A Model of Canadian Oil and Gas Price Fluctuations

Abstract

Abstract The biggest uncertainty in oil and gas economics is the commodity price. To determine this information, many experts produce very detailed price forecasts. These forecasts tend to follow very smooth trends. Unfortunately, both recent and past events have shown that hydrocarbon prices can change very rapidly. As well, several competing trends can be found in oil and gas prices. Long-term historical data indicate that hydrocarbon prices tend to revert back to historical averages. However, short-term price fluctuations are unpredictable. To address this need, a price fluctuation model has been developed for the Canadian oil industry. A random walk model with mean-reversion was developed and tuned to fit Canadian hydrocarbon prices. Starting with the current spot price, the model will generate a random but equiprobable prediction of future prices. The model can be used as input into a Monte-Carlo simulation. Alternately, the model can be run multiple times in order to generate "high," "low," and "expected" price predictions. Introduction In recent years, there have been dramatic swings in the price of crude oil and natural gas commodities. Between January 1, 1999 and January 1, 2004, the nominal monthly average price of light oil delivered to refineries in Edmonton, Alberta has varied between CDN$17 and CDN$54. Though these price extremes are not unprecedented, the speed with which prices dropped, then rebounded, have surprised many analysts. Even ignoring the recent swings in prices, Plourde and Watkins(1) found that oil prices are among the most volatile of all commodities. These price swings make it very difficult for an analyst to determine the economic viability and risk of a proposed investment. This is because the price of oil and gas over the first five years or so of a project often determines the project's overall economic success. However, quantitative predictions of spot oil and gas prices are quite unreliable past three months into the future. Options and hedging strategies can offload some of the uncertainty, but they must be valued. And, to evaluate an option, one must understand the price behaviour of the underlying asset. Analysts have devoted much time and effort to better understand oil and gas price fluctuations. In general, this effort has been directed towards several benchmark prices. The most widely studied of these benchmark prices is the West Texas Intermediate (WTI). This is a light, sweet oil, normally priced for delivery to Cushing, Oklahoma. Another commonly studied benchmark is the Brent Blend, which is priced for delivery at the Sullom Voe Terminal in Scotland. Often, other energy commodities are priced in relation to these benchmarks. For example, the price of light oil to be delivered to refineries in Edmonton is often assumed to be about CDN$1/bbl less than the WTI price. Due to the maturity of the markets trading these two commodities, both spot and futures price data have been available for a large number of years. Using this data, a tremendous number of structural and statistical models have been developed for WTI and Brent prices.