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

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  • Gaoxun Zhang
  • Gaoxiu Qiao

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.

Suggested Citation

  • Gaoxun Zhang & Gaoxiu Qiao, 2021. "Out-of-sample realized volatility forecasting: does the support vector regression compete combination methods," Applied Economics, Taylor & Francis Journals, vol. 53(19), pages 2192-2205, April.
    • Handle: RePEc:taf:applec:v:53:y:2021:i:19:p:2192-2205
    DOI: 10.1080/00036846.2020.1856326
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    Cited by:

    1. Gaoxiu Qiao & Yangli Cao & Feng Ma & Weiping Li, 2023. "Liquidity and realized covariance forecasting: a hybrid method with model uncertainty," Empirical Economics, Springer, vol. 64(1), pages 437-463, January.
    2. Auh, Jun Kyung & Cho, Wonho, 2023. "Factor-based portfolio optimization," Economics Letters, Elsevier, vol. 228(C).

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