标题： 具有噪声与收益率之间相关性的综合方差估计器的条件高斯随机序列 显示英文标题

标题： Conditionally Gaussian Random Sequences for an Integrated Variance Estimator with Correlation between Noise and Returns

摘要： 微观结构噪声与潜在金融对数收益率之间的相关性是一种具有坚实理论依据的经验相关现象。 除了少数显著例外，金融文献中提出的几乎所有已积方差估计量都没有被设计用来显式处理这种依赖关系，或者仅在特殊情况下处理它。 我们提供了一个对相关噪声和收益率稳健的已积方差估计器。 为此，提出了一种Forward Filtering Backward Sampling算法的广义形式，以提供潜在条件高斯随机序列的采样技术。 我们将我们的方法应用于微软公司日内价格，并通过模拟研究将其与现有替代方案进行比较，在均方根误差和离散度方面显示出优势。 显示英文摘要

摘要： Correlation between microstructure noise and latent financial logarithmic returns is an empirically relevant phenomenon with sound theoretical justification. With few notable exceptions, all integrated variance estimators proposed in the financial literature are not designed to explicitly handle such a dependence, or handle it only in special settings. We provide an integrated variance estimator that is robust to correlated noise and returns. For this purpose, a generalization of the Forward Filtering Backward Sampling algorithm is proposed, to provide a sampling technique for a latent conditionally Gaussian random sequence. We apply our methodology to intra-day Microsoft prices, and compare it in a simulation study with established alternatives, showing an advantage in terms of root mean square error and dispersion.