Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise

Neil Shephard

No 2004-FE-20, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: We consider kernel-based estimators of integrated variances in the presence of independent market microstructure effects. We derive the bias and variance properties for all regular kernel-based estimators and derive a lower bound for their asymptotic variance. Further we show that the subsample-based estimator is closely related to a Bartlett-type kernel estimator. The small difference between the two estimators due to end effects, turns out to be key for the consistency of the subsampling estimator. This observation leads us to a modified class of kernel-based estimators, which are also consistent. We study the efficiency of our new kernel-based procedure. We show that optimal modified kernel-based estimator converges to the integrated variance at rate m1/4, where m is the number of intraday returns.

Keywords: Quadratic Variation; Market Microstructure Noise; Integrated Variance; Kernel-Based Realized Variance; Realized Variance; Realized Volatility (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
Date: 2004-11-01
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Citations: View citations in EconPapers (27)

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