We investigate the computational issues related to the memory size in the estimation of quadratic covariation using financial ultra-high-frequency data. In the multivariate price process , we consider both contamination by the market microstructure noise and the non-synchronous observations. We express the multi-scale, flat-top realized kernel, non-flat-top realized kernel, pre-averaging and modulated realized covariance estimators in a quadratic form and fix their bandwidth parameter at a constant value. This allows us to operate with limited memory and formulate such estimation approach as a streaming algorithm. We compare the performance of the estimators with fixed bandwidth parameter in a simulation study. We find that the estimators ensuring positive semidefiniteness require much higher bandwidth than the estimators without such constraint.
We investigate the computational issues related to the memory size in the estimation of quadratic covariation , taking into account the specifics of financial ultra-high-frequency data. In multivariate price processes , we consider both contamination by the market microstructure noise and the non-synchronicity of the observations. We formulate a multi-scale, flat-top realized kernel, non-flat-top realized kernel, pre-averaging and modulated realized covariance estimators in quadratic form and fix their bandwidth parameter at a constant value. This allows us to operate with limited memory and formulate this estimation as a streaming algorithm. We compare the performance of the estimators with fixed bandwidth parameter in a simulation study. We find that the estimators ensuring positive semidefiniteness require much higher bandwidth than the estimators without this constraint.
R
using
, taking into account the specifics of
R
the multivariate price process
multivariate price processes
R
non-synchronous
non-synchronicity of the
R
express the
formulate a
D
a
R
such estimation approach
this estimation
R
such
this