Efficient and positive semidefinite pre-averaging realized covariance estimator

Liang Ching Lin, Ying Chen, Guangming Pan, Vladimir Spokoiny

Research output: Contribution to journalArticlepeer-review


We propose a realized-covariance estimator based on efficient multiple pre-averaging (EMP) for asynchronous and noisy high-frequency data. The EMP estimator is consistent, guaranteed to be positive-semidefinite, and achieves the optimal convergence rate at n1/4. It is constructed based on 1) an innovative synchronizing technique that uses all available price information, and 2) an eigenvalue correction method that ensures positive-semidefiniteness without sacrificing the optimal convergence rate. A simulation study demonstrates the good performance of the EMP estimator for finite samples in terms of accuracy, properties, and convergence rate. In a real-data analysis, the EMP covariance estimator delivers performance that is more stable than that of alternative estimators. The new estimator also outperforms alternative realized-covariance estimators in terms of portfolio selection.

Original languageEnglish
Pages (from-to)1441-1462
Number of pages22
JournalStatistica Sinica
Issue number3
Publication statusPublished - 2021 Jul

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Efficient and positive semidefinite pre-averaging realized covariance estimator'. Together they form a unique fingerprint.

Cite this