Moving Average-Based Estimators of Integrated Variance |
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Authors: | Peter R Hansen Jeremy Large Asger Lunde |
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Institution: |
a Department of Economics, Stanford University, Stanford, California, USA
b All Souls College, University of Oxford, Oxford, UK
c Department of Marketing and Statistics, Aarhus School of Business, University of Aarhus, Aarhus V, Denmark |
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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. |
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Keywords: | Bias correction High-frequency data Integrated variance Moving average Realized variance Realized volatility |
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