Stochastic Oil Price Models: Comparison and Impact

A good investment decision-making process is one that is able to assess risk and uncertainty and manage them in a balanced manner. Literature on current oil and gas industry practice indicates that the modeling of uncertainty is largely restricted to reserves while production, cost, and oil price data are all single point deterministic values indicating certainty. The objective of this article is to explore the impact of three stochastic oil price models: geometric Brownian motion (GBM), mean reversion (MR) and mean reversion with jumps, on the uncertainty output of a project's net present value (NPV). Furthermore, it investigates the impact of varying the input parameters of the three stochastic oil price models. This article concludes that range in the project NPV values using the MR with jumps model is far greater than the MR and GBM models. In addition, if the decision is based on the mean value, then the GBM model is a good approximation if the current oil price is close to the long-term price. However, if the decision is based on the standard deviation, GBM does not provide a good approximation. Finally, uncertainty in the volatility of oil price has a significant impact only in the GBM model, but not on the MR or the MR with jumps models.