Jaime Casassus

Optimal Exploration Investments under Price and Geological-Technical Uncertainty: A Real Options Model

This article develops a real options model for valuing natural resource exploration investments (e.g. oil or copper) when there is joint price and geological-technical uncertainty. After a successful several-stage exploration phase, there is a development investment and an extraction phase. All phases are optimised contingent on price and geological-technical uncertainty. Several real options are considered. There are flexible investment schedules for all exploration stages and a timing option for the development investment. Once the mine is developed, there are closure, opening and abandonment options for the extraction phase. Our model maintains a relatively simple valuation structure by collapsing price and geological-technical uncertainty into a one-factor model. A computational implementation of the model applied to a copper exploration prospect shows that a significant fraction of total project value is due to the operative, the development and the exploration options available to project managers.

Optimal timing of a mine expansion: Implementing a real options model

We present the results of implementing a real options model for valuing an investment project that expands production capacity and/or modifies unit costs of a copper mine. The model and its implementation addresses the three requirements we find necessary to increase the use of the real option methodology by the practitioner community: a user-acceptable stochastic model for commodity prices with mean reversion, a customized real asset model which includes the main managerial flexibilities of opening-closing production or delaying investments, and a user-friendly computer implementation. A case study shows that a significant fraction of investment value may be due to the flexibility of delaying investment, value that decreases as copper prices increase. Critical investment prices are analyzed.

Chapter 9 – Optimal exploration investments under price and geological—technical uncertainty: a real options model

Publisher Summary This chapter presents a real options model for valuing natural resource exploration investments when there is joint price and geological–technical uncertainty. Price risk refers to output market value, while geological–technical risk applies to reserves, development investments, and cost structure. By collapsing both sources of uncertainty, price and geological–technical uncertainty, into a one-factor model for expected value is able to maintain model simplicity, while retaining operational flexibility. The model considers that the exploration investment schedule may be stopped or resumed at any moment depending on cash flow expectations, which depend on current commodity price and geological–technical expectations. Once all exploration phases are concluded, the project is modeled as having the flexibility of postponing development investments and, once developed, as having the option to close or reopen production. Results for a copper exploration prospect show that a significant fraction of total project value is due to the operative, the development, and the exploration options available to project managers.

Short-horizon return predictability and oil prices

This paper shows that oil price changes, measured as short-term futures returns, are a strong predictor of excess stock returns at short horizons. Ours is a leading variable for the business cycle and exhibits low persistence which avoids the fictitious long-horizon predictability associated with other predictors used in the literature. We compare our variable with the most popular predictors in a sample period that includes the recent financial crisis. Our results suggest that oil price changes are the only variable with forecasting power for stock returns. This significant predictive ability is robust against the inclusion of other variables and out-of-sample tests. We also study the cross-section of expected stock returns in a conditional CAPM framework based on oil price shocks. Our model displays high statistical significance and a better fit than all the conditional and unconditional models considered, including the Fama–French three-factor model. From a practical perspective, ours is a high-frequency, observable variable that has the advantage of being readily available to market-timing investors.

Consumption and Hedging in Oil‐Importing Developing Countries

We study the consumption and hedging strategy of an oil†importing developing country that faces multiple crude oil shocks. In our model, developing countries have two particular characteristics: their economies are mainly driven by natural resources and their technologies are less efficient in energy usage. The natural resource exports can be correlated with the crude oil shocks. The country can hedge against the crude oil uncertainty by taking long/short positions in existing crude oil futures contracts. We find that both inefficiencies in energy usage and shocks to the crude oil price lower the productivity of capital. This generates a negative income effect and a positive substitution effect, because today’s consumption is relatively cheaper than tomorrow’s consumption. Optimal consumption of the country depends on the magnitudes of these effects and on its risk†aversion degree. Shocks to other crude oil factors, such as the convenience yield, are also studied. We find that the persistence of the shocks magnifies the income and substitution effects on consumption, thus also affecting the hedging strategy of the country. The demand for futures contracts is decomposed in a myopic demand, a pure hedging term and productive hedging demands. These hedging demands arise to hedge against changes in the productivity of capital due to changes in crude oil spot prices. We calibrate the model for Chile and study to what extent the country’s copper exports can be used to hedge the crude oil risk.

Maximal Affine Models for Multiple Commodities: A Note

This paper extends the maximal affine models of single assets to a multi-commodity setup. We show that the correlated version of maximal affine models for a single commodity is no longer maximal for multiple commodities. In the maximal model, the convenience yield of a certain commodity could depend on the prices of other commodities, which is consistent with the structural model in our companion paper Casassus, Liu, and Tang (2012). Furthermore, the maximal model can offer a new feedback (error-correction) effect among commodity prices, which is consistent with many empirical studies.