In this paper we introduce a latent variable based model for the dynamics of financial range, the stochastic
conditional range (SCR). We propose to estimate its parameters by Kalman filter, indirect inference and simulated
maximum likelihood depending on the hypotheses on the distributional form of the innovations. The model is
applied to a large subset of the S&P 500 components. A comparison of its fitting and forecasting abilities with the
conditional autoregressive range (CARR) model shows that the new approach can provide a competitive alternative