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EU ETS market interactions: The case for multiple hypothesis testing approaches

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  • Cummins, Mark

Abstract

As the European Union Emissions Trading Scheme (EU ETS) enters into the post-2012 Phase III period that runs to 2020, a timely assessment of European Union Allowance (EUA) and Certified Emissions Reduction (CER) price interactions over the Phase II period is presented. Vector autoregression and Granger causality testing is employed for this analysis. As a key contribution to the existing literature, the paper sets out the case for the application of sophisticated multiple hypothesis testing (MHT) procedures to control for the multiple comparisons problem, whereby under naive analysis the identification of significant results from performing multiple simultaneous hypothesis tests may occur by pure chance alone. Two classifications of procedures are employed to control for the multiple comparisons problem in the vector autoregression and Granger causality tests performed: (i) generalised familywise error rate procedures and (ii) false discovery proportion procedures. Over the latter part of Phase II, the generalised familywise error rate procedures show evidence of term structure interactions within both the EUA and CER forward curves. Interactions between the EUA and CER markets are also shown to exist. All of this evidence points to the improved structural reforms to Phase III of the EU ETS that include the extended greenhouse gas coverage, extended sectoral coverage (including aviation), single EU-wide emissions cap, introduction of auctioning and tighter offset limits. However, a word of caution is sounded as the false discovery proportion procedures fail to confirm any such interactions. This comparison of alternative MHT procedures in an empirical study provides important insights for researchers.

Suggested Citation

  • Cummins, Mark, 2013. "EU ETS market interactions: The case for multiple hypothesis testing approaches," Applied Energy, Elsevier, vol. 111(C), pages 701-709.
  • Handle: RePEc:eee:appene:v:111:y:2013:i:c:p:701-709
    DOI: 10.1016/j.apenergy.2013.05.032
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Cummins, Mark & Garry, Oonagh & Kearney, Claire, 2014. "Price discovery analysis of green equity indices using robust asymmetric vector autoregression," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 261-267.
    2. Deeney, Peter & Cummins, Mark & Dowling, Michael & Bermingham, Adam, 2015. "Sentiment in oil markets," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 179-185.
    3. 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.
    4. Li, Wei & Jia, Zhijie, 2016. "The impact of emission trading scheme and the ratio of free quota: A dynamic recursive CGE model in China," Applied Energy, Elsevier, vol. 174(C), pages 1-14.
    5. Xu, Bin & Lin, Boqiang, 2016. "Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model," Applied Energy, Elsevier, vol. 161(C), pages 375-386.
    6. Mark Cummins & Brian M. Lucey & Michael M. Dowling, 2014. "Behavioral Influences in Non-Ferrous Metals Prices," The Institute for International Integration Studies Discussion Paper Series iiisdp459, IIIS.
    7. Dowling, Michael & Cummins, Mark & Lucey, Brian M., 2016. "Psychological barriers in oil futures markets," Energy Economics, Elsevier, vol. 53(C), pages 293-304.

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