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This paper analyzes the benefits of sampling intraday returns in intrinsictime for the standard and pre-averaging realized variance (RV) estimators. Wetheoretically show in finite samples and asymptotically that the RV estimatoris most efficient under the new concept of realized business time, whichsamples according to a combination of observed trades and estimated tickvariance. Our asymptotic results carry over to the pre-averaging RV estimatorunder market microstructure noise. The analysis builds on the assumption thatasset prices follow a diffusion that is time-changed with a jump process thatseparately models the transaction times. This provides a flexible model thatseparately captures the empirically varying trading intensity and tick varianceprocesses, which are particularly relevant for disentangling the driving forcesof the sampling schemes. Extensive simulations confirm our theoretical resultsand show that realized business time remains superior also under more generalnoise and process specifications. An application to stock data providesempirical evidence for the benefits of using realized business time sampling toconstruct more efficient RV estimators as well as for an improved forecastingperformance.

Efficient Sampling for Realized Variance Estimation in Time-Changed Diffusion Models