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Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data

Author

Listed:
  • Vladimír Holý

    (Prague University of Economics and Business)

  • Petra Tomanová

    (Prague University of Economics and Business)

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.

Suggested Citation

  • Vladimír Holý & Petra Tomanová, 2023. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 463-485, June.
    • Handle: RePEc:kap:compec:v:62:y:2023:i:1:d:10.1007_s10614-021-10210-w
    DOI: 10.1007/s10614-021-10210-w
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