In this paper we present conditional heteroscedasticity models for time-series of discrete price changes in high-frequency financial data. They combine tractability of observation-driven GARCH models of Bollerslev~(1986) with the simplicity of the ordered probit/logit structure of Hausman, Lo and MacKinlay~(1992). In contrast to the ACM model of Russel and Engle~(1998) and the ADS decomposition model of Rydberg and Shephard~(1999), we separate groups of parameters driving conditional mean and conditional variance of the data, allowing us to test the effects of explanatory variables separately on the two moments of high-frequency price changes. We introduce two models belonging to the class outlined above: IV-GARCH model with short-memory volatility dynamics and IV-FIARCH model with long-range dependence in the conditional volatility. Application of the models to IBM trades dataset is provided.