Estimation of high-dimensional integrated covariance matrix based on noisy high-frequency data with multiple observations

DOI10.1016/J.SPL.2020.108996zbMath1461.62073OpenAlexW3107232020MaRDI QIDQ2657980

Ningning Xia, Moming Wang

Publication date: 18 March 2021

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2020.108996

zbMATH Keywords

random matrix theoryhigh-dimensionalnonlinear shrinkagemultiple transactionsintegrated covariance matrix

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to actuarial sciences and financial mathematics (62P05) Random matrices (probabilistic aspects) (60B20)

Related Items (1)

Asymptotic normality of Nadaraya–Waton kernel regression estimation for mixing high-frequency data

Cites Work

• Nonlinear shrinkage estimation of large-dimensional covariance matrices
• On the estimation of integrated covariance matrices of high dimensional diffusion processes
• Eigenvectors of some large sample covariance matrix ensembles
• Spectral analysis of large dimensional random matrices
• On the inference about the spectral distribution of high-dimensional covariance matrix based on high-frequency noisy observations
• Microstructure noise in the continuous case: the pre-averaging approach
• Large Covariance Estimation by Thresholding Principal Orthogonal Complements

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