Using a cycle reverting price process in modeling metal mining project profitability

The purpose of this paper is to demonstrate how managerial estimates of long-term market price trends can be included into investment analysis of metal mining. The inclusion of subjective market information with a new cycle reverting price process is proposed.

Subjective managerial estimates are included into stochastic metal price modeling by defining separately the following parameters of each price cycle phase: approximated length, approximated long-term price level and volatility. An net present value-based investment analysis model is applied together with Monte Carlo simulation.

It is plausible to combine managerial estimates about metal price trends and cycles with stochastic modeling for shorter term and to include the information into investment analysis. The results show that the difference between the proposed process and the commonly used mean reverting process is remarkable in terms of decision-making implications.

The proposed method allows the inclusion of more relevant information into the metal price modeling used in mining investment analysis. Results suggest that the cyclical nature of metal prices affects project value of metal mining projects, and it should be considered when making irreversible investment decisions. The proposed method can be generalized for any cyclical processes.