"Measuring and forecasting financial variability using realised variance with and without a model" Ole E. Barndorff-Nielsen The Centre for Mathematical Physics and Stochastics (MaPhySto), University of Aarhus, Ny Munkegade, DK-8000 Aarhus C, Denmark. Bent Nielsen Nuffield College, University of Oxford, Oxford OX1 1NF, U.K. Neil Shephard Nuffield College, University of Oxford, Oxford OX1 1NF, U.K. Carla Ysusi Department of Statistics, University of Oxford, South Parks Road, Oxford OX1 3TG, U.K. We use high frequency financial data to proxy, via the realised variance, each day's financial variability. Based on a semiparametric stochastic volatility process, a limit theory shows you can represent the proxy as a true underlying variability plus some measurement noise with known characteristics. Hence filtering, smoothing and forecasting ideas can be used to improve our estimates of variability by exploiting the time series structure of the realised variances. This can be carried out based on a model or without a model. A comparison is made between these two methods. Keywords: Kalman filter; Mixed Gaussian limit; OU process; Quadratic variation; Realised variance; Realised volatility; Square root process; Stochastic volatility.