Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -