We empirically examine Parkinson's range-based volatility estimate in the federal funds market, which is unique because institutional regulations create a predictable pattern in interday volatility. We find that range-based volatility estimates and standard deviations produce the expected volatility pattern. We also find that at trading pressure points where microstructure noise should be greatest, range-based estimates are less than the standard deviations. Thus, we support the argument that range-based volatility estimates remove the upward bias created by microstructure noise. We find that the Parkinson method is the most efficient range-based volatility measure among a set of alternates in this market.