Gold-Copper Mining Investment Evaluation Through Multivariate Copula-Innovated Simulations

Risk assessment of mining projects is a requirement in the mineral industries. In this process, many risk variables are time-dependent, and the only available data are historical time series. Moreover, in the case of a multivariate scenario, conventional forecasting methods fail to capture conditional dependency across the variables, which is important when there is an underlying causal relationship that needs to be modeled for accurate project evaluation. Thus, we investigated the use of copulas to capture the conditional distribution of the factors involved in a mine risk assessment study. We employed a multivariate copula-based time-series approach to model several uncertain variables. The Autoregressive Fractionally Integrated Moving Average - Generalized Autoregressive Conditional Heteroscedasticity (ARFIMA-GARCH) model was used for the conditional mean and copulas were used to model the error distribution, thus capturing the collective variation and dependence pattern across the variables. The method was implemented to model gold prices, copper prices, and the 10-year US Treasury bond yields and to determine the project's net present value and probability of being economically feasible. The proposed approach can be used for cases where simulation of multivariate time-series is conducted.

Automated summary

The study explored the use of copulas to capture the conditional distribution of factors in mine risk assessment. A multivariate copula-based time-series approach was employed to model uncertain variables, utilizing the ARFIMA-GARCH model for conditional mean and copulas for error distribution to capture collective variation and dependence patterns.