Integrated Covariance Estimation using High-frequency Data in the Presence of Noise
研究了非同步交易和市场微观结构噪声对已实现协方差估计的影响，提出了偏差校正和子抽样方法，并通过实证和模拟验证了方法的有效性。
We analyze the effects of nonsynchronicity and market microstructure noise on realized covariance type estimators. Hayashi and Yoshida (2005) propose a simple estimator that resolves the problem of nonsynchronicity and is unbiased and consistent for the integrated covariance in the absence of noise. When noise is present, however, we find that this estimator is biased, and show how the bias can be corrected for. Ultimately, we propose a subsampling version of the bias-corrected estimator which improves its efficiency. Empirically, we find that the usual assumption of a martingale price process plus an independently and identically distributed (i.i.d.) noise does not describe the dynamics of the observed price process across stocks, which confirms the practical relevance of our general noise specification and the estimation techniques we propose. Finally, a simulation experiment is carried out to complement the theoretical results.