论文标题
具有长长和短期组件的双重乘法误差模型
Doubly Multiplicative Error Models with Long- and Short-run Components
论文作者
论文摘要
我们建议模型的双重乘法误差类别（DMEM）用于建模和预测实现的波动率，该类别分别结合了两个组件，分别适合数据中低频特征。我们得出了最大可能性和矩估计器的广义方法的理论特性。然后提出了两个这样的模型，即组件-MEM，该模型使用两个组件的每日数据和MEM-MIDA，该数据利用了混合数据采样（MIDAS）的逻辑。经验应用涉及标准普尔500指数，纳斯达克，FTSE 100和Hang Seng Indices：无论市场如何，DMEM的跑赢大盘和其他相关的Garch型模型均表现不错。
We suggest the Doubly Multiplicative Error class of models (DMEM) for modeling and forecasting realized volatility, which combines two components accommodating low-, respectively, high-frequency features in the data. We derive the theoretical properties of the Maximum Likelihood and Generalized Method of Moments estimators. Two such models are then proposed, the Component-MEM, which uses daily data for both components, and the MEM-MIDAS, which exploits the logic of MIxed-DAta Sampling (MIDAS). The empirical application involves the S&P 500, NASDAQ, FTSE 100 and Hang Seng indices: irrespective of the market, both DMEM's outperform the HAR and other relevant GARCH-type models.
