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Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise Author info | Abstract | Publisher info | Download info | Related research | Statistics OLE E. BARNDORFF-NIELSEN
PETER REINHARD HANSEN
ASGER LUNDE
NEIL SHEPHARD
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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 the optimal rate, m^1/4, where m is the number of intraday returns.
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Paper provided by Oxford Financial Research Centre in its series OFRC Working Papers Series with number
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Date of creation: 2004Date of revision:
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Find related papers by JEL classification: C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
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