Modelling and Forecasting of Price Volatility: an Application of GARCH and EGARCH Models

Abstract

This paper has studied the autoregressive integrated moving-average (ARIMA) model, generalized autoregressive conditional heteroscedastic (GARCH) model and exponential GARCH (EGARCH) model along with their estimation procedures for modelling and forecasting of three price series, namely domestic and international edible oils price indices and the international cotton price ‘Cotlook A’ index. The Augmented Dickey-Fuller (ADF) and Philips Peron (PP) tests have been used for testing the stationarity of the series. Lagrange multiplier test has been applied to detect the presence of autoregressive conditional heteroscedastic (ARCH) effect. A comparative study of the above three models has been done in terms of root mean square error (RMSE) and relative mean absolute prediction error (RMAPE). The residuals of the fitted models have been used for diagnostic checking. The study has revealed that the EGARCH model outperformed the ARIMA and the GARCH models in forecasting the international cotton price series primarily due to its ability to capture asymmetric volatility pattern. The SAS software version 9.3 has been used for data analysis.