  This paper analyzes the benefits of sampling intraday returns in intrinsic
time for the standard and pre-averaging realized variance (RV) estimators. We
theoretically show in finite samples and asymptotically that the RV estimator
is most efficient under the new concept of realized business time, which
samples according to a combination of observed trades and estimated tick
variance. Our asymptotic results carry over to the pre-averaging RV estimator
under market microstructure noise. The analysis builds on the assumption that
asset prices follow a diffusion that is time-changed with a jump process that
separately models the transaction times. This provides a flexible model that
separately captures the empirically varying trading intensity and tick variance
processes, which are particularly relevant for disentangling the driving forces
of the sampling schemes. Extensive simulations confirm our theoretical results
and show that realized business time remains superior also under more general
noise and process specifications. An application to stock data provides
empirical evidence for the benefits of using realized business time sampling to
construct more efficient RV estimators as well as for an improved forecasting
performance.
