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

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  • Liu, Cheng
  • Wang, Moming
  • Xia, Ningning

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.

Suggested Citation

  • Liu, Cheng & Wang, Moming & Xia, Ningning, 2022. "Design-free estimation of integrated covariance matrices for high-frequency data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:jmvana:v:189:y:2022:i:c:s0047259x21001810
    DOI: 10.1016/j.jmva.2021.104910
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