We revisit in-sample asymptotic analysis extensively used in the realized volatility literature. We show that there are gains to be made in estimating current realized volatility from considering realizations in prior periods. The weighting schemes also relate to Kalman-Bucy filters, although our approach is non-Gaussian and model-free. We derive theoretical results for a broad class of processes pertaining to volatility, higher moments, and leverage. The paper also contains a Monte Carlo simulation study showing the benefits of across-sample combinations.

中文翻译：

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高频数据统计的样本内渐近和跨样本效率增益

我们重新审视已实现波动率文献中广泛使用的样本内渐近分析。我们表明，通过考虑前期的实现来估计当前已实现的波动率会有所收获。加权方案也与 Kalman-Bucy 滤波器有关，尽管我们的方法是非高斯和无模型的。我们为与波动性、高矩和杠杆有关的广泛过程得出了理论结果。该论文还包含一项蒙特卡罗模拟研究，展示了跨样本组合的优势。