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Predicting carbon market risk using information from macroeconomic fundamentals

Author

Listed:
  • Jiao, Lei
  • Liao, Yin
  • Zhou, Qing

Abstract

Economic theories suggest that carbon price movements are closely related to economic fundamentals. This paper develops an economic state-dependent (SD) approach to evaluate carbon market Value-at-Risk (VaR) that incorporates information from macroeconomic fundamentals into carbon return VaR modeling and forecasting. This method implements an economic SD sampling scheme that utilizes historical carbon return observations from the relevant economic states to predict future carbon market VaR. Applying this SD method to the European Union (EU) carbon market, we confirm that the EU fundamental economy has two distinct states that correspond to “expansion” and “recession” periods and that the carbon returns have different distributions in the two states. We find that the SD method outperforms the traditional non-SD methods in out-of-sample VaR forecasts, and this is particularly evident when the carbon market experiences large-scale economy-driven structural breaks.

Suggested Citation

  • Jiao, Lei & Liao, Yin & Zhou, Qing, 2018. "Predicting carbon market risk using information from macroeconomic fundamentals," Energy Economics, Elsevier, vol. 73(C), pages 212-227.
    • Handle: RePEc:eee:eneeco:v:73:y:2018:i:c:p:212-227
    DOI: 10.1016/j.eneco.2018.05.008
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    Citations

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    Cited by:

    1. Yu, Xing & Shen, Xilin & Li, Yanyan & Gong, Xue, 2023. "Selective hedging strategies for crude oil futures based on market state expectations," Global Finance Journal, Elsevier, vol. 57(C).
    2. Joao Leitao & Joaquim Ferreira & Ernesto Santibanez‐Gonzalez, 2021. "Green bonds, sustainable development and environmental policy in the European Union carbon market," Business Strategy and the Environment, Wiley Blackwell, vol. 30(4), pages 2077-2090, May.
    3. Jang, Minchul & Yoon, Soeun & Jung, Seoyoung & Min, Baehyun, 2024. "Simulating and assessing carbon markets: Application to the Korean and the EU ETSs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).
    4. Liu, Tao & Guan, Xinyue & Wei, Yigang & Xue, Shan & Xu, Liang, 2023. "Impact of economic policy uncertainty on the volatility of China's emission trading scheme pilots," Energy Economics, Elsevier, vol. 121(C).
    5. Ye, Jing & Xue, Minggao, 2021. "Influences of sentiment from news articles on EU carbon prices," Energy Economics, Elsevier, vol. 101(C).
    6. Xianzi Yang & Chen Zhang & Yu Yang & Wenjun Wang & Zulfiqar Ali Wagan, 2022. "A new risk measurement method for China's carbon market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1280-1290, January.
    7. Wang, Xiong & Li, Jingyao & Ren, Xiaohang & Bu, Ruijun & Jawadi, Fredj, 2023. "Economic policy uncertainty and dynamic correlations in energy markets: Assessment and solutions," Energy Economics, Elsevier, vol. 117(C).
    8. Friedrich, Marina & Mauer, Eva-Maria & Pahle, Michael & Tietjen, Oliver, 2020. "From fundamentals to financial assets: the evolution of understanding price formation in the EU ETS," EconStor Preprints 196150, ZBW - Leibniz Information Centre for Economics, revised 2020.
    9. Peng Chen & Andrew Vivian & Cheng Ye, 2022. "Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine," Annals of Operations Research, Springer, vol. 313(1), pages 559-601, June.
    10. Jonek-Kowalska, Izabela, 2019. "Efficiency of Enterprise Risk Management (ERM) systems. Comparative analysis in the fuel sector and energy sector on the basis of Central-European companies listed on the Warsaw Stock Exchange," Resources Policy, Elsevier, vol. 62(C), pages 405-415.
    11. Chen, Weidong & Xiong, Shi & Chen, Quanyu, 2022. "Characterizing the dynamic evolutionary behavior of multivariate price movement fluctuation in the carbon-fuel energy markets system from complex network perspective," Energy, Elsevier, vol. 239(PA).
    12. Yang, Lu, 2022. "Idiosyncratic information spillover and connectedness network between the electricity and carbon markets in Europe," Journal of Commodity Markets, Elsevier, vol. 25(C).
    13. Chen, Huayi & Shi, Huai-Long & Zhou, Wei-Xing, 2024. "Carbon volatility connectedness and the role of external uncertainties: Evidence from China," Journal of Commodity Markets, Elsevier, vol. 33(C).
    14. Xu, Yingying, 2021. "Risk spillover from energy market uncertainties to the Chinese carbon market," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    15. Hong Qiu & Genhua Hu & Yuhong Yang & Jeffrey Zhang & Ting Zhang, 2020. "Modeling the Risk of Extreme Value Dependence in Chinese Regional Carbon Emission Markets," Sustainability, MDPI, vol. 12(19), pages 1-15, September.
    16. Zhu, Bangzhu & Wan, Chunzhuo & Wang, Ping, 2022. "Interval forecasting of carbon price: A novel multiscale ensemble forecasting approach," Energy Economics, Elsevier, vol. 115(C).
    17. Chai, Shanglei & Zhou, P., 2018. "The Minimum-CVaR strategy with semi-parametric estimation in carbon market hedging problems," Energy Economics, Elsevier, vol. 76(C), pages 64-75.

    More about this item

    Keywords

    Carbon price; Value at risk; Business cycle; Macroeconomy; State-dependence;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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