The petroleum industry has recognized that a consistent probabilistic approach provides improved understanding and insights into the investment decisions. Yet, although most oil & gas companies appreciate the impact of commodity prices on the value of their potential investments, few are implementing price models at the level of probabilistic sophistication and realism of their, say, subsurface models.
We illustrate the implementation and calibration of the two-factor stochastic price model (Schwartz and Smith, 2000) that allows mean-reversion in short-term price deviations and uncertainty in the long-term equilibrium level. It provides advantages over more basic methods but is still simple enough to be communicated to corporate decision makers. The balance between realism and ease of communication of the model has led us to choose this model in favor of one-factor models, which assume that only one source of uncertainty contributes to the uncertainty in prices, or other multi-factor models where two or more factors contributes to the uncertainty in prices.
Previously, a Kalman filter was used to estimate the model parameters based on historical spot and futures prices. We illustrate how current market information (such as futures prices and options on futures observed in commodity futures exchanges) can be utilized to assess the parameters of the two-factor price model. As opposed to the Kalman filter technique, the implied approach to parameter estimation is easy and intuitive, and it will generate estimates that are good enough for most valuation assessments.