Volatility is central to financial theory. In practice volatility is not directly observed and must be estimated. Merton?s (1980) seminal work suggests that as the sampling interval approaches zero arbitrarily precise volatility estimates can be obtained. Realistically, however, the limiting case is not attainable since the sampling frequency cannot be any higher than transaction by transaction, and asymptotic results may not provide a good approximation. In this talk, we examine the precision of both the estimates of unconditional daily volatility and ex-post estimates of daily volatility that use the high-frequency data. We derive analytical expressions for the precision of these estimates as functions of the prominent high frequency data characteristics including leptokurtosis, autocorrelation in the returns, deterministic patterns and volatility clustering in intra-day variances. Once these features are accounted for, we find that the benefit of using high-frequency data over daily data is limited. In other words, large amounts of high frequency data do not necessarily translate into very precise volatility estimates.