Recently it has become popular to measure daily variance using the summation of squared intraday returns, called realized variance. Realized variance renders a much more efficient estimator of daily volatility than the daily squared return. Parkinson (1980) showed that this also holds for the range between high and low prices observed during a day. However, Parkinson's argument applies to intervals of any length, hence also to intraday intervals. As such it is possible to improve upon the realized variance estimator by replacing each intraday squared return with the high-low range. We will call the resulting estimator of daily volatility the realized range. Of course in Merton's utopia (continuous trading, no market frictions) realized variance can be computed from infinitely small intervals, and then there is no point to use realized range. However, in the presence of market microstructure noise such as non-trading or bid-ask bounce, it will be of importance that the realized range estimator can achieve the same efficiency as realized variance using a lower sampling frequency (or achieve a higher efficiency for the same sampling frequency). Our simulation experiments show that for plausible market frictions the optimal realized range has a lower MSE than the optimal realized variance (optimal indicates we choose the best sampling frequency given the market frictions). The empirical analysis of S&P500 futures prices as well as the prices of the constituents of the S&P100 confirm the potential of the realized range.