Validates a modified version of the long-term trend reverting jump and dip diffusion model for forecasting commodity prices and estimates the gold price for the next 10 years using historical monthly data.
Investigates several commodities’ co-movements, such as Aluminium, Copper, Lead, Nickel, Tin, and Zinc, at different time frequencies, and uses a bias-corrected average forecast method proposed by Issler and Lima [43] to give combined forecasts of these metal commodities employing RMSE as a measure of forecasting accuracy.
Models palm oil prices using the Autoregressive Distributed Lag (ARDL) model and compares its forecasting accuracy with the benchmark model ARIMA. It uses an ARDL bound-testing approach to co-integration in order to analyse the relationship between the price of palm oil and its determinant factors.
Models gold prices using the Ornstein-Uhlenbeck Process (OUP) to account for a potentially existent long-term trend in a Real Option Valuation of a mining project.