Many existing studies have documented that daily realized volatility estimates based on intraday data provide volatility forecasts that are superior to forecasts constructed from daily data only. Some studies also find that density forecasts based on realized volatility are superior to those based on daily data. We investigate whether these forecasting improvements translate into economic value added. In order to address this question we develop a new class of discrete-time option valuation models that use daily returns as well as realized volatility, and that nest the daily Heston and Nandi (2000) GARCH model as a special case. We derive closed-form option valuation formulas and we assess the option valuation properties using S&P500 return and option data. We find that realized volatility reduces the pricing errors of the benchmark model significantly across moneyness, maturity and volatility levels.