  Mounting empirical evidence suggests that the observed extreme prices within
a trading period can provide valuable information about the volatility of the
process within that period. In this paper we define a class of stochastic
volatility models that uses opening and closing prices along with the minimum
and maximum prices within a trading period to infer the dynamics underlying the
volatility process of asset prices and compares it with similar models that
have been previously presented in the literature. The paper also discusses
sequential Monte Carlo algorithms to fit this class of models and illustrates
its features using both a simulation study and data form the SP500 index.
