Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach
提出一种通过整合自回归条件久期模型得到的瞬时条件方差来估计股票日内波动率的方法，蒙特卡洛模拟显示其均方根误差低于多种已实现波动率方法。
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.