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Modelling returns volatility: mixed-frequency model based on momentum of predictability

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  • Zhenlong Chen
  • Shang Jin

Abstract

The estimation and prediction of financial asset volatility are important in terms of theoretical and practical applications. Considering that low-frequency and high-frequency information plays an important role in volatility prediction, this article proposes a mixed-frequency model based on the momentum of predictability (MF-MoP). To illustrate the advantages of the proposed model, comparative research is conducted on the prediction accuracy of volatility among the GARCH model, the Realized GARCH model and the MF-MoP model, by the loss function and MCS test. The empirical results show that the MF-MoP model has higher prediction accuracy than the other two models; especially based on skewed-t distribution, the MF-MoP significantly outperforms the competing models. Moreover, the MF-MoP model can improve the forecasting of volatility, regardless of different look-back periods (including 1, 3, 6 and 9 days), different data (including the CSI 300 index, the N225 index and the KS11 index), and realized measures (including RV, RRV and MedRV), indicating that the model is robust.

Suggested Citation

  • Zhenlong Chen & Shang Jin, 2023. "Modelling returns volatility: mixed-frequency model based on momentum of predictability," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 36(1), pages 2117228-211, March.
    • Handle: RePEc:taf:reroxx:v:36:y:2023:i:1:p:2117228
    DOI: 10.1080/1331677X.2022.2117228
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