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Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market

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  • Wang, Yajing
  • Liang, Fang
  • Wang, Tianyi
  • Huang, Zhuo

Abstract

Based on methods developed by Bollerslev et al. (2016), we explicitly accounted for the heteroskedasticity in the measurement errors and for the high volatility of Chinese stock prices; we proposed a new model, the LogHARQ model, as a way to appropriately forecast the realized volatility of the Chinese stock market. Out-of-sample findings suggest that the LogHARQ model performs better than existing logarithmic and linear forecast models, particularly when the realized quarticity is large. The better performance is also confirmed by the utility based economic value test through volatility timing.

Suggested Citation

  • Wang, Yajing & Liang, Fang & Wang, Tianyi & Huang, Zhuo, 2020. "Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market," Economic Modelling, Elsevier, vol. 87(C), pages 148-157.
    • Handle: RePEc:eee:ecmode:v:87:y:2020:i:c:p:148-157
    DOI: 10.1016/j.econmod.2019.07.014
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    Cited by:

    1. Alemany, Nuria & Aragó, Vicent & Salvador, Enrique, 2020. "The distribution of index futures realised volatility under seasonality and microstructure noise," Economic Modelling, Elsevier, vol. 93(C), pages 398-414.
    2. Chen, Jilong & Xu, Liao, 2023. "Do exchange-traded fund activities destabilize the stock market? Evidence from the China securities index 300 stocks," Economic Modelling, Elsevier, vol. 127(C).
    3. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.

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    More about this item

    Keywords

    Realized volatility; Measurement errors; Volatility forecasting; Chinese stock market;
    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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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