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The impact of cryptocurrencies on China's carbon price variation during COVID-19: A quantile perspective

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  • Chen, Hao
  • Xu, Chao

Abstract

We aim to document the impact of cryptocurrencies on China's carbon price variation using some quantile techniques during COVID-19 with the daily data spanning from August 7, 2015 to April 30, 2021. In this paper, we show that cryptocurrencies have a very strong explanation power for carbon market with the non-parametric causality-in-quantiles method. In addition, cryptocurrencies can work as a good hedging candidate for carbon market at different investment horizons with the quantile coherency approach. Using hedging effectiveness measure, we further show that COVID-19 can reverse the optimal hedging ratios in our portfolio specification in cryptocurrencies‑carbon emission trading pairs while this pandemic does not have effects on the trading effectiveness. Finally, the heterogeneity and asymmetry features in the dynamic quantile-on-quantile effects are detected and the effects on carbon efficient index show relatively strong fluctuation while on carbon emission trading market are relatively strong in magnitude. Our empirical results conclude with many potential applications for policymakers and investors.

Suggested Citation

  • Chen, Hao & Xu, Chao, 2022. "The impact of cryptocurrencies on China's carbon price variation during COVID-19: A quantile perspective," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:tefoso:v:183:y:2022:i:c:s0040162522004541
    DOI: 10.1016/j.techfore.2022.121933
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    Cited by:

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    3. Qi, Haozhi & Ma, Lijun & Peng, Pin & Chen, Hao & Li, Kang, 2022. "Dynamic connectedness between clean energy stock markets and energy commodity markets during times of COVID-19: Empirical evidence from China," Resources Policy, Elsevier, vol. 79(C).
    4. Liu, Xiaoqin & Wojewodzki, Michal & Cai, Yifei & Sharma, Satish, 2023. "The dynamic relationships between carbon prices and policy uncertainties," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    5. Cheng, Jiyang & Tiwari, Sunil & Khaled, Djebbouri & Mahendru, Mandeep & Shahzad, Umer, 2024. "Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

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    More about this item

    Keywords

    Cryptocurrencies; China's carbon price; Causality-in-quantiles; Quantile-on-quantile; Quantile coherency; COVID-19;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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