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Moving Average-Based Estimators of Integrated Variance

Moving Average-Based Estimators of Integrated Variance 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Econometric Reviews Taylor & Francis

Moving Average-Based Estimators of Integrated Variance

Econometric Reviews , Volume 27 (1-3): 33 – Feb 19, 2008
33 pages

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References (40)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1532-4168
eISSN
0747-4938
DOI
10.1080/07474930701853640
Publisher site
See Article on Publisher Site

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.

Journal

Econometric ReviewsTaylor & Francis

Published: Feb 19, 2008

Keywords: Bias correction; High-frequency data; Integrated variance; Moving average; Realized variance; Realized volatility; C10; C22; C80

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