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A Real-Time GARCH-MIDAS model

Author

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  • Wu, Xinyu
  • Zhao, An
  • Cheng, Tengfei

Abstract

This paper proposes the Real-Time GARCH-MIDAS model to model and forecast volatility. An empirical application to the Shanghai Stock Exchange Composite Index (SSEC) and Shenzhen Stock Exchange Component Index (SZSEC) of China shows that the Real-Time GARCH-MIDAS model outperforms competing models in terms of both empirical return fitting and out-of-sample volatility forecasting. Moreover, the superior forecasting performance of the Real-Time GARCH-MIDAS model is robust to alternative rolling windows, alternative benchmark models, alternative MIDAS lags and alternative volatility proxy. Further discussion illustrates the flexibility of the Real-Time GARCH-MIDAS model.

Suggested Citation

  • Wu, Xinyu & Zhao, An & Cheng, Tengfei, 2023. "A Real-Time GARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 56(C).
    • Handle: RePEc:eee:finlet:v:56:y:2023:i:c:s1544612323004750
    DOI: 10.1016/j.frl.2023.104103
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    Cited by:

    1. Mei, Xueting & Wang, Xinyu, 2024. "Forecasting stock volatility using time-distance weighting fundamental’s shocks," Finance Research Letters, Elsevier, vol. 65(C).
    2. Wu, Xinyu & Zhao, An & Wang, Yuyao & Han, Yang, 2024. "Forecasting Chinese stock market volatility with high-frequency intraday and current return information," Pacific-Basin Finance Journal, Elsevier, vol. 86(C).
    3. Chen, Zhenlong & Liu, Junjie & Hao, Xiaozhen, 2024. "Can asymmetry, long memory, and current return information improve crude oil volatility prediction? ——Evidence from ASHARV-MIDAS model," Finance Research Letters, Elsevier, vol. 64(C).

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    More about this item

    Keywords

    Real-Time GARCH-MIDAS; Persistence; Current return information; Volatility of volatility; Volatility forecasting;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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