IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v216y2018icp132-141.html
   My bibliography  Save this article

Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS

Author

Listed:
  • Zhao, Xin
  • Han, Meng
  • Ding, Lili
  • Kang, Wanglin

Abstract

This paper presents a real-time forecasting procedure that utilizes multiple factors with different sampling frequencies to predict the weekly carbon price. Novel combination-MIDAS models with five weight-type schemes are proposed for evaluating the forecast accuracy. The evidence shows that combination-MIDAS models provide forecasting performance gains over traditional models, which supports the use of mixed-frequency data that consist of economic and energy indicators to forecast the weekly carbon price. It is also shown that, Coal is the best predictor for carbon price forecasting and that forecasts that are based on Crude have similar trends to actual carbon prices but are higher than the actual prices.

Suggested Citation

  • Zhao, Xin & Han, Meng & Ding, Lili & Kang, Wanglin, 2018. "Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS," Applied Energy, Elsevier, vol. 216(C), pages 132-141.
    • Handle: RePEc:eee:appene:v:216:y:2018:i:c:p:132-141
    DOI: 10.1016/j.apenergy.2018.02.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918301223
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.02.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Benz, Eva & Trück, Stefan, 2009. "Modeling the price dynamics of CO2 emission allowances," Energy Economics, Elsevier, vol. 31(1), pages 4-15, January.
    2. Fang, Guochang & Tian, Lixin & Liu, Menghe & Fu, Min & Sun, Mei, 2018. "How to optimize the development of carbon trading in China—Enlightenment from evolution rules of the EU carbon price," Applied Energy, Elsevier, vol. 211(C), pages 1039-1049.
    3. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    4. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    5. Thijs Benschopa & Brenda López Cabrera, 2014. "Volatility Modelling of CO2 Emission Allowance Spot Prices with Regime-Switching GARCH Models," SFB 649 Discussion Papers SFB649DP2014-050, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    7. Aatola, Piia & Ollikainen, Markku & Toppinen, Anne, 2013. "Price determination in the EU ETS market: Theory and econometric analysis with market fundamentals," Energy Economics, Elsevier, vol. 36(C), pages 380-395.
    8. James H. Stock & Mark W. Watson, 2012. "Generalized Shrinkage Methods for Forecasting Using Many Predictors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 481-493, June.
    9. Hintermann, Beat, 2010. "Allowance price drivers in the first phase of the EU ETS," Journal of Environmental Economics and Management, Elsevier, vol. 59(1), pages 43-56, January.
    10. Chevallier, Julien, 2009. "Carbon futures and macroeconomic risk factors: A view from the EU ETS," Energy Economics, Elsevier, vol. 31(4), pages 614-625, July.
    11. Creti, Anna & Jouvet, Pierre-André & Mignon, Valérie, 2012. "Carbon price drivers: Phase I versus Phase II equilibrium?," Energy Economics, Elsevier, vol. 34(1), pages 327-334.
    12. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    13. Chevallier, Julien, 2011. "A model of carbon price interactions with macroeconomic and energy dynamics," Energy Economics, Elsevier, vol. 33(6), pages 1295-1312.
    14. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    15. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2013. "Pooling Versus Model Selection For Nowcasting Gdp With Many Predictors: Empirical Evidence For Six Industrialized Countries," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 392-411, April.
    16. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    17. Bangzhu Zhu, 2012. "A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network," Energies, MDPI, vol. 5(2), pages 1-16, February.
    18. 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.
    19. Jennie Bai & Eric Ghysels & Jonathan H. Wright, 2013. "State Space Models and MIDAS Regressions," Econometric Reviews, Taylor & Francis Journals, vol. 32(7), pages 779-813, October.
    20. repec:dau:papers:123456789/6969 is not listed on IDEAS
    21. repec:dau:papers:123456789/4222 is not listed on IDEAS
    22. Andrade, Philippe & Fourel, Valère & Ghysels, Eric & Idier, Julien, 2014. "The financial content of inflation risks in the euro area," International Journal of Forecasting, Elsevier, vol. 30(3), pages 648-659.
    23. Hammoudeh, Shawkat & Nguyen, Duc Khuong & Sousa, Ricardo M., 2014. "What explain the short-term dynamics of the prices of CO2 emissions?," Energy Economics, Elsevier, vol. 46(C), pages 122-135.
    24. Zhu, Bangzhu & Wei, Yiming, 2013. "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology," Omega, Elsevier, vol. 41(3), pages 517-524.
    25. repec:dau:papers:123456789/4598 is not listed on IDEAS
