Lenders know there are billions of dollars of weak financial assets in the market, such as securities backed by bad mortgages. The problem is no one knows who is exposed at what level to those...
When the Price Is Right
How to measure hedging effectiveness and regulatory policy.
instrument not designated as an FAS 133 hedging instrument are recognized in current period earnings. Under hedge accounting, however, the combined profit and loss from the derivative and the hedged item are recognized in earnings in the same accounting period. In other words, hedging is not simply a matter of efficiently procuring energy supplies going into a winter season. It has implications on a utility’s financial statement (reported earnings) because not every item is marked to market in the current period.
The FAS 133 "80/125 Rule” judges a hedge effective if the ratio of the change in derivative value to hedged item values is between 80 and 125 percent. Many hedges fail this test under stable market conditions, and would not be recognized as effective for financial reporting purposes.
It’s best to consider FAS 133 hedge effectiveness tests as gatekeeper tests controlling access to financial statements. While FAS 133 hedge effectiveness may tell us something about the hedge instrument, so-called “effective hedges” may not dampen volatility. And FAS 133 tells us nothing about optimizing performance. Effectiveness and risk-capital efficiency are separable issues.
All of this leaves CFOs and regulators with a practical problem: When presented a collection of “effective hedges” within a variety of possible portfolios, which portfolio should be chosen?
There are many risk-based metrics for ordering portfolio preferences. Some companies use value at risk (VaR), which has its own problems. They examine how the previous day’s closing prices affect their portfolio and adjust their positions so as to satisfy supply requirements within a VaR limit established by risk management. Portfolios that reduce performance or increase VaR are less desirable compared to those that increase performance or reduce VaR.
In many cases, this strategy is crudely implemented through a pseudo “delta neutral” hedging strategy. A “delta neutral” hedging strategy seeks risk-neutral portfolios on the naïve assumption that energy markets are perfect, liquid markets unaffected by the effects of option pricing. Of course, naïve assumptions are not realistic, and unbiased, pseudo “delta normal” hedging practices are unlikely to be risk neutral. Worse, they can even be risk enhancing for many portfolios and scenarios.
Moreover, when presented with the dual problem of improving performance and reducing VaR, which goals dominate, by how much, and measured over what period? There needs to be more structure and rigor in methods used to measure risk and procedures employed to select risk-efficient portfolios.
Recall that regulatory policy seeks to maximize the cost savings in supply procurement while minimizing the risk. Proper implementation of this policy consequently means that utilities need a structured, documented process for selecting a mathematically optimized portfolio from among an extraordinary number of possible portfolios.
Just so we’re clear, optimization does not include running a program some number of times and performing a visual inspection for the “optimal” case. Unfortunately, life is not that easy.
Fortunately, whatever challenges exist in creating and managing business processes, implementing mathematical models with the requisite sophistication is much less an issue these days. A variety of standard stochastic optimization methods ( e.g., branch-and-bound, Benders decomposition, Lagrangian relaxation,