Integrated Covariance Estimation Using High-Frequency Data in the Presence of Noise

2006 ◽  
Author(s):  
Valeri Voev ◽  
Asger Lunde
Keyword(s):  
High Frequency ◽  
Covariance Estimation ◽  
High Frequency Data ◽  
Frequency Data ◽  
Integrated Covariance

Pre-averaging estimate of high dimensional integrated covariance matrix with noisy and asynchronous high-frequency data

2018 ◽  
Vol 07 (03) ◽  
pp. 1850005 ◽  
Author(s):  
Zhi Liu ◽  
Xiaochao Xia ◽  
Guoliang Zhou
Keyword(s):  
Covariance Matrix ◽  
High Frequency ◽  
Rapid Development ◽  
Global Market ◽  
High Frequency Data ◽  
High Dimensional ◽  
Frequency Data ◽  
Microstructure Noise ◽  
Matrix Estimation ◽  
Integrated Covariance

With rapid development of the global market, the number of financial securities has significantly grown, which greatly challenges the measuring of financial quantities. Among others, the estimation of covariance matrix which plays an important role in risk management becomes no longer accurate. In this paper, we consider the estimation of integrated covariance matrix of semi-martingales under framework of high dimension by using high frequency data. We assume that the multivariate asset prices are observed asynchronously and all the observed prices are contaminated by microstructure noise. We employ the pre-averaging method to remove the microstructure noise and the generalized synchronization method to deal with the non-synchronicity. Moreover, to avoid the inconsistency in the high-dimensional covariance matrix estimation, we propose a regularized estimate. The consistency under matrix [Formula: see text]-norm is established. Compared to existing results, our estimator improves the accuracy of the estimation. Finally, we assess the theoretical results via some simulation studies.

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