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英文文献：Flat-Top Realized Kernel Estimation of Quadratic Covariation with Non-Synchronous and Noisy Asset Prices-平顶实现了非同步噪声资产价格二次共变的核估计
英文文献作者：Rasmus Tangsgaard Varneskov
英文文献摘要：
This paper extends the class of generalized at-top realized kernels, introduced in Varneskov (2011), to the multivariate case, where quadratic covariation of non-synchronously observed asset prices is estimated in the presence of market microstructure noise that is allowed to exhibit serial dependence and to be correlated with the efficient price process. Estimators in this class are shown to posses desirable statistical properties such as consistency, asymptotic normality, and asymptotic unbiasedness at an optimal n^(1/4)-convergence rate. A finite sample correction based on projections of symmetric matrices ensures positive (semi-)definiteness without altering asymptotic properties of the class of estimators. The finite sample correction admits non-linear transformations of the estimated covariance matrix such as correlations and realized betas, and it can be used in portfolio optimization problems. These transformations are all shown to inherit the desirable asymptotic properties of the generalized at-top realized kernels. A simulation study shows that the class of estimators has a superior finite sample tradeoff between bias and root mean squared error relative to competing estimators. Lastly, two small empirical applications to high frequency stock market data illustrate the bias reduction relative to competing estimators in estimating correlations, realized betas, and mean-variance frontiers, as well as the use of the new estimators in the dynamics of hedging.
本文扩展了一类广义以最高意识到内核,引入Varneskov(2011),二次共变的多变量情况下,观察到的非同步的资产价格估计在市场微观结构噪音的存在,可以表现出连续依赖和相关有效价格的过程。这类估计量在n^(1/4)收敛率下具有良好的统计性质，如一致性、渐近正态性和渐近无偏性。在不改变估计量渐近性质的情况下，基于对称矩阵投影的有限样本修正保证了估计量的正(半)确定性。有限样本校正允许协方差估计矩阵的相关和已实现贝塔等非线性变换，可用于投资组合优化问题。这些变换都被证明继承了广义上实现的核的渐近性质。仿真研究表明，相对于竞争估计量，这类估计量在偏置和均方根误差之间具有更优的有限样本折衷。最后，对高频股票市场数据的两个小的经验应用说明了在估计相关性、实现贝塔值和均值-方差边界时相对于竞争估计量的偏差减少，以及在对冲动态中新估计量的使用。
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