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Testing the directional predictability between carbon trading and sectoral stocks in China: New insights using cross-quantilogram and rolling window causality approaches

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  • Razzaq, Asif
  • Sharif, Arshian
  • An, Hui
  • Aloui, Chaker

Abstract

Carbon trading is a market-based instrument to regulate industrial carbon footprints that affects other financial markets. The preliminary test of this study reveals that a wide variety of nonlinearities exist in carbon trading and stock market data, indicating that an investigation of asymmetric behavior is imperative to explore the complex relationship. This study applies cross-quantilogram, and rolling window causality approaches to investigate the asymmetric dependence structure and directional predictability from the carbon trading market to the stock market in China from aggregate and sectoral perspectives. The overall results confirm negative (positive) directional predictability from carbon trading price (CTP) to stock market prices in bull (bear) market conditions. Nevertheless, the dependence structure between variables varies substantially across sectors, with technology, industrial, financial, utility, and material sectors responding more strongly at higher quantiles. The results imply that lower CTP (bear markets) encourage firms' production, revenue, and stock prices, whereas higher CTP (bull markets) lead to higher production costs, generating lower output, lower profitability, and, finally, a reduction in stock prices. The results also reveal that any change in CTP distinctly affects stock market indices and the magnitude and direction of causality changes across lag structures. These findings suggest that carbon trading and stock markets are well integrated and require an inclusive policy design to promote sustainable growth.

Suggested Citation

  • Razzaq, Asif & Sharif, Arshian & An, Hui & Aloui, Chaker, 2022. "Testing the directional predictability between carbon trading and sectoral stocks in China: New insights using cross-quantilogram and rolling window causality approaches," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    • Handle: RePEc:eee:tefoso:v:182:y:2022:i:c:s0040162522003705
    DOI: 10.1016/j.techfore.2022.121846
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