Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Banking and Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Banking and Finance, 137, 106420, 2022 DOI: 10.1016/j.jbankfin.2022.106420
Accepted author manuscript, 3.12 MB, PDF document
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Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Weighted Least Squares Realized Covariation Estimation
AU - Li, Yifan
AU - Nolte, Ingmar
AU - Vasios, Michalis
AU - Voev, Valeri
AU - Xu, Qi
N1 - This is the author’s version of a work that was accepted for publication in Journal of Banking and Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Banking and Finance, 137, 106420, 2022 DOI: 10.1016/j.jbankfin.2022.106420
PY - 2022/4/30
Y1 - 2022/4/30
N2 - 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.
AB - 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.
KW - Market Microstructure Noise
KW - Realized Volatility
KW - Realized Covariation
KW - Weighted Least Squares
KW - Volatility Forecasting
KW - Asset Allocation
U2 - 10.1016/j.jbankfin.2022.106420
DO - 10.1016/j.jbankfin.2022.106420
M3 - Journal article
VL - 137
JO - Journal of Banking and Finance
JF - Journal of Banking and Finance
SN - 0378-4266
M1 - 106420
ER -