Home > Research > Publications & Outputs > Estimating the quadratic covariation matrix for...

Links

Text available via DOI:

View graph of relations

Estimating the quadratic covariation matrix for asynchronously observed high frequency stock returns corrupted by additive measurement error

Research output: Contribution to journalJournal article

Published

Standard

Estimating the quadratic covariation matrix for asynchronously observed high frequency stock returns corrupted by additive measurement error. / Park, Sujin; Hong, Seok Young; Linton, Oliver.

In: Journal of Econometrics, Vol. 191, No. 2, 23.12.2015, p. 325-347.

Research output: Contribution to journalJournal article

Harvard

APA

Vancouver

Author

Bibtex

@article{7c344f876e634087b50e6db84be4c0b2,
title = "Estimating the quadratic covariation matrix for asynchronously observed high frequency stock returns corrupted by additive measurement error",
abstract = "This paper studies the estimation problem of the covariance matrices of asset returns in the presence of microstructure noise and asynchronicity between the observations across different assets. Motivated by Malliavin and Mancino (2002, 2009) we propose a new Fourier domain based estimator of multivariate ex-post volatility, which we call the Fourier Realized Kernel (FRK). An advantage of this approach is that no explicit time alignment is required unlike the time domain based methods widely adopted in the existing literature. We derive the large sample properties and establish asymptotic normality of our estimator under some general conditions that allow for both temporal and cross-sectional correlations in the measurement error process. Our results can be viewed as Frequency domain extension of the asymptotic theories for the multivariate realized kernel estimator of Barndorff-Nielsen et al. (2011). We show in extensive simulations that our method outperforms the time domain estimators when two assets with different liquidity are traded asynchronously.",
author = "Sujin Park and Hong, {Seok Young} and Oliver Linton",
year = "2015",
month = dec,
day = "23",
doi = "10.1016/j.jeconom.2015.12.005",
language = "English",
volume = "191",
pages = "325--347",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "2",

}

RIS

TY - JOUR

T1 - Estimating the quadratic covariation matrix for asynchronously observed high frequency stock returns corrupted by additive measurement error

AU - Park, Sujin

AU - Hong, Seok Young

AU - Linton, Oliver

PY - 2015/12/23

Y1 - 2015/12/23

N2 - This paper studies the estimation problem of the covariance matrices of asset returns in the presence of microstructure noise and asynchronicity between the observations across different assets. Motivated by Malliavin and Mancino (2002, 2009) we propose a new Fourier domain based estimator of multivariate ex-post volatility, which we call the Fourier Realized Kernel (FRK). An advantage of this approach is that no explicit time alignment is required unlike the time domain based methods widely adopted in the existing literature. We derive the large sample properties and establish asymptotic normality of our estimator under some general conditions that allow for both temporal and cross-sectional correlations in the measurement error process. Our results can be viewed as Frequency domain extension of the asymptotic theories for the multivariate realized kernel estimator of Barndorff-Nielsen et al. (2011). We show in extensive simulations that our method outperforms the time domain estimators when two assets with different liquidity are traded asynchronously.

AB - This paper studies the estimation problem of the covariance matrices of asset returns in the presence of microstructure noise and asynchronicity between the observations across different assets. Motivated by Malliavin and Mancino (2002, 2009) we propose a new Fourier domain based estimator of multivariate ex-post volatility, which we call the Fourier Realized Kernel (FRK). An advantage of this approach is that no explicit time alignment is required unlike the time domain based methods widely adopted in the existing literature. We derive the large sample properties and establish asymptotic normality of our estimator under some general conditions that allow for both temporal and cross-sectional correlations in the measurement error process. Our results can be viewed as Frequency domain extension of the asymptotic theories for the multivariate realized kernel estimator of Barndorff-Nielsen et al. (2011). We show in extensive simulations that our method outperforms the time domain estimators when two assets with different liquidity are traded asynchronously.

U2 - 10.1016/j.jeconom.2015.12.005

DO - 10.1016/j.jeconom.2015.12.005

M3 - Journal article

VL - 191

SP - 325

EP - 347

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 2

ER -