Segilola gold mine valuation using Monte Carlo simulation approach
- PDF / 742,681 Bytes
- 8 Pages / 595.276 x 790.866 pts Page_size
- 8 Downloads / 260 Views
ORIGINAL PAPER
Segilola gold mine valuation using Monte Carlo simulation approach Chima C. Ugwuegbu
Received: 4 November 2012 / Accepted: 7 March 2013 # Springer-Verlag Berlin Heidelberg 2013
Abstract The Segilola gold mine has been valued in this work to determine its viability using Monte Carlo simulation approach. To achieve this, a base case discounted cash flow (DCF) model was developed for the project from which sensitivity analysis was conducted to determine the value drivers in the project. Using Palisade's @Risk 6.0 software and setting suitable probability distribution function for the value drivers of the project, Monte Carlo simulation was used to calculate the statistical forecast of the project value. The base case DCF analysis gave a negative net present value (NPV) of US$−7.46 million. The sensitivity analysis showed that the input parameters: gold price, gold grade, strip ratio, and operating costs are the value drivers of the project. A mean NPV of US$30.79 million was obtained for the project with a standard deviation of US$58.26 million after running the Monte Carlo simulation. The simulation result also showed that the probability of achieving positive NPV (profit) in the project is 69.2 % and probability of loss of money is 30.8 %. Keywords Segilola gold mine . Valuation . Monte Carlo simulation . Project . Prefeasibility . Feasibility . Uncertainty
Introduction Mineral property valuations, according to Frimpong (1992), are used to highlight the value, viability, and inherent uncertainty of a project. Valuation involves the analysis and synthesis of various input data to produce a value estimate of the project. An element of uncertainty exists in each of the input data as they are not definitive but rather an estimate that a valuer puts to C. C. Ugwuegbu (*) Department of Materials and Metallurgical Engineering, Federal University of Technology, P. M. B. 1526, Owerri, Nigeria e-mail: [email protected]
represent what he deems to be the best in the given situation. As the input data are uncertain, it implies that the output value of the mine project is equally uncertain. According to Mallinson and French (2000), due to this uncertainty, it is argued that a mine project valuation advice must increasingly incorporate a measure of the uncertainty so as to be more meaningful and informative to the user. Conventional valuation approaches such as the discounted cash flow (DCF) technique fail to capture some of the uncertainties in the input data. According to Said and Daud (2005), valuers have often come under attack for the lack of “accuracy” in their recommended value. There is therefore the need to use better valuation approaches that can capture the uncertainties in all the input data, especially for mine projects in the prefeasibility stage in which there is high level of uncertainty. One valuation approach that can achieve this is the Monte Carlo simulation technique. Monte Carlo simulation is the extended DCF valuation technique which takes the uncertain nature of project input variables
Data Loading...
