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

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.

KEYWORDS:

• Realized volatility
• HAR model
• support vector regression
• combination method
• realized semi-variances

JEL CLASSIFICATION:

• G13

Data availability statement

The data that support this study are available from the corresponding author upon reasonable request.

CRediT author statement

Gaoxun Zhang: Methodology, Software, Data curation, Writing.

Gaoxiu Qiao: Conceptualization, Writing-Reviewing and Editing, Funding acquisition.

Disclosure statement

No, potential conflict of interest was reported by the authors.

Additional information

Funding

The work is supported by the National Natural Science Foundation of China grant (71701171, 72001180), Humanities and Social Science Fund of Ministry of Education of China (17YJC790119) and the Fundamental Research Funds for the Central Universities (2682018WXTD05)