Out-of-sample realized volatility forecasting: does the support vector regression compete combination methods

This article investigates whether the nonlinear support vector regression method under the Heterogeneous Auto-Regressive model (SVR-HAR) can compete for combination methods in terms of out-of-sample realized volatility forecasting. Empirical analyses are conducted based on the CSI 300 index high-fre...

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Bibliographic Details
Published in:Applied economics 2021-04, Vol.53 (19), p.2192-2205
Main Authors: Zhang, Gaoxun, Qiao, Gaoxiu
Format: Article
Language:English
Subjects:
combination method
Economic analysis
Economic theory
Forecasting
HAR model
Methods
Portfolio performance
realized semi-variances
Realized volatility
support vector regression
Value
Volatility
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Summary:This article investigates whether the nonlinear support vector regression method under the Heterogeneous Auto-Regressive model (SVR-HAR) can compete for combination methods in terms of out-of-sample realized volatility forecasting. Empirical analyses are conducted based on the CSI 300 index high-frequency data, two new combination methods are employed and compared with the forecasting ability of the SVR method. The empirical results show that SVR-HAR models outperform individual models and all the combination methods, although the new combination methods are superior to other combination strategies. Specifically, HAR models with realized semi-variances as regressors obtains the lowest forecasting errors, confirming the strong forecasting ability of nonlinear SVR method and the realized semi-variances. The portfolio performance further confirms the highest economic value for models employing realized semi-variances and nonlinear SVR method in terms of volatility forecasting.
ISSN:0003-6846
1466-4283
DOI:10.1080/00036846.2020.1856326