Design-free estimation of integrated covariance matrices for high-frequency data

Cheng Liu, Moming Wang and Ningning Xia

Journal of Multivariate Analysis, 2022, vol. 189, issue C

Abstract: The presence of microstructure noise presents a major challenge for estimating the integrated covariance matrices based on high-frequency data. In this paper, we introduce a new method for estimating the integrated covariance matrices that exploits a nonlinear-shrinkage estimation strategy in the high-dimensional setting and a de-noising method in the univariate case. Our estimator is design-free: no structure assumptions are made on the volatility matrix process. We also show that our proposed estimator not only is asymptotically positive definite, but also enjoys a certain desirable estimation efficiency. At last, simulations and financial applications show that the our estimator performs well in comparison with other existing methods.

Keywords: High-dimension; High-frequency; Integrated covariance matrix; Microstructure noise; Random matrix theory (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:189:y:2022:i:c:s0047259x21001810

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DOI: 10.1016/j.jmva.2021.104910

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