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    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

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Weighted Least Squares Realized Covariation Estimation

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Weighted Least Squares Realized Covariation Estimation. / Li, Yifan; Nolte, Ingmar; Vasios, Michalis et al.
In: Journal of Banking and Finance, Vol. 137, 106420, 30.04.2022.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Li, Y, Nolte, I, Vasios, M, Voev, V & Xu, Q 2022, 'Weighted Least Squares Realized Covariation Estimation', Journal of Banking and Finance, vol. 137, 106420. https://doi.org/10.1016/j.jbankfin.2022.106420

APA

Li, Y., Nolte, I., Vasios, M., Voev, V., & Xu, Q. (2022). Weighted Least Squares Realized Covariation Estimation. Journal of Banking and Finance, 137, [106420]. https://doi.org/10.1016/j.jbankfin.2022.106420

Vancouver

Li Y, Nolte I, Vasios M, Voev V, Xu Q. Weighted Least Squares Realized Covariation Estimation. Journal of Banking and Finance. 2022 Apr 30;137:106420. Epub 2022 Mar 3. doi: 10.1016/j.jbankfin.2022.106420

Author

Li, Yifan ; Nolte, Ingmar ; Vasios, Michalis et al. / Weighted Least Squares Realized Covariation Estimation. In: Journal of Banking and Finance. 2022 ; Vol. 137.

Bibtex

@article{f0f7ea6489cf405ca4633b22eac2d3bd,
title = "Weighted Least Squares Realized Covariation Estimation",
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.",
keywords = "Market Microstructure Noise, Realized Volatility, Realized Covariation, Weighted Least Squares, Volatility Forecasting, Asset Allocation",
author = "Yifan Li and Ingmar Nolte and Michalis Vasios and Valeri Voev and Qi Xu",
note = "This is the author{\textquoteright}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",
year = "2022",
month = apr,
day = "30",
doi = "10.1016/j.jbankfin.2022.106420",
language = "English",
volume = "137",
journal = "Journal of Banking and Finance",
issn = "0378-4266",
publisher = "Elsevier",

}

RIS

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 -