Properties of Realized Variance Under Alternative Sampling Schemes
研究市场微观结构噪声下已实现方差估计量的统计性质，发现交易时间抽样优于日历时间抽样，能降低均方误差，尤其当交易强度波动大时效果更显著。基于IBM数据，最优抽样频率约3分钟，交易时间抽样平均降低均方误差约5%，不规则交易日可达40%。
This paper investigates the statistical properties of the realized variance estimator in the presence of market microstructure noise. Different from the existing literature, the analysis relies on a pure jump process for high frequency security prices and explicitly distinguishes among alternative sampling schemes, including calendar time sampling, business time sampling, and transaction time sampling. The main finding of this paper is that transaction time sampling is generally superior to the common practice of calendar time sampling in that it leads to a lower mean squared error of the realized variance. The benefits of sampling in transaction time are particularly pronounced when the trade intensity pattern is volatile. Based on IBM transaction data over the period 2000-2004 the empirical analysis finds (i) an average optimal sampling frequency of about 3 minutes with a steady downward trend and significant day-to-day variation related to market liquidity and (ii) a consistent reduction in mean squared error of the realized variance due to sampling in transaction time that is about 5% on average but can be as high as 40% on days with irregular trading. Keywords: high frequency data, market microstructure noise, pure jump process, optimal sampling JEL Classifications: C14, C22, G12