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MINING ORE VALUATION BY REAL OPTION UNDER UNCERTAINTY AND RISK Ph.D Manuel Viera Ceo& Managing Partner Metaproject S.A

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Strategic Mining Investments Are exposed to a high level of uncertainty Big amounts of money on the table Complex capital structures. Political risk and all kinds of financial risk. Flexibility is really important

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Open Pit at Codelco´s Andina Division

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Zaldivar Open Pit (Placer Dome) and Escondida Open Pit (BHP Billiton)

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Blast at Codelco´s Chuquicamata Mine

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How big are the known reserves? What is the level of confidence? What is the level of risk associated with? Which is the expected return? When ore deposit is economically attractive, arise a series of questions such as:

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Value Quality and Risk How increase NPV? EVA QualityRisk Added Asegura Income Costs ++++ ---- NPV Expected value La Gestión de Calidad y Riesgo se justifica si el beneficio es mayor a su costo

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DECISION MAKING HEURÍSTICA DE DECISIÓN - RISKMANAGEMENT a) Costo Incertidumbre = 0 (p) NPV (+) b) Hay Costo Incertidumbre (Mayoría de los Proyectos Mineros) c) Costo Incertidumbre = Flujos de Caja Futuros 0 E(NPV) Fig. D.6 (p) VAN (+)0E(NPV) (-) P(Sucessf ull) Costo Incertidumbre P(reje ct) (p) NPV (+)0- E(NPV) (-) Costo Incertidumbre P(fracaso)

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Evolution of Valuation Methods DCF analysis was introduced in the 1950’s and first applied to petrochemical projects. Prior to that the Payback method prevailed. Sensitivity and Scenario analysis were developed at the US Air Force and the first corporate use occurred at Shell later in that decade. The advent of computers brought simulation methods in the 1960’s and decision trees. Option theory was developed in 1973, and applications to real assets occurred a few years later.

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Evolution of Valuation Methods DCF Sensitivity Analysis Simulation Decision Trees Option Pricing Real Option Valuation NPV IRR Value of Information Strategic Considerations Impact of Variables Risk Management Risk Analysis CAPM ’s1960’s1970’s1980’s Source:Brandao

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Alternative methods to price real options Analitic (closed formula) models like Black- Scholes:Big biases because their assumptions are unrealistic Traditional decision trees. Binomial models (lattices).-Good alternative but we need to know the binomial process parameters for the underlying project. Montecarlo simulation.- Maybe a black box

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Mining Projects Integral Evaluation Methodology through Real Options Step 1: Review and Define the Mining Business Strategy Step 2: Determine Geological Uncertainty Level. Step 3: Perfect Information Level and Information Quality Step 4: Mining Resources Cubication and Valuation. Step 5: Determination of Engineering Level (Information Quality). Step 6: Calculation of Optimal Production Rate. Step 7: Metals and main Supplies Price Projection. Step 8: Determination of the Project’s Risks and Dangers. Step 9: The Normal Portfolio of Projects, to determine which candidates will be evaluated by Real Options Step 10: Scenarios and variables taken into account to Evaluate Projects. Step 11: Definition of the Decision Trees with Flexibilities. Step12: Selection of the Strategic Flexibilities and Options

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ROV VALUE FOR PROJECT CHARACTERISTICS FLEXIBILITYUNCERTAINTY LOW UNCERTAINTY HIGH Moderate Value High Value LOWLow ValueModerate Value

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Strategic Flexibilities and Options in a Mining Project Option to Postpone the Decision Option of Expansion Option of Closure Option of Knowledge Option of Technological Package Selection Option of Optimal Production Rate Selection Option of Product Quality Improvement Option to anticipate with the Mining Infrastructure Preparation and Development Option of Selection of the Exploitation Method

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METAL PRICE SIMULATION MODEL

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Brownian Motion with Jumps Model (Discrete Time Version).

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Jumps in Copper Price Movements

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Forecast Oil Price ??? Fuente: U.S. Department of Energy, Año 1982 Tendencia prevista Actual Dolares por Barril

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CASE STUDY

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Mining Modeling

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Optimum rate production

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Alway there are Risk anyplaces Please give me the dry device

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RESULTS Other Case

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RESULTS

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Mina Cinabrio- C.M. Punitaqui Metaproject S.A

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Mining Plan 2011 Nv-370 Nv-350 Nv-330 Nv-305 Nv-275 Nv-245 Nv-220 Nv-190 Nv-160 Nv-135 Nv-115 Nv-80 Nv-50 Nv-25 C-13 C-17 C-34 C-35 C-30 C-31 C-32 C-33 C-24C-23 C-27C-26 C-25 C-28 C-14 C-21C-20C-19 C-22 C-16C-15C-18 C-29

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Main conclusions

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MAIN CONCLUSIONS The Latin Hypercube or Montecarlo Simulation with reversion to the mean has resulted to be a good copper and molybdenum price forecast model, allowing a better valuation of the mining property project. The NPV criterion is rigid and doesn’t allow visualizing the strategic value of mining flexibilities. The Option to expand has resulted as the best decision. The DELPHI/HAZOP experts panel is a good method to determine the uncertainty level of the ore deposit, in order to apply the Options theory.

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