    26. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    27. repec:dau:papers:123456789/4210 is not listed on IDEAS
    28. 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.
    29. Turhan, Ibrahim M. & Sensoy, Ahmet & Hacihasanoglu, Erk, 2015. "Shaping the manufacturing industry performance: MIDAS approach," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 286-290.
    30. Clements, Michael P & Galvão, Ana Beatriz, 2008. "Macroeconomic Forecasting With Mixed-Frequency Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 546-554.
    31. Oberndorfer, Ulrich, 2009. "EU Emission Allowances and the stock market: Evidence from the electricity industry," Ecological Economics, Elsevier, vol. 68(4), pages 1116-1126, February.
    32. Chunpeng Yang & Rengui Zhang, 2014. "Does mixed-frequency investor sentiment impact stock returns? Based on the empirical study of MIDAS regression model," Applied Economics, Taylor & Francis Journals, vol. 46(9), pages 966-972, March.
    33. repec:dau:papers:123456789/4349 is not listed on IDEAS
    34. Alberola, Emilie & Chevallier, Julien & Cheze, Benoi^t, 2008. "Price drivers and structural breaks in European carbon prices 2005-2007," Energy Policy, Elsevier, vol. 36(2), pages 787-797, February.
    35. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    36. repec:ipg:wpaper:2014-081 is not listed on IDEAS
    37. 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.
    38. Julien Chevallier, 2010. "Volatility forecasting of carbon prices using factor models," Economics Bulletin, AccessEcon, vol. 30(2), pages 1642-1660.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Han, Meng & Ding, Lili & Zhao, Xin & Kang, Wanglin, 2019. "Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors," Energy, Elsevier, vol. 171(C), pages 69-76.
    2. 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.
    3. Duan, Kun & Ren, Xiaohang & Shi, Yukun & Mishra, Tapas & Yan, Cheng, 2021. "The marginal impacts of energy prices on carbon price variations: Evidence from a quantile-on-quantile approach," Energy Economics, Elsevier, vol. 95(C).
    4. Fang, Sheng & Lu, Xinsheng & Li, Jianfeng & Qu, Ling, 2018. "Multifractal detrended cross-correlation analysis of carbon emission allowance and stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 551-566.
    5. 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).
    6. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    7. Huang, Wenyang & Wang, Huiwen & Qin, Haotong & Wei, Yigang & Chevallier, Julien, 2022. "Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method," Energy Economics, Elsevier, vol. 110(C).
    8. 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.
    9. Zhu, Bangzhu & Ye, Shunxin & Han, Dong & Wang, Ping & He, Kaijian & Wei, Yi-Ming & Xie, Rui, 2019. "A multiscale analysis for carbon price drivers," Energy Economics, Elsevier, vol. 78(C), pages 202-216.
    10. Qian Chen & Xiang Gao & Shan Xie & Li Sun & Shuairu Tian & Shigeyuki Hamori, 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading," Energies, MDPI, vol. 14(5), pages 1-24, February.
    11. 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.
    12. Lovcha, Yuliya & Perez-Laborda, Alejandro & Sikora, Iryna, 2022. "The determinants of CO2 prices in the EU emission trading system," Applied Energy, Elsevier, vol. 305(C).
    13. Lutz, Benjamin Johannes & Pigorsch, Uta & Rotfuß, Waldemar, 2013. "Nonlinearity in cap-and-trade systems: The EUA price and its fundamentals," Energy Economics, Elsevier, vol. 40(C), pages 222-232.
    14. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    15. Fang Zhang & Zhengjun Zhang, 2020. "The tail dependence of the carbon markets: The implication of portfolio management," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-17, August.
    16. Lovcha, Yuliya & Pérez Laborda, Àlex & Sikora, Iryna, 2019. "The Determinants of CO2 prices in the EU ETS System," Working Papers 2072/376031, Universitat Rovira i Virgili, Department of Economics.
    17. Hintermann, Beat & Peterson, Sonja & Rickels, Wilfried, 2014. "Price and market behavior in Phase II of the EU ETS," Kiel Working Papers 1962, Kiel Institute for the World Economy (IfW Kiel).
    18. Demiralay, Sercan & Gencer, Hatice Gaye & Bayraci, Selcuk, 2022. "Carbon credit futures as an emerging asset: Hedging, diversification and downside risks," Energy Economics, Elsevier, vol. 113(C).
    19. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2020. "How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics," Energy Economics, Elsevier, vol. 90(C).
    20. Xu, Jia & Tan, Xiujie & He, Gang & Liu, Yu, 2019. "Disentangling the drivers of carbon prices in China's ETS pilots — An EEMD approach," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 1-9.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:216:y:2018:i:c:p:132-141. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.