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Forecasting the Volatility of European Union Allowance Futures with Climate Policy Uncertainty Using the EGARCH-MIDAS Model

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  • Xinyu Wu

    (School of Finance, Anhui University of Finance and Economics, Bengbu 233030, China)

  • Xuebao Yin

    (School of Finance, Anhui University of Finance and Economics, Bengbu 233030, China)

  • Xueting Mei

    (School of Finance, Anhui University of Finance and Economics, Bengbu 233030, China)

Abstract

We propose the EGARCH-MIDAS-CPU model, which incorporates the leverage effect and climate policy uncertainty (CPU) to model and forecast European Union allowance futures’ (EUAF) volatility. An empirical analysis based on the daily data of the EUAF price index and the monthly data of the CPU index using the EGARCH-MIDAS-CPU model shows that the EUAF’s volatility exhibits a leverage effect, and the CPU has a significantly negative impact on the EUAF’s volatility. Furthermore, out-of-sample analysis based on three loss functions and the Model Confidence Set (MCS) test suggests that EGARCH-MIDAS-CPU model yields more accurate out-of-sample volatility forecasting results than various competing models. There is room for further application of the model, such as this model could be applied to price carbon futures, so as to improve the liquidity of the carbon market and achieve carbon peak and carbon neutrality as soon as possible.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4306-:d:787154
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    References listed on IDEAS

    as
    1. repec:dau:papers:123456789/6969 is not listed on IDEAS
    2. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    3. 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.
    4. 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).
    5. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou, 2016. "Macro-Finance Determinants of the Long-Run Stock–Bond Correlation: The DCC-MIDAS Specification," Journal of Financial Econometrics, Oxford University Press, vol. 14(3), pages 617-642.
    6. Libing Fang & Baizhu Chen & Honghai Yu & Yichuo Qian, 2018. "The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH‐MIDAS approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 413-422, March.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Yu, Honghai & Fang, Libing & Sun, Wencong, 2018. "Forecasting performance of global economic policy uncertainty for volatility of Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 931-940.
    9. Yinpeng Zhang & Zhixin Liu & Yingying Xu, 2018. "Carbon price volatility: The case of China," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-15, October.
    10. Conrad, Christian & Rittler, Daniel & Rotfuß, Waldemar, 2012. "Modeling and explaining the dynamics of European Union Allowance prices at high-frequency," Energy Economics, Elsevier, vol. 34(1), pages 316-326.
    11. Frederick Ploeg & Aart Zeeuw, 2019. "Pricing Carbon and Adjusting Capital to Fend Off Climate Catastrophes," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(1), pages 29-50, January.
    12. Chevallier, Julien, 2011. "A model of carbon price interactions with macroeconomic and energy dynamics," Energy Economics, Elsevier, vol. 33(6), pages 1295-1312.
    13. Pan, Zhiyuan & Wang, Yudong & Wu, Chongfeng & Yin, Libo, 2017. "Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 130-142.
    14. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    15. repec:dau:papers:123456789/4210 is not listed on IDEAS
    16. 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.
    17. 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.
    18. Golub, Alexander A. & Lubowski, Ruben N. & Piris-Cabezas, Pedro, 2020. "Business responses to climate policy uncertainty: Theoretical analysis of a twin deferral strategy and the risk-adjusted price of carbon," Energy, Elsevier, vol. 205(C).
    19. Rodriguez Lopez, Juan Miguel & Sakhel, Alice & Busch, Timo, 2017. "Corporate investments and environmental regulation: The role of regulatory uncertainty, regulation-induced uncertainty, and investment history," European Management Journal, Elsevier, vol. 35(1), pages 91-101.
    20. Gugler, Klaus & Haxhimusa, Adhurim & Liebensteiner, Mario, 2021. "Effectiveness of climate policies: Carbon pricing vs. subsidizing renewables," Journal of Environmental Economics and Management, Elsevier, vol. 106(C).
    21. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    22. Zhou, Zhongbao & Fu, Zhangyan & Jiang, Yong & Zeng, Ximei & Lin, Ling, 2020. "Can economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 34(C).
    23. Jian Liu & Ziting Zhang & Lizhao Yan & Fenghua Wen, 2021. "Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH-MIDAS model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    24. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    25. Batten, Jonathan A. & Maddox, Grace E. & Young, Martin R., 2021. "Does weather, or energy prices, affect carbon prices?," Energy Economics, Elsevier, vol. 96(C).
    26. Hambel, Christoph & Kraft, Holger & Schwartz, Eduardo, 2021. "Optimal carbon abatement in a stochastic equilibrium model with climate change," European Economic Review, Elsevier, vol. 132(C).
    27. Peng-Fei Dai & Xiong Xiong & Toan Luu Duc Huynh & Jiqiang Wang, 2020. "The impact of economic policy uncertainties on the volatility of European carbon market," Papers 2007.10564, arXiv.org, revised Aug 2021.
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    6. 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).

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