A time series model in which the random error is conditionally heteroscedastic with respect to its past realizations. This model is used to describe volatility clustering, i.e. a pattern observed in many financial data where large and small forecast errors appear to occur in clusters. The simplest form is ARCH (1),
yt = βxt + εt
where and ut has a standard normal distribution.