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A Factor-Based Estimation of Integrated Covariance Matrix With Noisy High-Frequency Data

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  • Yucheng Sun
  • Wen Xu

Abstract

This article studies a high-dimensional factor model with sparse idiosyncratic covariance matrix in continuous time, using asynchronous high-frequency financial data contaminated by microstructure noise. We focus on consistent estimations of the number of common factors, the integrated covariance matrix and its inverse, based on the flat-top realized kernels introduced by Varneskov. Simulation results illustrate the satisfactory performance of our estimators in finite samples. We apply our methodology to the high-frequency price data on a large number of stocks traded in Shanghai and Shenzhen stock exchanges, and demonstrate its value for capturing time-varying covariations and portfolio allocation.

Suggested Citation

  • Yucheng Sun & Wen Xu, 2022. "A Factor-Based Estimation of Integrated Covariance Matrix With Noisy High-Frequency Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 770-784, April.
    • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:2:p:770-784
    DOI: 10.1080/07350015.2020.1868301
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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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