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

Gaoxun Zhang and Gaoxiu Qiao

Applied Economics, 2021, vol. 53, issue 19, 2192-2205

Abstract: 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.

Date: 2021
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DOI: 10.1080/00036846.2020.1856326

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