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Efficient asymptotic variance reduction when estimating volatility in high frequency data

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  • Clinet, Simon
  • Potiron, Yoann

Abstract

This paper shows how to carry out efficient asymptotic variance reduction when estimating volatility in the presence of stochastic volatility and microstructure noise with the realized kernels (RK) from Barndorff-Nielsen et al. (2008) and the quasi-maximum likelihood estimator (QMLE) studied in Xiu (2010). To obtain such a reduction, we chop the data into B blocks, compute the RK (or QMLE) on each block, and aggregate the block estimates. The ratio of asymptotic variance over the bound of asymptotic efficiency converges as B increases to the ratio in the parametric version of the problem, i.e. 1.0025 in the case of the fastest RK Tukey-Hanning 16 and 1 for the QMLE. The impact of stochastic sampling times and jump in the price process is examined carefully. The finite sample performance of both estimators is investigated in simulations, while empirical work illustrates the gain in practice.

Suggested Citation

  • Clinet, Simon & Potiron, Yoann, 2018. "Efficient asymptotic variance reduction when estimating volatility in high frequency data," Journal of Econometrics, Elsevier, vol. 206(1), pages 103-142.
  • Handle: RePEc:eee:econom:v:206:y:2018:i:1:p:103-142
    DOI: 10.1016/j.jeconom.2018.05.002
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    Cited by:

    1. Clinet, Simon & Potiron, Yoann, 2019. "Testing if the market microstructure noise is fully explained by the informational content of some variables from the limit order book," Journal of Econometrics, Elsevier, vol. 209(2), pages 289-337.
    2. Richard Y. Chen, 2018. "Inference for Volatility Functionals of Multivariate It\^o Semimartingales Observed with Jump and Noise," Papers 1810.04725, arXiv.org, revised Nov 2019.
    3. Simon Clinet & Yoann Potiron, 2021. "Estimation for high-frequency data under parametric market microstructure noise," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(4), pages 649-669, August.
    4. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    5. Li, Z. Merrick & Laeven, Roger J.A. & Vellekoop, Michel H., 2020. "Dependent microstructure noise and integrated volatility estimation from high-frequency data," Journal of Econometrics, Elsevier, vol. 215(2), pages 536-558.

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    More about this item

    Keywords

    High frequency data; Jumps; Market microstructure noise; Integrated volatility; Quasi-maximum likelihood estimator; Realized kernels; Stochastic sampling times;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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