Despite the variable nature of the resource, wind can be managed so that it will not impair the reliability of a utility system. The Federal Energy Regulatory Commission proposed a rule that would...
Three-Dimensional Price Forecasting
extent to which these three market factors move together has a significant impact on the overall value of such a contract. If correlation is low, the spread will increase and decrease more than if the markets were highly correlated and this would elevate the value of the contract. Therefore, three-dimensional price forecasting must incorporate the level of correlation among all relevant markets. Instead of examining the distribution of a single market factor, all three markets must be examined simultaneously, as in Table 4.
Basically, it is a three-dimensional price cube. The dimensions: 1) tenor of future time; 2) number of markets; and 3) number of scenarios.
Imagine if you had this information. Not only would you be able to see what your “forecasters” thought about prices over the next year, but you also would be able to apply a distribution around this forecast and examine the simultaneous set of prices from other markets.
Why is this a benefit?
Of course, energy merchant have assets, load commitments, and trades in many locations across the regions they serve. Therefore, this set of three prices becomes a much larger set of market factors. Table 5 illustrates the three-dimensional data cube concepts for a multi-regional utility. In this case, powers and fuels must be simulated simultaneously for a large number of regions.
Now that this concept is defined, the data cube concept can be generalized to describe any number of markets, any number of simulations across any time period. The problem is now one of size rather than complexity.
The three-dimensional data cube incorporates expectation, uncertainty, and the interrelationships between markets that enables the analyst to run a wide range of extended analysis in the areas of valuations, risk measurement and portfolio optimization.
• Traders would be able to assess whether or not their view of the market “tradeably” differs from the current, executable forward markets.
• Business-development managers would be able to measure the extrinsic option value embedded in the contracts they were bidding on.
• Asset managers would be able to value and measure the risk on their generation assets and tolling contracts as well as pipelines and transportation contracts.
• With the addition of optimization tools, analysts would be able to value gas storage.
• Senior executives would be able to look across their entire business and understand both the expected value and the corresponding risk of each component, as well as the entire risk-return profile of the entire portfolio.
Every firm with these types of exposures should develop some form of three-dimensional pricing to augment the existing point forecasts. Only with this information can a firm fully understand the risk and opportunities embedded in its business.