IN-SAMPLE ASYMPTOTICS AND ACROSS-SAMPLE EFFICIENCY GAINS FOR HIGH FREQUENCY DATA STATISTICS

We revisit in-sample asymptotic analysis extensively used in the realized volatility literature. show that there are gains to be made estimating current from considering realizations prior periods. The weighting schemes also relate Kalman-Bucy filters, although our approach is non-Gaussian and model-free. derive theoretical results for a broad class of processes pertaining volatility, higher moments, leverage. paper contains Monte Carlo simulation study showing benefits across-sample combinations.