This paper presents a comprehensive framework for integrating high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower-frequency volatility and return distributions. The authors argue that traditional parametric multivariate ARCH or stochastic volatility models, which are often used for financial asset return volatility, correlations, and distributions, are restrictive and complex, and perform poorly at intraday frequencies. Instead, they propose using realized volatility, which is constructed from high-frequency intraday returns, to allow for the use of traditional time series procedures. The paper develops the theoretical links between the conditional covariance matrix and realized volatility, based on continuous-time arbitrage-free price processes and quadratic variation theory. Using data from the Deutschemark/Dollar and Yen/Dollar spot exchange rates, the authors find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform significantly better than those from popular daily ARCH models. Additionally, the vector autoregressive volatility forecast, combined with a parametric lognormal-normal mixture distribution, yields well-calibrated density forecasts of future returns and accurate quantile estimates. The results have implications for practical modeling and forecasting of large covariance matrices relevant in asset pricing, asset allocation, and financial risk management.This paper presents a comprehensive framework for integrating high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower-frequency volatility and return distributions. The authors argue that traditional parametric multivariate ARCH or stochastic volatility models, which are often used for financial asset return volatility, correlations, and distributions, are restrictive and complex, and perform poorly at intraday frequencies. Instead, they propose using realized volatility, which is constructed from high-frequency intraday returns, to allow for the use of traditional time series procedures. The paper develops the theoretical links between the conditional covariance matrix and realized volatility, based on continuous-time arbitrage-free price processes and quadratic variation theory. Using data from the Deutschemark/Dollar and Yen/Dollar spot exchange rates, the authors find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform significantly better than those from popular daily ARCH models. Additionally, the vector autoregressive volatility forecast, combined with a parametric lognormal-normal mixture distribution, yields well-calibrated density forecasts of future returns and accurate quantile estimates. The results have implications for practical modeling and forecasting of large covariance matrices relevant in asset pricing, asset allocation, and financial risk management.