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Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model

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  • Wang, Lu
  • Zhao, Chenchen
  • Liang, Chao
  • Jiu, Song

Abstract

Given that the new energy stock market plays a vital part in the development of China's new energy industry, we first introduce the REGARCH-MIDAS model to predict volatility in the new energy stock market. Intraday, daily and monthly data, and the leverage effect are simultaneously fully introduced in a MIDAS setting, the in-sample results show that the REGARCH-MIDAS model can obtain an optimal estimate. Moreover, the out-of-sample results demonstrate that the REGARCH-MIDAS model exhibits the strongest predictive power in all selected models. Finally, our conclusions are robust to further robustness checks. Our work provides deep insights into how to predict the volatility of China's new energy by using three different frequency data.

Suggested Citation

  • Wang, Lu & Zhao, Chenchen & Liang, Chao & Jiu, Song, 2022. "Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 48(C).
    • Handle: RePEc:eee:finlet:v:48:y:2022:i:c:s1544612322002306
    DOI: 10.1016/j.frl.2022.102981
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    2. Bonaparte, Yosef & Chatrath, Arjun & Christie-David, Rohan, 2023. "S&P volatility, VIX, and asymptotic volatility estimates," Finance Research Letters, Elsevier, vol. 51(C).
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    6. Duan, Huayou & Zhao, Chenchen & Wang, Lu & Liu, Guangqiang, 2024. "The relationship between renewable energy attention and volatility: A HAR model with markov time-varying transition probability," Research in International Business and Finance, Elsevier, vol. 71(C).

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