A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data
研究了当日内部分时段高频价格数据缺失时，如何估计日度已实现方差，提出了三种估计量并给出最优组合方法，最后用道琼斯30只股票四年数据验证。
We consider the problem of deriving an empirical measure of daily integrated variance (IV) in the situation where high-frequency price data are unavailable for part of the day. We study three estimators in this context and characterize the assumptions that justify their use. We show that the optimal combination of the realized variance and squared overnight return can be determined, despite the latent nature of IV, and we discuss this result in relation to the problem of combining forecasts. Finally, we apply our theoretical results and construct four years of daily volatility estimates for the 30 stocks of the Dow Jones Industrial Average.