IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v54y2023ics1544612323000946.html
   My bibliography  Save this article

EU Climate Change News Index: Forecasting EU ETS prices with online news

Author

Listed:
  • Hartvig, Áron Dénes
  • Pap, Áron
  • Pálos, Péter

Abstract

Carbon prices have been rapidly increasing in the EU since 2018 and accurate forecasting of EU Emissions Trading System (ETS) prices has become essential. This paper proposes a novel method to generate alternative predictors for daily ETS price returns using relevant online news information. We devise the EU Climate Change News Index by calculating the term frequency–inverse document frequency (TF–IDF) feature for climate change-related keywords. The index is capable of tracking the ongoing debate about climate change in the EU. Finally, we show that incorporating the index in a simple predictive model significantly improves forecasts of ETS price returns.

Suggested Citation

  • Hartvig, Áron Dénes & Pap, Áron & Pálos, Péter, 2023. "EU Climate Change News Index: Forecasting EU ETS prices with online news," Finance Research Letters, Elsevier, vol. 54(C).
    • Handle: RePEc:eee:finlet:v:54:y:2023:i:c:s1544612323000946
    DOI: 10.1016/j.frl.2023.103720
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612323000946
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2023.103720?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. Guidolin, Massimo & Pedio, Manuela, 2021. "Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit," Finance Research Letters, Elsevier, vol. 42(C).
    2. Arouri, Mohamed El Hédi & Jawadi, Fredj & Nguyen, Duc Khuong, 2012. "Nonlinearities in carbon spot-futures price relationships during Phase II of the EU ETS," Economic Modelling, Elsevier, vol. 29(3), pages 884-892.
    3. 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.
    4. 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.
    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. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    7. Corina Haita-Falah, 2016. "Uncertainty and speculators in an auction for emissions permits," Journal of Regulatory Economics, Springer, vol. 49(3), pages 315-343, June.
    8. Ye, Jing & Xue, Minggao, 2021. "Influences of sentiment from news articles on EU carbon prices," Energy Economics, Elsevier, vol. 101(C).
    9. Zhang, Fang & Xia, Yan, 2022. "Carbon price prediction models based on online news information analytics," Finance Research Letters, Elsevier, vol. 46(PA).
    10. Pesaran, M Hashem & Timmermann, Allan, 1995. "Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
    11. Massimo Guidolin & Alexei G. Orlov & Manuela Pedio, 2018. "How good can heuristic-based forecasts be? A comparative performance of econometric and heuristic models for UK and US asset returns," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 139-169, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).

    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. 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).
    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. Peng Ye & Yong Li & Abu Bakkar Siddik, 2023. "Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm," Energies, MDPI, vol. 16(11), pages 1-39, June.
    4. 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.
    5. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2019. "Forecasting stock returns with cycle-decomposed predictors," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 250-261.
    6. Adekoya, Oluwasegun B. & Oliyide, Johnson A. & Noman, Ambreen, 2021. "The volatility connectedness of the EU carbon market with commodity and financial markets in time- and frequency-domain: The role of the U.S. economic policy uncertainty," Resources Policy, Elsevier, vol. 74(C).
    7. Afees A. Salisu & Abdulsalam Abidemi Sikiru & Philip C. Omoke, 2023. "COVID-19 pandemic and financial innovations," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3885-3904, August.
    8. Monticini, Andrea & Ravazzolo, Francesco, 2014. "Forecasting the intraday market price of money," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 304-315.
    9. Vivian, Andrew & Wohar, Mark E., 2013. "The output gap and stock returns: Do cyclical fluctuations predict portfolio returns?," International Review of Financial Analysis, Elsevier, vol. 26(C), pages 40-50.
    10. Odendahl, Florens & Rossi, Barbara & Sekhposyan, Tatevik, 2023. "Evaluating forecast performance with state dependence," Journal of Econometrics, Elsevier, vol. 237(2).
    11. Jiang, Wei & Chen, Yunfei, 2022. "The time-frequency connectedness among carbon, traditional/new energy and material markets of China in pre- and post-COVID-19 outbreak periods," Energy, Elsevier, vol. 246(C).
    12. Chen, Nan-Kuang & Chen, Shiu-Sheng & Chou, Yu-Hsi, 2017. "Further evidence on bear market predictability: The role of the external finance premium," International Review of Economics & Finance, Elsevier, vol. 50(C), pages 106-121.
    13. Straetmans, S.T.M. & Candelon, B. & Ahmed, J., 2012. "Predicting and capitalizing on stock market bears in the U.S," Research Memorandum 019, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    14. Qingjie Zhou & Panpan Zhu & Yinpeng Zhang, 2023. "Contagion Spillover from Bitcoin to Carbon Futures Pricing: Perspective from Investor Attention," Energies, MDPI, vol. 16(2), pages 1-22, January.
    15. Hongwei Zhang & Qiang He & Ben Jacobsen & Fuwei Jiang, 2020. "Forecasting stock returns with model uncertainty and parameter instability," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 629-644, August.
    16. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2014. "Forecasting stock returns under economic constraints," Journal of Financial Economics, Elsevier, vol. 114(3), pages 517-553.
    17. Wang, Minggang & Zhu, Mengrui & Tian, Lixin, 2022. "A novel framework for carbon price forecasting with uncertainties," Energy Economics, Elsevier, vol. 112(C).
    18. Xinyu Wu & Xuebao Yin & Xueting Mei, 2022. "Forecasting the Volatility of European Union Allowance Futures with Climate Policy Uncertainty Using the EGARCH-MIDAS Model," Sustainability, MDPI, vol. 14(7), pages 1-13, April.
    19. Gonzalo, Jesús & Pitarakis, Jean-Yves, 2020. "Out of sample predictability in predictive regressions with many predictor candidates," UC3M Working papers. Economics 31554, Universidad Carlos III de Madrid. Departamento de Economía.
    20. Wen, Chufu & Zhu, Haoyang & Dai, Zhifeng, 2023. "Forecasting commodity prices returns: The role of partial least squares approach," Energy Economics, Elsevier, vol. 125(C).

    More about this item

    Keywords

    Emissions trading system; Carbon price prediction; Online news; TF–IDF; Climate change; Market index;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

    Statistics

    Access and download statistics

    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:finlet:v:54:y:2023:i:c:s1544612323000946. 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/locate/frl .

    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.