Statistical models can be used to characterize numerical data so as to understand its behavior and pattern. Gold price model, for example, can give signals to investors as to when they should enter and/or exit the market. To find an appropriate gold price model, it is crucial to choose a model that reflects the pattern of the price movement so as to make the model fit and adequate. This study examines the performances of ARIMA-TGARCH with five innovations in modeling and forecasting gold prices. The innovations considered include Gaussian, Student's-t, skewed Student's-t, generalized error distribution and skewed generalized error distribution. Using daily gold price data from the years 2003 to 2014, this study concluded that a hybrid ARIMA(0,1,0)-TGARCH(1,1) with t-innovation was the best model due to the existence of leverage effect and heavier tail characteristics in the data.

Gold has been considered a safe return investment because of its characteristic to hedge against inflation. As a result, the models to forecast gold must reflect its structure and pattern. Gold prices follow a natural univariate time series data and one of the methods to forecast gold prices is Box-Jenkins, specifically the autoregressive integrated moving average (ARIMA) models. This is due to its statistical properties, accurate forecasting over a short period of time, ease of implementation and able to handle nonstationary data. Despite the fact that ARIMA is powerful and flexible in forecasting, however it is not able to handle the volatility and nonlinearity that are present in the data series. Previous studies showed that generalized autoregressive conditional heteroskedatic (GARCH) models are used in time series forecasting to handle volatility in the commodity data series including gold prices. Hence, this study investigate the performance of hybridization of potential univariate time series specifically ARIMA models with the superior volatility model, GARCH incorporates with the formula of Box-Cox transformation in analyzing and forecasting gold price. The Box-Cox transformation is used as the data transformation due to its power in normalizing data, stabilizing variance and reducing heteroskedasticity.