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Forecasting the Chinese low-carbon index volatility

Author

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  • Mei, Dexiang
  • Zhao, Chenchen
  • Luo, Qin
  • Li, Yan

Abstract

This paper investigates the predictive power of economic policy uncertainty on the Chinese low-carbon market volatility and takes into account realized measures. First, in-sample analysis shows that both economic policy uncertainty and intraday high-frequency information have a significant impact on low-carbon index volatility. Second, out-of-sample evaluations show that the model combining China's economic policy uncertainty and intraday high-frequency information has the best predictive power. Finally, we use several robustness tests of alternative macroeconomic variable, alternative forecasting window, and alternative realized measure to prove that the results of this study are robust. This study enriches the market volatility model research. In addition, it can also promote low-carbon investment and provide a reference for national macro-control.

Suggested Citation

  • Mei, Dexiang & Zhao, Chenchen & Luo, Qin & Li, Yan, 2022. "Forecasting the Chinese low-carbon index volatility," Resources Policy, Elsevier, vol. 77(C).
  • Handle: RePEc:eee:jrpoli:v:77:y:2022:i:c:s0301420722001805
    DOI: 10.1016/j.resourpol.2022.102732
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    More about this item

    Keywords

    GARCH model; China'S economic policy uncertainty; Realized measure; The Chinese low-carbon index;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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