摘要
基于已实现GARCH模型和混频数据抽样(MIDAS)结构,提出了已实现混频数据抽样GARCH模型.该模型使用混频数据抽样结构从已实现测度中提取长短期波动率信息以提升模型对波动率的拟合和预测能力.基于指数和个股数据的实证分析表明,相比传统的已实现GARCH模型,新模型的样本内拟合能力更强,对长记忆性的捕捉更好.样本外结果表明,新模型显著提升了波动率的多步预测效果,并且改进效果随着预测期的延长而增强.
This paper proposed a realized MIDAS GARCH model based on the realized GARCH model and the mixed data sampling(MIDAS) regression structure. The model uses MIDAS structure to extract long and short term information from realized measures to improve the model’s ability to fit and forecast volatility process. Empirical results based on indices and stocks data show that, compared with the classical realized GARCH model, the new model is better in in-sample data fitting and replicating long memory feature. The outof-sample forecasting results show that the new model significantly improves the multi-period out-of-sample volatility forecast. The improvement is more pronounced in longer horizons.
作者
王天一
刘浩
黄卓
Wang Tianyi;Liu Hao;Huang Zhuo(School of Banking and Finance,University of International Business and Economics,Beijing 100029,China;National School of Development,Peking University,Beijing 100871,China)
出处
《系统工程学报》
CSCD
北大核心
2018年第6期812-822,共11页
Journal of Systems Engineering
基金
国家自然科学基金资助项目(71301027
71671004
71871060)
