IN-SAMPLE ASYMPTOTICS AND ACROSS-SAMPLE EFFICIENCY GAINS FOR HIGH FREQUENCY DATA STATISTICS
IN-SAMPLE ASYMPTOTICS AND ACROSS-SAMPLE EFFICIENCY GAINS FOR HIGH FREQUENCY DATA STATISTICS
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DOI：
10.1017/s0266466621000359
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发表时间：
2021-07
影响因子：
0.8
通讯作者：
Eric Ghysels;P. Mykland;É. Renault
中科院分区：
文献类型：
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作者：
Eric Ghysels;P. Mykland;É. Renault
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，可以在估计当前已实现的波动性方面取得进展。尽管我们的方法是非高斯且无模型的，但加权方案也与卡尔曼-布西滤波器相关。我们得出了与波动性、高矩和杠杆相关的广泛过程的理论结果。该论文还包含蒙特卡罗模拟研究，显示跨样本组合的好处。
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.