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Contagion Spillover from Bitcoin to Carbon Futures Pricing: Perspective from Investor Attention

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  • Qingjie Zhou

    (School of Economics, Beijing Technology and Business University, Beijing 100048, China
    Institute of New Commercial Economy, Beijing Technology and Business University, Beijing 100048, China)

  • Panpan Zhu

    (School of Economics, Beijing Technology and Business University, Beijing 100048, China)

  • Yinpeng Zhang

    (Business School, Wuchang University of Technology, Wuhan 430223, China)

Abstract

The uniqueness of this investigation lies in empirically testing and proving the contagion spillover of Bitcoin attention to carbon futures. Specifically, several models are adopted to investigate the explanatory and predictive abilities of Bitcoin attention to carbon futures. The results can be generalized as follows. First, Bitcoin attention Granger causes the variation of carbon futures. Second, Bitcoin attention shows a negative impact on carbon futures and an addition, an invert U-shaped connection exists. Third, the Bitcoin attention-based models can beat the commonly used historical average benchmark during out-of-sample forecasting both in statistical and economic levels. Fourth, we complete robustness checks to certify that the contagion spillover from Bitcoin attention to the pricing of carbon futures does exist. Finally, we prove the linear and non-linear impacts from Bitcoin attention to realized volatility of carbon futures. All the results prove that Bitcoin attention is an important pricing factor for carbon futures market.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:929-:d:1035420
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    References listed on IDEAS

