There’s just no stopping it. The capital amassed by private takeover firms is simply overwhelming. Any reasonable person could conclude that public utilities face wholesale changes in terms of...
Business & Money
value of spread options and transmission products may be overestimated. Similarly, the viability of interregional hedges may be either understated or overstated if other stochastic pricing assumptions are used.
The Valuation Model
Once you have the necessary weather, generation, and load data in place, you need software that can process that data in a rigorous, consistent, and market-relevant fashion. First and foremost, you need to ensure that all transactions are valued using the same underlying assumptions regarding price distributions. Transactions valued outside the core system ignore the inherent correlation effects with the remainder of the portfolio. This severely limits the quality of risk analysis, particularly Value-at-Risk and sensitivity analysis.
Power traders know that traditional parametric-based position estimation and valuation methods are inappropriate for electricity markets, particularly when risk is at its highest. Valuation and position estimation methodologies must be able to smoothly transition from a marginal cost pricing paradigm into a more volatile paradigm. When sufficient excess generation capability exists, price-stack-based economics work fairly well because the supply curve is fairly elastic. However, once load begins to approach total generation capacity, the potential for much more dramatic increases in price exist for small increases in demand. Valuation methodologies must accommodate the greater likelihood of price spikes to occur simultaneously with load spikes.
When implementing a valuation methodology, it is also important to ensure serial or chronological integrity. Stochastic processes that simulate pricing paradigm shifts are inferior to prices derived from actual weather patterns because they are incapable of modeling path-dependent characteristics of the market.
For a real-world example, consider the many position- and price-related effects of a sustained period of high temperatures. Chronologically based algorithms are capable of handling the progressive pressure on prices, loads, generation efficiency, and transmission availability as a heat wave persists. Conversely, stochastic methods fail to do so, or must be modified by less precise and arbitrary factors in an attempt to represent these phenomena.
Possibly the most important consideration in valuation computation is keeping up with the incredibly fast pace of power trading. Valuation algorithms should be designed to generate accurate results with fewer computations. The need for a scalable solution-one that can process the enormous volumes of data required to accurately value positions with hourly granularity and serve up results in a real-time trading environment-is obvious. And, of course, throwing hardware and memory at a problem is no substitute for robust, efficient algorithms that provide far greater increases in performance. Using smart algorithms enables a dramatic reduction in processing needs-far less than 1 percent of a naïve brute-force approach. Moreover, the final crucial step in implementing an optimal position and risk reporting system is to design a reporting structure capable of delivering real-time summarized and detailed risk metrics to the user.
There are sound business reasons why traders and risk managers will want to see their data aggregated/disaggregated in multiple views depending upon the particular task at hand. Hourly traders are rarely concerned with financial or forward positions, while daily traders look only a few days ahead. It is common for term traders to