Home > Research > Publications & Outputs > Weighted Least Squares Realized Covariation Est...

Electronic data

  • WLS_FINAL

    Accepted author manuscript, 3.12 MB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

Links

Text available via DOI:

View graph of relations

Weighted Least Squares Realized Covariation Estimation

Research output: Contribution to journalJournal articlepeer-review

Forthcoming
Close
Article number106420
<mark>Journal publication date</mark>15/01/2022
<mark>Journal</mark>Journal of Banking and Finance
Publication StatusAccepted/In press
<mark>Original language</mark>English

Abstract

We introduce a novel weighted least squares approach to estimate daily realized covariation and microstructure noise variance using high-frequency data. We provide an asymptotic theory and conduct a comprehensive Monte Carlo simulation to demonstrate the desirable statistical properties of the new estimator, compared with existing estimators in the literature. Using high-frequency data of 27 DJIA constituting stocks over a period from 2014 to 2020, we confirm that the new estimator performs well in comparison with existing estimators. We also show that the noise variance extracted based on our method can be used to improve volatility forecasting and asset allocation performance.