GARCH models, short for generalized autoregressive conditional heteroskedasticity models, have been widely used in financial and econometric modeling and analysis since the 1980s. These models are characterized by their ability to capture volatility clustering, and they are widely used to account for nonuniform variance in time-series data.
Effective approaches to modeling and analyzing univariate GARCH processes include:
Additional time-series capabilities to consider for modeling stochastic processes include:
For more information, see Financial Toolbox™.