Original scientific paper
https://doi.org/10.1080/1331677X.2022.2117228
Modelling returns volatility: mixed-frequency model based on momentum of predictability
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 lookback
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.
Keywords
Mixed-frequency; prediction; loss function; MCS test; realized measures
Hrčak ID:
306456
URI
Publication date:
31.3.2023.
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