    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Ding, Qian & Huang, Jianbai & Zhang, Hongwei, 2022. "Time-frequency spillovers among carbon, fossil energy and clean energy markets: The effects of attention to climate change," International Review of Financial Analysis, Elsevier, vol. 83(C).
    3. Huang, Yingying & Duan, Kun & Urquhart, Andrew, 2023. "Time-varying dependence between Bitcoin and green financial assets: A comparison between pre- and post-COVID-19 periods," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    4. Yao, Ting & Zhang, Yue-Jun & Ma, Chao-Qun, 2017. "How does investor attention affect international crude oil prices?," Applied Energy, Elsevier, vol. 205(C), pages 336-344.
    5. Adra, Samer & Barbopoulos, Leonidas G., 2018. "The valuation effects of investor attention in stock-financed acquisitions," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 108-125.
    6. Benz, Eva & Trück, Stefan, 2009. "Modeling the price dynamics of CO2 emission allowances," Energy Economics, Elsevier, vol. 31(1), pages 4-15, January.
    7. Feng, Zhen-Hua & Zou, Le-Le & Wei, Yi-Ming, 2011. "Carbon price volatility: Evidence from EU ETS," Applied Energy, Elsevier, vol. 88(3), pages 590-598, March.
    8. Ferreira, Miguel A. & Santa-Clara, Pedro, 2011. "Forecasting stock market returns: The sum of the parts is more than the whole," Journal of Financial Economics, Elsevier, vol. 100(3), pages 514-537, June.
    9. Teixidó, Jordi & Verde, Stefano F. & Nicolli, Francesco, 2019. "The impact of the EU Emissions Trading System on low-carbon technological change: The empirical evidence," Ecological Economics, Elsevier, vol. 164(C), pages 1-1.
    10. 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.
    11. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.
    12. 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.
    13. Ciner Cetin, 2001. "Energy Shocks and Financial Markets: Nonlinear Linkages," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 5(3), pages 1-11, October.
    14. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2019. "Does twitter predict Bitcoin?," Economics Letters, Elsevier, vol. 174(C), pages 118-122.
    15. Wu, You & Han, Liyan & Yin, Libo, 2019. "Our currency, your attention: Contagion spillovers of investor attention on currency returns," Economic Modelling, Elsevier, vol. 80(C), pages 49-61.
    16. Xianfang Su & Wenqiang Zhan & Yong Li, 2021. "Quantile dependence between investor attention and cryptocurrency returns: evidence from time and frequency domain analyses," Applied Economics, Taylor & Francis Journals, vol. 53(55), pages 6439-6471, November.
    17. Kou, Yi & Ye, Qiang & Zhao, Feng & Wang, Xiaolin, 2018. "Effects of investor attention on commodity futures markets," Finance Research Letters, Elsevier, vol. 25(C), pages 190-195.
    18. Paolella, Marc S. & Taschini, Luca, 2008. "An econometric analysis of emission allowance prices," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2022-2032, October.
    19. Han, Liyan & Lv, Qiuna & Yin, Libo, 2017. "Can investor attention predict oil prices?," Energy Economics, Elsevier, vol. 66(C), pages 547-558.
    20. Xiaohang Ren & Yue Dou & Kangyin Dong & Yiying Li, 2022. "Information spillover and market connectedness: multi-scale quantile-on-quantile analysis of the crude oil and carbon markets," Applied Economics, Taylor & Francis Journals, vol. 54(38), pages 4465-4485, August.
    21. Yin, Libo & Feng, Jiabao, 2019. "Can investors attention on oil markets predict stock returns?," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 786-800.
    22. Wang, Yizhi, 2022. "Volatility spillovers across NFTs news attention and financial markets," International Review of Financial Analysis, Elsevier, vol. 83(C).
    23. Ibikunle, Gbenga & McGroarty, Frank & Rzayev, Khaladdin, 2020. "More heat than light: Investor attention and bitcoin price discovery," International Review of Financial Analysis, Elsevier, vol. 69(C).
    24. 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.
    25. 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.
    26. Guidolin, Massimo & Hyde, Stuart, 2012. "Can VAR models capture regime shifts in asset returns? A long-horizon strategic asset allocation perspective," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 695-716.
    27. Hedi Arouri, Mohamed El & Khuong Nguyen, Duc, 2010. "Oil prices, stock markets and portfolio investment: Evidence from sector analysis in Europe over the last decade," Energy Policy, Elsevier, vol. 38(8), pages 4528-4539, August.
    28. repec:dau:papers:123456789/4598 is not listed on IDEAS
    29. Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
    30. Shangrong Jiang & Yuze Li & Quanying Lu & Yongmiao Hong & Dabo Guan & Yu Xiong & Shouyang Wang, 2021. "Policy assessments for the carbon emission flows and sustainability of Bitcoin blockchain operation in China," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    31. Ali, Fahad & Bouri, Elie & Naifar, Nader & Shahzad, Syed Jawad Hussain & AlAhmad, Mohammad, 2022. "An examination of whether gold-backed Islamic cryptocurrencies are safe havens for international Islamic equity markets," Research in International Business and Finance, Elsevier, vol. 63(C).
    32. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    33. 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.
    34. Yin, Libo & Feng, Jiabao & Liu, Li & Wang, Yudong, 2019. "It's not that important: The negligible effect of oil market uncertainty," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 62-84.
    35. Neto, David, 2022. "Examining interconnectedness between media attention and cryptocurrency markets: A transfer entropy story," Economics Letters, Elsevier, vol. 214(C).
    36. Han, Liyan & Xu, Yang & Yin, Libo, 2018. "Does investor attention matter? The attention-return relationships in FX markets," Economic Modelling, Elsevier, vol. 68(C), pages 644-660.
    37. Chen, Rongda & Qian, Qian & Jin, Chenglu & Xu, Min & Song, Qiping, 2020. "Investor attention on internet financial markets," Finance Research Letters, Elsevier, vol. 36(C).
    38. Liyan Han & You Wu & Libo Yin, 2018. "Investor attention and currency performance: international evidence," Applied Economics, Taylor & Francis Journals, vol. 50(23), pages 2525-2551, May.
    39. Zhang, Yue-Jun & Lin, Jia-Juan, 2019. "Can the VAR model outperform MRS model for asset allocation in commodity market under different risk preferences of investors?," International Review of Financial Analysis, Elsevier, vol. 66(C).
    40. Yunhe Cheng & Beibei Hu, 2022. "Forecasting Regional Carbon Prices in China Based on Secondary Decomposition and a Hybrid Kernel-Based Extreme Learning Machine," Energies, MDPI, vol. 15(10), pages 1-18, May.
    41. Wang, Yudong & Wei, Yu & Wu, Chongfeng & Yin, Libo, 2018. "Oil and the short-term predictability of stock return volatility," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 90-104.
    42. Wang, Chen & Shen, Dehua & Li, Youwei, 2022. "Aggregate Investor Attention and Bitcoin Return: The Long Short-term Memory Networks Perspective," Finance Research Letters, Elsevier, vol. 49(C).
    43. Qingjie Zhou & Panpan Zhu & You Wu & Yinpeng Zhang, 2022. "Research on the Volatility of the Cotton Market under Different Term Structures: Perspective from Investor Attention," Sustainability, MDPI, vol. 14(21), pages 1-20, November.
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    1. Qiyun Cheng & Huiting Qiao & Yimiao Gu & Zhenxi Chen, 2023. "Price Dynamics and Interactions between the Chinese and European Carbon Emission Trading Markets," Energies, MDPI, vol. 16(4), pages 1-12, February.

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