Looking for Accurate Forecasting of Copper TC/RC Benchmark Levels

Table 1

Summary of previous literature research.

AUTHOR	RESEARCH
Shafiee & Topal [17]	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.
Li et al. [18]	Proposes an ARIMA-Markov Chain method to accurately forecast mineral commodity prices, testing the method using mineral molybdenum prices.
Issler et al. [19]	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.
Hamid & Shabri [44]	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.
Duan et al. [45]	Predicts China’s crude oil consumption for 2015-2020 using the fractional-order FSIGM model.
Brennan & Schwartz [46]	Employs the Geometric Brownian Motion (GBM) to analyse a mining project’s expected returns assuming it produces a single commodity.
McDonald & Siegel [47]	Uses GBM to model the random evolution of the present value of an undefined asset in an investment decision model.
Zhang et al. [48]	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.
Sharma [49]	Forecasts gold prices in India with the Box Jenkins ARIMA method.