Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach
Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach
复制标题
高频波动率的估计：自回归条件持续时间方法
DOI：
10.1080/07350015.2012.707582
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发表时间：
2012
影响因子：
3
通讯作者：
T. Yang
中科院分区：
文献类型：
--
作者：
Y. Tse;T. Yang
我们提出了一种通过积分自回归条件持续期(ACD)模型得到的单位时间的瞬时条件收益方差来估计股票日内波动性的方法，称为ACD-ICV方法。我们将使用ACD-ICV方法估计的日波动率与几种版本的已实现波动率(RV)方法进行了比较，这些方法包括二次抽样双幂变化RV、已实现核估计和基于持续期的RV。蒙特卡罗结果表明，在几乎所有情况下，ACD-ICV方法都比RV方法具有更低的均方根误差。这篇文章有在线补充材料。
We propose a method to estimate the intraday volatility of a stock by integrating the instantaneous conditional return variance per unit time obtained from the autoregressive conditional duration (ACD) model, called the ACD-ICV method. We compare the daily volatility estimated using the ACD-ICV method against several versions of the realized volatility (RV) method, including the bipower variation RV with subsampling, the realized kernel estimate, and the duration-based RV. Our Monte Carlo results show that the ACD-ICV method has lower root mean-squared error than the RV methods in almost all cases considered. This article has online supplementary material.