Properties of Bias Corrected Realized Variance in Calendar Time and Business Time

In this paper I study the statistical properties of a bias corrected realized variance measure when high frequency asset prices are contaminated with market microstructure noise. The analysis is based on a pure jump process for asset prices and explicitly distinguishes among different sampling schemes. Two important findings emerge from the theoretical and empirical analysis. Firstly, business time sampling is generally superior to the common practice of calendar time sampling in terms of MSE of realized variance. Secondly, a first order bias correction to realized variance allows for the efficient use of much higher frequency data and results in substantial improvements of the statistical properties of realized variance. For IBM transaction data, I estimate a 12 second optimal sampling frequency and a drop of more than 65% in bias and MSE when a first order bias correction is applied.