作　　者： ; ; ;

机构地区： 华南农业大学经济管理学院

出　　处： 《中国管理科学》 2013年第3期50-60,共11页

摘　　要： 本文基于C_TMPV理论估计已实现波动率的跳跃成分,在此基础上构建考虑跳跃的AHAR-RV-CJ模型和MIDAS-RV-CJ模型来预测中国股市的已实现波动率,并评价和比较各类波动率模型的预测精度。实证结果表明:基于C_TMPV估计的波动率跳跃成分对日、周以及月波动率的预测有显著的正向影响;AHAR-RV-CJ模型和MIDAS-RV-CJ模型的样本内和样本外预测精度在不同的预测时域上都是最高的,尤其是对数形式的模型;MI-DAS族模型的样本外预测精度在中长期预测时域上比HAR族模型高;AHAR-RV-CJ模型和MIDAS-RV-CJ模型的样本外预测能力在中长期预测时域上比基于低频数据的Jump-GARCH模型、SV-CJ模型和SV-IJ模型好。 Based on the theory of corrected realized threshold multipower variation（C_TMPV）, the jump components of the realized volatility are estimated, and two newly developed realized volatility model allo- wing for jump, the AHAR-RV-CJ model and MIDAS-RV-CJ model, are proposed to predict realized vola- tility of Chinese Stock Markets. The forecast accuracies of several volatility models are also evaluated and compared. Our findings demonstrate that the jump components of the realized volatility estimated by C_ TMPV have positive and significant impacts on daily, weekly and monthly volatility prediction, and the AHAR-RV-CJ model and MIDAS-RV-CJ models with the continuous and jump components of the volatili- ty are the best models for future volatility prediction in different prediction horizons. These results hold up for both the in-sample and out-of-sample forecasts, especially the logarithmic models. It is also found that the out-of-sample forecasting performance of MIDAS model is better than HAR model with the same re- gressor and the out-of-sample predictive power of AHAR-RV-CJ and MIDAS-RV-CJ models is better than Jump-GARCH, SV-CJ and SV-IJ models in the medium and long prediction horizons.

关 键 词： 波动率预测 已实现波动率 模型 检验

领　　域： [经济管理] [经济管理]