Hybrid SARIMA-GARCH Model for Forecasting Indian Gold Price

A hybrid model has been considered an effective way to improve the forecast accuracy.
This paper proposes the hybrid model of the linear seasonal autoregressive moving average
(SARIMA) and the non-linear generalized autoregressive conditional heteroscedasticity
(GARCH) in modeling and forecasting the Indian gold price. The goodness of fit of the
model is measured using Akaike information criteria (AIC), while the forecasting
performance is assessed using root mean square error (RMSE), mean absolute Error
(MAE) and mean absolute percentage error (MAPE). The study concluded that SARIMAGARCH
is a more appropriate model forecasting Indian gold price. The analysis is carried
out by using the R (3.2.1)-software.