Abstract
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.
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Acknowledgements
We would like to thank the organizers and participants of the 23rd International Conference on Computational Statistics (Iasi, August 28–39, 2018) for fruitful discussions. Computational resources were supplied by the project "e-Infrastruktura CZ" (e-INFRA CZ ID:90140) supported by the Ministry of Education, Youth and Sports of the Czech Republic.
Funding
This research was supported by the Internal Grant Agency of the Prague University of Economics and Business under project F4/21/2018 and the Czech Science Foundation under project 19-02773S.
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Holý, V., Tomanová, P. Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data. Comput Econ 62, 463–485 (2023). https://doi.org/10.1007/s10614-021-10210-w
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DOI: https://doi.org/10.1007/s10614-021-10210-w