This paper analyzes a generalized class of flat-top realized kernels for
estimation of the quadratic variation spectrum in the presence of a
market microstructure noise component that is allowed to exhibit both
endogenous and exogenous $\alpha$-mixing dependence with polynomially
decaying autocovariances. In the absence of jumps, the class of flat-top
estimators are shown to be consistent, asymptotically unbiased, and
mixed Gaussian with the optimal rate of convergence, $n^{1/4}$. Exact
bounds on lower order terms are obtained using maximal inequalities and
these are used to derive a conservative MSE-optimal flat-top shrinkage.
In a theoretical and/or a numerical comparison with alternative
estimators, including the realized kernel, the two-scale realized
kernel, and a proposed robust pre-averaging estimator, the flat-top
realized kernels are shown to have superior bias reduction properties
with little or no increase in finite sample variance.