Several recent studies advocate the use of nonparametric estimators of daily price vari-
ability that exploit intraday information. This paper compares four such estimators, realised
volatility, realised range, realised power variation and realised bipower variation, by examining
their in-sample distributional properties and out-of-sample forecast ranking when the object
of interest is the conventional conditional variance. The analysis is based on a 7-year sample of
transaction prices for 14 NYSE stocks. The forecast race is conducted in a GARCH framework
and relies on several loss functions. The realized range fares relatively well in the in-sample .t
analysis, for instance, regarding the extent to which it brings normality in returns. However,
overall the realised power variation provides the most accurate 1-day-ahead forecasts. Fore-
cast combination of all four intraday measures produces the smallest forecast errors in about
half of the sampled stocks. A market conditions analysis reveals that the additional use of
intraday data on day t .. 1 to forecast volatility on day t is most advantageous when day t is
a low volume or an up-market day. The results have implications for value-at-risk analysis.