论文标题
综合协方差矩阵的高维Hayashi-Yoshida估计器的限制光谱分布
Limiting Spectral Distribution of High-dimensional Hayashi-Yoshida Estimator of Integrated Covariance Matrix
论文作者
论文摘要
在本文中，考虑了高维股票价格过程的高频数据对综合协方差矩阵的估计。 Hayashi-Yoshida的共价估计量是对异步数据的实现的共透性的改进，并且在低维度上效果很好。但是，在高维情况下，它变得不一致和不可靠。我们研究了该矩阵的整体频谱，并在限制的情况下建立了其与真实协方差矩阵谱的连接。通过有限但高维病例的模拟研究来说明结果。提出了有关50个股票的逐个数据的真实数据的应用程序。
In this paper, the estimation of the Integrated Covariance matrix from high-frequency data, for high dimensional stock price process, is considered. The Hayashi-Yoshida covolatility estimator is an improvement over Realized covolatility for asynchronous data and works well in low dimensions. However it becomes inconsistent and unreliable in the high dimensional situation. We study the bulk spectrum of this matrix and establish its connection to the spectrum of the true covariance matrix in the limiting case where the dimension goes to infinity. The results are illustrated with simulation studies in finite, but high, dimensional cases. An application to real data with tick-by-tick data on 50 stocks is presented.
