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Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?

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  • Ouyang, Zisheng
  • Lu, Min
  • Lai, Yongzeng

Abstract

This paper proposes a new forecast method of stock price return and volatility by GAVMD-Carbon-BiLSTM. First, based on the data from eight carbon markets in China, the logarithmic rate of return of carbon emission rights price is constructed. Second, the TVP-VAR model is used to investigate the impact effect of carbon emission rights trading, stock price return, and volatility. It is used as a predictor of return and volatility rationality. Third, the genetic algorithm is used to optimize the parameters of the variational mode decomposition. Fourth, the return and volatility of stock price are decomposed into multiple intrinsic modes, effectively reducing the data’s complexity. Finally, GAVMD is combined with BiLSTM, and the logarithmic return of carbon emission trading price is used as input to predict the stock price return and volatility. The results show that carbon emissions trading impacts the rate of return and volatility at different lead times and time points. Therefore, using it to predict stock price return and volatility can improve prediction accuracy. At the same time, combining GAVMD with BiLSTM, the prediction performance of carbon emission trading price logarithmic return rate as input is much better than other machine learning models.

Suggested Citation

  • Ouyang, Zisheng & Lu, Min & Lai, Yongzeng, 2023. "Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?," Energy Economics, Elsevier, vol. 128(C).
    • Handle: RePEc:eee:eneeco:v:128:y:2023:i:c:s0140988323006321
    DOI: 10.1016/j.eneco.2023.107134
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    References listed on IDEAS

    as
    1. Hanif, Waqas & Arreola Hernandez, Jose & Mensi, Walid & Kang, Sang Hoon & Uddin, Gazi Salah & Yoon, Seong-Min, 2021. "Nonlinear dependence and connectedness between clean/renewable energy sector equity and European emission allowance prices," Energy Economics, Elsevier, vol. 101(C).
    2. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
    3. 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.
    4. Balcılar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2016. "Risk spillovers across the energy and carbon markets and hedging strategies for carbon risk," Energy Economics, Elsevier, vol. 54(C), pages 159-172.
    5. Xue Gao & Yixin Ren & Muhammad Umar, 2022. "To what extent does COVID-19 drive stock market volatility? A comparison between the U.S. and China," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 1686-1706, December.
    6. Xingrui Jiao & Yuping Song & Yang Kong & Xiaolong Tang, 2022. "Volatility forecasting for crude oil based on text information and deep learning PSO‐LSTM model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 933-944, August.
    7. Jouchi Nakajima, 2011. "Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 29, pages 107-142, November.
    8. Charlotte Christiansen & Maik Schmeling & Andreas Schrimpf, 2012. "A comprehensive look at financial volatility prediction by economic variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 956-977, September.
    9. Ouyang, Zi-sheng & Yang, Xi-te & Lai, Yongzeng, 2021. "Systemic financial risk early warning of financial market in China using Attention-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    10. Xu, Yingying, 2021. "Risk spillover from energy market uncertainties to the Chinese carbon market," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    11. Yang, Cai & Niu, Zibo & Gao, Wang, 2022. "The time-varying effects of trade policy uncertainty and geopolitical risks shocks on the commodity market prices: Evidence from the TVP-VAR-SV approach," Resources Policy, Elsevier, vol. 76(C).
    12. Frank Venmans & Jane Ellis & Daniel Nachtigall, 2020. "Carbon pricing and competitiveness: are they at odds?," Climate Policy, Taylor & Francis Journals, vol. 20(9), pages 1070-1091, October.
    13. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    14. Chang, Kai & Ye, Zhifang & Wang, Weihong, 2019. "Volatility spillover effect and dynamic correlation between regional emissions allowances and fossil energy markets: New evidence from China’s emissions trading scheme pilots," Energy, Elsevier, vol. 185(C), pages 1314-1324.
    15. Dima Alberg & Haim Shalit & Rami Yosef, 2008. "Estimating stock market volatility using asymmetric GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
    16. Awartani, Basel M.A. & Corradi, Valentina, 2005. "Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries," International Journal of Forecasting, Elsevier, vol. 21(1), pages 167-183.
    17. Eachempati, Prajwal & Srivastava, Praveen Ranjan & Kumar, Ajay & Tan, Kim Hua & Gupta, Shivam, 2021. "Validating the impact of accounting disclosures on stock market: A deep neural network approach," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    18. Ouyang, Zisheng & Zhou, Xuewei, 2023. "Multilayer networks in the frequency domain: Measuring extreme risk connectedness of Chinese financial institutions," Research in International Business and Finance, Elsevier, vol. 65(C).
    19. Fang, Yi & Jing, Zhongbo & Shi, Yukun & Zhao, Yang, 2021. "Financial spillovers and spillbacks: New evidence from China and G7 countries," Economic Modelling, Elsevier, vol. 94(C), pages 184-200.
    20. Li, Yuze & Jiang, Shangrong & Li, Xuerong & Wang, Shouyang, 2021. "The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach," Energy Economics, Elsevier, vol. 95(C).
    21. Bolin Lei & Boyu Zhang & Yuping Song, 2021. "Volatility Forecasting for High-Frequency Financial Data Based on Web Search Index and Deep Learning Model," Mathematics, MDPI, vol. 9(4), pages 1-17, February.
    22. Sun, Xiaotian & Fang, Wei & Gao, Xiangyun & An, Haizhong & Liu, Siyao & Wu, Tao, 2022. "Complex causalities between the carbon market and the stock markets for energy intensive industries in China," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 404-417.
    23. Klein, Tony & Walther, Thomas, 2016. "Oil price volatility forecast with mixture memory GARCH," Energy Economics, Elsevier, vol. 58(C), pages 46-58.
    24. Sharma, Prateek & Vipul,, 2016. "Forecasting stock market volatility using Realized GARCH model: International evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 59(C), pages 222-230.
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