Managing natural resource projects requires that future costs and revenues be forecasted. Most commodity pricing models are fairly simple, involving a slow, steady increase in base prices while including volatility. When analyzing stock prices, this pattern is commonly referred to as the 'random walk'. Complicating the forecasting process is the fact that many commodities exhibit mean reverting tendencies, where prices may increase or decrease, but tend to revert to a long-term mean. Stock price volatility is measured using the standard deviation of the rate of return of a stock. Commodity price volatility is the same; however, when mean reversion exists, the normal standard deviation will overestimate true volatility. This complicates the pricing of many types of derivatives that are based on commodity prices. This work investigates the mean reversion tendency of oil prices. Specifically , a 25 year database of West Texas Intermediate daily oil prices is analyzed to determine price volatility, mean reversion speed, and the adjusted volatility that should be applied to today's oil-related projects
