Modelling returns volatility
Mixed-frequency model based on momentum of predictability
Bibliographic Data
| ID | 15066409 |
|---|---|
| Authors | Zhenlong Chen (0000-0002-0598-3099, Zhejiang Gongshang University), Shang Jin (0009-0002-7342-7884, Zhejiang Gongshang University, corresponding author) |
| Year | 2023 |
| Volume | 36 |
| Issue | 1 |
| Publication date | 2023-03-31 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Economic Research-Ekonomska Istraživanja (JOURNAL) |
| Journal identifiers | ISSN: 1331-677X • E-ISSN: 1848-9664 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/1331677x.2022.2117228 |
| OpenAlex | W4294938562 |
| Language | EN |
| References cited | 39 |
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
Autoregressive conditional heteroskedasticity · Econometrics · Economics · Forward volatility · Predictability · Realized variance · Statistics · Stochastic volatility · Computer Science · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Mathematics · Stock Market Forecasting Methods
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Generalized autoregressive conditional heteroskedasticity
Volatility analysis based on GARCH-type models
The impact of Covid-19 shocks on the volatility of stock markets in technologically advanced countries
An analytical approximation of option prices via TGARCH model
| Citation velocity | historical |
|---|---|
| Highly cited | No |