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Mixed interval realized variance: A robust estimator of stock price volatility

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  • Sutton, Maxwell
  • Vasnev, Andrey L.
  • Gerlach, Richard

Abstract

An ex post volatility estimator, called mixed interval realized variance (MIRV), is proposed. The estimator uses high-frequency price data to provide measurements robust to the idiosyncratic noise of stock markets caused by the bid-ask bounce. The theoretical properties of the new volatility estimator are illustrated and compared with those of the two canonical realized measures: realized volatility and realized range. A simulation study adds to this comparison and highlights some favorable robustness properties of the new estimator when subject to market microstructures. The main finding is that mixed interval realized variance is robust to the presence of microstructures, but inconsistent in the hypothetical ideal scenario. The empirical illustration features Australian stocks from the ASX 20 and provides evidence that for a number of stocks the mixed interval realized variance is competitive with other realized measures under predictive likelihood when it is included in a Realized GARCH model.

Suggested Citation

  • Sutton, Maxwell & Vasnev, Andrey L. & Gerlach, Richard, 2019. "Mixed interval realized variance: A robust estimator of stock price volatility," Econometrics and Statistics, Elsevier, vol. 11(C), pages 43-62.
    • Handle: RePEc:eee:ecosta:v:11:y:2019:i:c:p:43-62
    DOI: 10.1016/j.ecosta.2018.06.001
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    References listed on IDEAS

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    More about this item

    Keywords

    Volatility; Robust estimator;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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