Comparative Analysis of ARMA and GARMA Models in Forecasting

Abstract

In this paper, two traditional Autoregressive Moving Average models and two different Generalised Autoregressive Moving Average models are considered to forecast financial time series. These time series models are fitted to the financial time series data namely Dow Jones Utilities Index data set, Daily Closing Value of the Dow Jones Average and Daily Returns of the Dow Jones Utilities Average Index. Three different estimation methods such as Hannan-Rissanen Algorithm, Whittle’s Estimation and Maximum Likelihood Estimation are used to estimate the parameters of the models. Point forecasts have been done and the performance of all the models and the estimation methods are discussed.