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A bias-corrected estimator of the covariation matrix of multiple security prices when both microstructure effects and sampling durations are persistent and endogenous

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  • Ikeda, Shin S.

Abstract

I propose a bias-corrected non-parametric estimator of the covariation matrix of log security prices, designed as a convex combination of two realized kernels. The estimator is simple but possesses desirable statistical properties including consistency, asymptotic normality and the parametric rate of convergence in the presence of persistent, diurnally heteroskedastic and endogenous microstructure effects. It is robust to the asynchronous trading of multiple securities with persistent and endogenous refresh-time durations. I also prove the consistency of a subsampling-based estimator of the asymptotic covariance matrix of the proposed estimator. In simulations, the non-linear functions of the proposed estimator exhibit smaller bias than those based on a realized kernel, while only slightly increasing the variance. Thereby, the proposed estimator reduces the mean squared error.

Suggested Citation

  • Ikeda, Shin S., 2016. "A bias-corrected estimator of the covariation matrix of multiple security prices when both microstructure effects and sampling durations are persistent and endogenous," Journal of Econometrics, Elsevier, vol. 193(1), pages 203-214.
  • Handle: RePEc:eee:econom:v:193:y:2016:i:1:p:203-214
    DOI: 10.1016/j.jeconom.2016.02.016
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    Cited by:

    1. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
    2. repec:eee:econom:v:208:y:2019:i:1:p:80-100 is not listed on IDEAS
    3. repec:eee:jocoma:v:14:y:2019:i:c:p:16-39 is not listed on IDEAS
    4. repec:eee:econom:v:208:y:2019:i:1:p:43-79 is not listed on IDEAS

    More about this item

    Keywords

    High frequency data; Covariation; Microstructure; Endogenous durations;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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