  We formulate a discrete-time Bayesian stochastic volatility model for
high-frequency stock-market data that directly accounts for microstructure
noise, and outline a Markov chain Monte Carlo algorithm for parameter
estimation. The methods described in this paper are designed to be coherent
across all sampling timescales, with the goal of estimating the latent
log-volatility signal from data collected at arbitrarily short sampling
periods. In keeping with this goal, we carefully develop a method for eliciting
priors. The empirical results derived from both simulated and real data show
that directly accounting for microstructure in a state-space formulation allows
for well-calibrated estimates of the log-volatility process driving prices.
