On the realized volatility of the ECX CO2 emissions 2008 futures contract: distribution, dynamics and forecasting

Abstract

This article documents the conditional and unconditional distributions of the realized volatility for the 2008 futures contract in the European climate exchange (ECX), which is valid under the EU emissions trading scheme (EU ETS). 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-normals hypothesis is strongly rejected, as the returns standardized using daily measures of volatility clearly departs from normality. A simplified HAR-RV model (Corsi in J Financ Econ 7:174–196, 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.

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Correspondence to Julien Chevallier or Benoît Sévi.

Additional information

Helpful comments were received from seminar participants at the 2009 London School of Economics “Carbon Markets” Workshop, the IEW 2009 in Venice, and the EEA-ESEM 2009 in Barcelona. Last but not least, we wish to thank Derek Bunn for insightful comments on previous drafts and Kevin Sheppard for having made available to us the Matlab Oxford Realized Volatility Toolbox. Financial support from the Conseil Français de l’Energie under the grant #CFE-61 is gratefully acknowledged.

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Chevallier, J., Sévi, B. On the realized volatility of the ECX CO2 emissions 2008 futures contract: distribution, dynamics and forecasting. Ann Finance 7, 1–29 (2011). https://doi.org/10.1007/s10436-009-0142-x

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Keywords

  • CO2 price
  • Realized volatility
  • HAR-RV
  • Emissions markets
  • EU ETS
  • Intraday data
  • Forecasting

JEL Classification

  • C5
  • G1
  • Q4