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Dynamic information spillover between Chinese carbon and stock markets under extreme weather shocks

Author

Listed:
  • Zhang-Hangjian Chen

    (Anhui University
    Anhui University)

  • Xiang Gao

    (Shanghai Business School)

  • Apicha Insuwan

    (Payap University)

Abstract

The present study aims to investigate the dynamic information spillover relationship between Chinese carbon and stock markets, as well as the impact of extreme weather shocks exerted on this relationship. The method adopted is the least absolute shrinkage and selection operator–vector autoregressive–Diebold-Yilmaz spillover approach so that the degree and direction of risk spillovers among markets can be assessed simultaneously. Empirical results reveal that there is a high level of extreme risk spillover among markets in comparison to return spillover. The carbon market receives return spillover from high-polluting sectors, but it will turn into a risk transmitter under extreme risk conditions. Weather shocks significantly affect extreme risk spillover among markets and may lead to spillovers from the carbon market to low-polluting sectors. The portfolio strategy constructed based on the identified information spillover relationship is shown to achieve higher average returns than strategies focusing on a single carbon or stock market sector. This paper is among the first to integrate carbon markets and 38 stock sector indices for different pollution intensities, comprehensively exploring their dynamic interrelationships under extreme weather threats. The corresponding practical and policy implications for investors and regulators are also provided along with these findings.

Suggested Citation

  • Zhang-Hangjian Chen & Xiang Gao & Apicha Insuwan, 2023. "Dynamic information spillover between Chinese carbon and stock markets under extreme weather shocks," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
  • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02134-7
    DOI: 10.1057/s41599-023-02134-7
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    1. 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).
    2. Natalia Fabra & Mar Reguant, 2014. "Pass-Through of Emissions Costs in Electricity Markets," American Economic Review, American Economic Association, vol. 104(9), pages 2872-2899, September.
    3. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    4. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
    5. Bourdeau-Brien, Michael & Kryzanowski, Lawrence, 2017. "The impact of natural disasters on the stock returns and volatilities of local firms," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 259-270.
    6. Meng, Bin & Chen, Shuiyang & Haralambides, Hercules & Kuang, Haibo & Fan, Lidong, 2023. "Information spillovers between carbon emissions trading prices and shipping markets: A time-frequency analysis," Energy Economics, Elsevier, vol. 120(C).
    7. Hong, Yongmiao & Liu, Yanhui & Wang, Shouyang, 2009. "Granger causality in risk and detection of extreme risk spillover between financial markets," Journal of Econometrics, Elsevier, vol. 150(2), pages 271-287, June.
    8. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    9. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    10. Chen, Zhang-HangJian & Ren, Fei & Yang, Ming-Yuan & Lu, Feng-Zhi & Li, Sai-Ping, 2023. "Dynamic lead–lag relationship between Chinese carbon emission trading and stock markets under exogenous shocks," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 295-305.
    11. Bin Meng & Shuiyang Chen & Hercules Haralambides & Haibo Kuang & Lidong Fan, 2023. "Information spillovers between carbon emissions trading prices and shipping markets: A time-frequency analysis," Post-Print hal-04046290, HAL.
    12. William N. Goetzmann & Dasol Kim & Alok Kumar & Qin Wang, 2015. "Weather-Induced Mood, Institutional Investors, and Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 73-111.
    13. Kewei Hou & Tobias J. Moskowitz, 2005. "Market Frictions, Price Delay, and the Cross-Section of Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 981-1020.
    14. Nicholson, William B. & Matteson, David S. & Bien, Jacob, 2017. "VARX-L: Structured regularization for large vector autoregressions with exogenous variables," International Journal of Forecasting, Elsevier, vol. 33(3), pages 627-651.
    15. Bin Meng & Shuiyang Chen & Hercules Haralambides & Haibo Kuang & Lidong Fan, 2023. "Information spillovers between carbon emissions trading prices and shipping markets: A time-frequency analysis," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-04046290, HAL.
    16. 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.
    17. Liu, Hsiang-Hsi & Chen, Yi-Chun, 2013. "A study on the volatility spillovers, long memory effects and interactions between carbon and energy markets: The impacts of extreme weather," Economic Modelling, Elsevier, vol. 35(C), pages 840-855.
    18. Xu, Lin & Wu, Chenyang & Qin, Quande & Lin, Xiaoying, 2022. "Spillover effects and nonlinear correlations between carbon emissions and stock markets: An empirical analysis of China's carbon-intensive industries," Energy Economics, Elsevier, vol. 111(C).
    19. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.
    20. Lanfear, Matthew G. & Lioui, Abraham & Siebert, Mark G., 2019. "Market anomalies and disaster risk: Evidence from extreme weather events," Journal of Financial Markets, Elsevier, vol. 46(C).
    21. Yue-Jun Zhang, 2016. "Research on carbon emission trading mechanisms: current status and future possibilities," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 39(1/2), pages 89-107.
    22. Fabio Pizzutilo & Massimo Mariani & Alessandra Caragnano & Marianna Zito, 2020. "Dealing with Carbon Risk and the Cost of Debt: Evidence from the European Market," IJFS, MDPI, vol. 8(4), pages 1-10, October.
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