"Regular and Modified Kernel-Based Estimators of Integrated
 Variance: The Case with Independent Noise"
	
	
OLE E. BARNDORFF-NIELSEN
   University of Aarhus
   Centre for Mathematical Physics and Stochastics
			  
PETER REINHARD HANSEN
   Stanford University
  
ASGER LUNDE
   Aarhus School of Business
   Department of Information Science
  
NEIL SHEPHARD
   Nuffield College - University of Oxford

		
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 the optimal rate, m^1/4, where m is the
 number of intraday returns.


JEL Classification: C13, C22