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27 Exhibit I-30. Accuracy of heating oil futures prices as a function of volatility. 3.00 Forward 12-Month Futures/Spot Ratio 2.50 Feb-99 2.00 1.50 1.00 0.50 0.00 -75% -50% -25% 0% 25% 50% 75% 100% Volatility (% Chg in HO Spot Price vs. 12 Months Ago) upper and lower bounds based on the range of observed his- is somewhat smaller when (absolute) volatility is smaller (in torical changes in the models' explanatory variables. Focusing the 25% to +25% range). During times of high volatility, the on heating oil futures and Energy Information Administration shaded confidence band gets larger, as would be expected (EIA) projections of future GDP, an analysis was undertaken (beyond 25% and +25%). The empirical confidence bands to assess how prior volatility affects the accuracy of futures shown in Exhibit I-30 are embedded in the software to projections over the past 20 years. allow the user to quickly define lower and upper bound sce- For heating oil futures, monthly data of 12-month-ahead narios for the price of jet fuel based on recent observed futures prices from August 1990 through February 2009 were price volatility. examined.13 Exhibit I-30 relates the accuracy of these futures A corresponding analysis was undertaken for EIA projec- prices (relative to the actual spot prices 12 months later) to tions of GDP growth.14 But in this case, there are many fewer recent volatility as measured by the percentage change in the projections compared to the heating oil projections (annual spot price over the prior 12 months. A futures price exactly only from 1994 on), and they are spread out over one to five hitting the 12-month-ahead spot price would be indicated by years ahead. An analysis of these data indicated that the over- points exactly at 1.00 on the vertical axis. all error range of the projections relative to the actual was On the horizontal axis, points to the left of zero indicate fairly evenly spread within ±2 percentage points regardless of falling spot heating oil prices over the past 12 months, and the number of years ahead being forecast or the magnitude of points to the right indicate rising prices. For example, the recent volatility in the data series. Consequently, the ±2 point point identified as February 1999 on the chart reflects a year- range is embedded in the software for purposes of defining ahead spot price (for February 2000) that significantly exceeded lower and upper bound scenarios for local income growth for the February 1999 12-month futures price as measured on the all future projection years. vertical axis (93.72 cents per gallon vs. 38.83 cents); this was partially a reflection of the fact that spot prices had declined 4.2 Airport Outreach by more than 31 percent (measured on the horizontal axis) between the 12-month period from February 1998 to Febru- An important part of the research project was to reach out to ary 1999. airport sponsors and operators to get feedback about how use- The shaded area represents an approximate 90 percent con- ful the software might be to their activity forecastdependent fidence band based on the observed data points and indicates that the range of uncertainty for heating oil futures projections 14 Projections of local per capita income (the metric used in the air service mod- els) for the five-year period from 2010 through 2014 could not be obtained. Instead, it is assumed that local income changes are likely to follow national 13 Until 2007, futures contracts for heating oil were traded only for periods of 18 trends as measured by the EIA national projections of GDP. But unlike the months ahead and shorter. Currently the maximum forward period is 36 monthly heating oil projections, EIA's annual GDP projections are available for months. The analysis described here is based on 12-month-ahead contracts, several years into the future; thus, the analysis for GDP is based on projections which have been actively traded for many years. from one to five years ahead.
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28 decision making and how the software tool itself could be Institutional factors are very important, particularly for improved. Valuable feedback was obtained from representa- smaller airports (e.g., AIP funding). tives of five different airports--two medium hub commercial Impacts may be different at airports that have signifi- airports, two small hub airports, and one non-hub airport. In cant non-aviationrelated revenue sources. addition, the project panel included several industry profes- · Practical usefulness of the software that was developed sionals who provided direct feedback from presentations Program appears to be easy to use, given its relatively made during the work effort. Finally, the project team made a narrow focus. presentation at the Airport Finance and Administration Con- Ability to view and compare historical data is useful. ference held by the Southeast Chapter of the American Asso- User should be reminded that many other factors may ciation of Airport Executives (AAAE) held in February 2010. affect airport activity and revenues. The feedback fell into two major categories: Results appear to come from a black box; user would have to read report to understand how the underlying · Overall usefulness of assessing how airports deal with statistical model works. uncertainty Limitations of TAF are shown clearly, which is useful to How can a simple model accurately gauge uncertainty airport planners. at specific airports? (Every airport is different.) In practice, airport decision making is often reactive, A number of useful revisions and enhancements were made to not proactive or forward-looking. the software based on this feedback, which also led the project Effect of fuel prices on airports depends primarily on panel to recommend that the scope and focus of the software airline reactions, which in turn are very dependent on be kept fairly narrow and straightforward. For the software to many factors, including carrier financial strength, mar- be truly useful to its intended audience, a fine line had to be ket competition, fleet composition, network effects, fuel followed to ensure that it did not overwhelm the end user or hedging strategies, etc. require a significant learning curve.