  This paper shows how to carry out efficient asymptotic variance reduction
when estimating volatility in the presence of stochastic volatility and
microstructure noise with the realized kernels (RK) from [Barndorff-Nielsen et
al., 2008] and the quasi-maximum likelihood estimator (QMLE) studied in [Xiu,
2010]. To obtain such a reduction, we chop the data into B blocks, compute the
RK (or QMLE) on each block, and aggregate the block estimates. The ratio of
asymptotic variance over the bound of asymptotic efficiency converges as B
increases to the ratio in the parametric version of the problem, i.e. 1.0025 in
the case of the fastest RK Tukey-Hanning 16 and 1 for the QMLE. The impact of
stochastic sampling times and jump in the price process is examined carefully.
The finite sample performance of both estimators is investigated in
simulations, while empirical work illustrates the gain in practice.
