Three-Dimensional Price Forecasting


Using the past, present, and future to optimize our understanding of today’s energy markets.

Using the past, present, and future to optimize our understanding of today’s energy markets.
Fortnightly Magazine - April 2007

Price forecasting is a significant business process within any energy merchant that trades electricity and natural gas. Business planning, trading, mergers and acquisitions (M&A), even rate-case activities rely upon some type of a price forecast as the foundation to analysis.

The problem with a single forecast is that it never is correct. As soon as the forecast is complete, the world changes and the information becomes dated and often even irrelevant.

This article reviews two common approaches used to forecast prices and introduces the concept of “three-dimensional price forecasting.” Incorporating these other dimensions changes the environment in a structural way.

Econometric vs. Fundamental Forecasting Methods

Energy-price forecasting employs two families of analysis. One is grounded in market based econometrics; the other relies on supply-and-demand fundamentals. Each method requires a deep set of experience and knowledge in very different areas.

Proponents of the two methods reveal an interesting lack of overlap. Analysts with a trading background tend to favor the insights derived from analyzing price data, and they migrate toward the econometric method. Those with an operational background like the idea of measuring the intersection between supply and demand, and they predict prices based on marginal costs. This approach is intuitive and maps to how utilities think about their business.

The econometric method presumes that historic sets of market prices describe some structural behavior that will be repeatable in the future. Analysts focus on statistical attributes of prices. Geometric Brownian Motion, mean reversion, and seasonality are intermixed with tools and methods that include regime switching, ARIMA, co-integration, GARCH, and principal component analysis. Sophisticated and rigorous statistical analysis leads to a better view of the future—or so they would like you to believe.

The fundamental forecasting method attempts to use a number of observable supply-and-demand factors to predict the expected price of electricity over both short- and long-term time periods. Factors include:

• Current generation units within a specific region stack from the lowest to highest marginal costs. These costs are driven primarily by a fuel cost, but they also include operating costs, start-up costs, and operational constraints;

• Generation outages;

• Transmission congestion;

• Customer demand estimated seasonally across the year; and

• Plant additions and retirements.

Experienced forecast managers realize that both approaches fail to predict the future. Markets are simply too dynamic. If the manager is operationally rigorous, he or she should create a quantitative function that includes two equally weighted analyst groups that use these methods competitively. Then the manager can let his or her analysts duke it out for supremacy. Competition is the best way to bring out innovation and creative thought.

Volatility and Correlation

All of the hard work described above simply gives you the “expectation”— the first dimension of the three-dimensional price process. But it is incomplete and inadequate if you fail to add the other two dimensions of “uncertainty” and “interrelationship” into the analysis. Statisticians refer