Moving Average-Based Estimators of Integrated Variance

Peter Hansen, Jeremy Large and Asger Lunde ()

Econometric Reviews, 2008, vol. 27, issue 1-3, 79-111

Abstract: We examine moving average (MA) filters for estimating the integrated variance (IV) of a financial asset price in a framework where high-frequency price data are contaminated with market microstructure noise. We show that the sum of squared MA residuals must be scaled to enable a suitable estimator of IV. The scaled estimator is shown to be consistent, first-order efficient, and asymptotically Gaussian distributed about the integrated variance under restrictive assumptions. Under more plausible assumptions, such as time-varying volatility, the MA model is misspecified. This motivates an extensive simulation study of the merits of the MA-based estimator under misspecification. Specifically, we consider nonconstant volatility combined with rounding errors and various forms of dependence between the noise and efficient returns. We benchmark the scaled MA-based estimator to subsample and realized kernel estimators and find that the MA-based estimator performs well despite the misspecification.

Keywords: Bias correction; High-frequency data; Integrated variance; Moving average; Realized variance; Realized volatility (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (54)

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DOI: 10.1080/07474930701853640

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