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

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  • Wang, Moming
  • Xia, Ningning

Abstract

In this paper, we study the estimation of integrated covariance matrix based on noisy high-frequency data with multiple transactions using random matrix theory. We further prove that the proposed estimator is also asymptotically optimal for portfolio selection.

Suggested Citation

  • Wang, Moming & Xia, Ningning, 2021. "Estimation of high-dimensional integrated covariance matrix based on noisy high-frequency data with multiple observations," Statistics & Probability Letters, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:stapro:v:170:y:2021:i:c:s0167715220302996
    DOI: 10.1016/j.spl.2020.108996
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    References listed on IDEAS

    as
    1. Jianqing Fan & Yuan Liao & Martina Mincheva, 2013. "Large covariance estimation by thresholding principal orthogonal complements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 603-680, September.
    2. Olivier Ledoit & Michael Wolf, 2017. "Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks," The Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4349-4388.
    3. Jacod, Jean & Li, Yingying & Mykland, Per A. & Podolskij, Mark & Vetter, Mathias, 2009. "Microstructure noise in the continuous case: The pre-averaging approach," Stochastic Processes and their Applications, Elsevier, vol. 119(7), pages 2249-2276, July.
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    Cited by:

    1. Jin Yuan & Xianghui Yuan, 2023. "A Best Linear Empirical Bayes Method for High-Dimensional Covariance Matrix Estimation," SAGE Open, , vol. 13(2), pages 21582440231, June.

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