This paper analyzes the benefits of sampling intraday returns in intrinsic time for the standard and pre-averaging realized variance (RV) estimators. We theoretically show in finite samples and asymptotically that the RV estimator is most efficient under the new concept of realized business time, which samples according to a combination of observed trades and estimated tick variance. Our asymptotic results carry over to the pre-averaging RV estimator under market microstructure noise. The analysis builds on the assumption that asset prices follow a diffusion that is time-changed with a jump process that separately models the transaction times. This provides a flexible model that separately captures the empirically varying trading intensity and tick variance processes, which are particularly relevant for disentangling the driving forces of the sampling schemes. Extensive simulations confirm our theoretical results and show that realized business time remains superior also under more general noise and process specifications. An application to stock data provides empirical evidence for the benefits of using realized business time sampling to construct more efficient RV estimators as well as for an improved forecasting performance.

翻译：本文分析了在标准与预平均已实现方差（RV）估计量中采用内蕴时间进行日内收益率采样的优势。我们通过有限样本与渐进理论证明，在"已实现交易时间"这一新概念下——该概念依据观测交易次数与估计的逐笔波动方差组合进行采样——RV估计量具有最高效率。我们的渐进结论在微观结构噪声环境下可推广至预平均RV估计量。该分析基于资产价格服从由跳跃过程时间变化的扩散模型这一假设，其中跳跃过程独立刻画交易时刻。这一灵活模型能分别捕捉实证中交易强度与逐笔波动方差的时变特征，对厘清不同采样方案背后的驱动力尤为重要。广泛模拟实验验证了理论结果，表明即使在更一般的噪声与过程设定下，已实现交易时间仍保持优越性。基于股票数据的实证应用证明了采用已实现交易时间采样可构建更高效的RV估计量，并提升预测性能。