Forecasting the Price of Gold

This paper seeks to evaluate the appropriateness of a variety of existing forecasting techniques (17 methods) at providing accurate, and statistically significant forecasts for gold price. We report the results from the 9 most competitive techniques. Special consideration is given to the ability of these techniques at providing forecasts which outperforms the random walk as we noticed that certain multivariate models (which included prices of silver, platinum, palladium and rhodium, besides gold) were also unable to outperform the random walk in this case. Interestingly, the results show that none of the forecasting techniques are able to outperform the random walk at horizons of 1 and 9 steps ahead, and on average the Exponential Smoothing model is seen providing the best forecasts in terms of the lowest root mean squared error over the 24 months forecasting horizons. Moreover, we find that the univariate models used in this paper are able to outperform the Bayesian autoregression, and Bayesian vector autoregressive models, with exponential smoothing (ETS) reporting statistically significant results in comparison to the former models, and classical autoregressive and the vector autoregressive models in most cases.