Estimation of Mining Project Values Through Real Option Valuation Using a Combination of Hedging Strategy and a Mean Reversion Commodity Price
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Cash flows generated from mining projects are typically highly volatile and significantly influenced by a number of exogenous factors including commodity price as one of the most influential uncertainties. In addition, mining projects are complex and many of their executed investment decisions are irreversible. Therefore, management needs to address this potential risk exposure before making an investment decision. Due to the deterioration and fluctuation of mineral commodity prices for a successful mining project acquisition or development, an important and appropriate investment strategy should include a hedging strategy for reducing potential losses suffered by a company. The discounted cash flow methods, which are commonly used to calculate mining project values, often fail to respond to this identified economic uncertainty and also to incorporate de-risking hedging strategies. Therefore, this study approximates the numerical value or value ranges of a mining project considering the combination of a mean reverting commodity price and hedging strategies using continuous time modeling. A novel time-dependent partial differential equation has been proposed using a continuous time, mean reverting model, and hedging strategy to approximate the mining project value. Application of a new real options valuation technique demonstrated its superiority by providing the advantage of mitigating financial losses and procuring financial gains. In this study, some key results are deferral option and expansion option enhanced the maximum values of the project which are, respectively, 2.51 % and 4.4 % compared to the base case. Furthermore, the country risk has a great impact on project values, as when we considered the country risk premium is zero in our model, the project value increases up to 0.97 %.
KeywordsReal option Hedging Futures contract Partial differential equation Finite difference method
The first acknowledgement goes to Curtin University who provided research scholarship for this research. We also acknowledge the anonymous journal reviewers for their helpful comments and suggestions, and also the editor- in-chief of the journal Dr. John Carranza, for his valuable comments and editing of the earlier version of this manuscript which greatly helped to improve this paper.
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