具有长期和短期分量的双乘误差模型
Doubly Multiplicative Error Models with Long- and Short-run Components
摘要：
我们建议双乘误差模型（DMEM）的建模和预测已实现波动率，它结合了两个组件，分别容纳低，高频功能的数据。我们推导了极大似然估计和广义矩估计的理论性质。然后提出了两个这样的模型，该模型使用日常数据的两个组件，和MEM-MIDAS，它利用逻辑的混合数据采样（MIDAS）。实证应用涉及标准普尔500指数，纳斯达克，富时100指数和恒生指数：无论市场，DMEM的表现优于HAR和其他相关的GARCH型模型。
英文摘要：
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.
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