The realized GARCH framework is extended to incorporate the realized range, and the intra-day range, as potentially more efficient series of information than re- alized variance or daily returns, for the purpose of volatility and tail risk forecasting in a financial time series. A Bayesian adaptive Markov chain Monte Carlo method is employed for estimation and forecasting. Compared to a range of well known parametric GARCH models, predictive log-likelihood results across six market in- dex return series favor the realized GARCH models incorporating the realized range. Further, these same models also compare favourably for tail risk forecasting, both during and after the global financial crisis
