  Many methods for estimating integrated volatility and related functionals of
semimartingales in the presence of jumps require specification of tuning
parameters for their use in practice. In much of the available theory, tuning
parameters are assumed to be deterministic and their values are specified only
up to asymptotic constraints. However, in empirical work and in simulation
studies, they are typically chosen to be random and data-dependent, with
explicit choices often relying entirely on heuristics. In this paper, we
consider novel data-driven tuning procedures for the truncated realized
variations of a semimartingale with jumps based on a type of random fixed-point
iteration. Being effectively automated, our approach alleviates the need for
delicate decision-making regarding tuning parameters in practice and can be
implemented using information regarding sampling frequency alone. We show our
methods can lead to asymptotically efficient estimation of integrated
volatility and exhibit superior finite-sample performance compared to popular
alternatives in the literature.
