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.

摘要

本文从样本外实现的波动率预测角度研究了异构自回归模型（SVR-HAR）下的非线性支持向量回归方法是否可以竞争组合方法。基于CSI 300指数高频数据进行了实证分析，采用了两种新的组合方法，并与SVR方法的预测能力进行了比较。实验结果表明，尽管新的组合方法优于其他组合策略，但SVR-HAR模型优于单个模型和所有组合方法。具体而言，以已实现的半方差作为回归因子的HAR模型获得的预测误差最低，这证明了非线性SVR方法和已实现的半方差的强大预测能力。