Generalised autoregressive models with conditional heteroscedasticity: An application to financial time series modelling
University of Woollongong
Business and Public Management
Accounting, Finance and Business Economics
This article considers a new class of time series models generated by GAR (generalised autoregressive) models with conditional heteroscedasticity. In particular this class combines the popular GAR models with GARCH ( generalised autoregressive conditional heteroscedastic) models. Some statistical properties of the underlying process are given. An algorithm for parameter estimation based on the MLE procedure is given. A simulation study is presented to compare the finite sample properties of parameter estimates. An example is added to illustrate the theory.