures," Working Papers halshs-00463921, HAL.
    8. Shenghua Xiong & Chunfeng Wang & Zhenming Fang & Dan Ma, 2019. "Multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast Multi-Output Relevance Vector Regression Optimized by the Multi-Objective Whale Optimization Algorithm," Energies, MDPI, vol. 12(1), pages 1-21, January.
    9. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    10. Song, Yazhi & Liu, Tiansen & Liang, Dapeng & Li, Yin & Song, Xiaoqiu, 2019. "A Fuzzy Stochastic Model for Carbon Price Prediction Under the Effect of Demand-related Policy in China's Carbon Market," Ecological Economics, Elsevier, vol. 157(C), pages 253-265.
    11. Yue-Jun Zhang, 2016. "Research on carbon emission trading mechanisms: current status and future possibilities," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 39(1/2), pages 89-107.
    12. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2017. "Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 692-704.
    13. Didit Budi Nugroho & Takayuki Morimoto, 2019. "Incorporating Realized Quarticity into a Realized Stochastic Volatility Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(4), pages 495-528, December.
    14. Todorova, Neda, 2015. "The course of realized volatility in the LME non-ferrous metal market," Economic Modelling, Elsevier, vol. 51(C), pages 1-12.
    15. Wen, Fenghua & Gong, Xu & Cai, Shenghua, 2016. "Forecasting the volatility of crude oil futures using HAR-type models with structural breaks," Energy Economics, Elsevier, vol. 59(C), pages 400-413.
    16. Chevallier, Julien, 2013. "Variance risk-premia in CO2 markets," Economic Modelling, Elsevier, vol. 31(C), pages 598-605.
    17. Chevallier, Julien, 2011. "Nonparametric modeling of carbon prices," Energy Economics, Elsevier, vol. 33(6), pages 1267-1282.
    18. Chevallier, Julien, 2011. "Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models," Economic Modelling, Elsevier, vol. 28(6), pages 2634-2656.
    19. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2022. "Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals," International Journal of Forecasting, Elsevier, vol. 38(3), pages 944-969.
    20. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
    21. Rittler, Daniel, 2012. "Price discovery and volatility spillovers in the European Union emissions trading scheme: A high-frequency analysis," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 774-785.
    22. Viteva, Svetlana & Veld-Merkoulova, Yulia V. & Campbell, Kevin, 2014. "The forecasting accuracy of implied volatility from ECX carbon options," Energy Economics, Elsevier, vol. 45(C), pages 475-484.
    23. Rannou, Yves, 2017. "Liquidity, information, strategic trading in an electronic order book: New insights from the European carbon markets," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 779-808.
    24. Bredin, Don & Hyde, Stuart & Muckley, Cal, 2014. "A microstructure analysis of the carbon finance market," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 222-234.

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    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G1 - Financial Economics - - General Financial Markets
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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