Blending Curves in the Power Market: Data in Action
We recently held one of our “Data in Action” webinars, alongside one of ZE’s newest data partners, Tullett Prebon Information (TPI), and we highlighted curve blending using U.S. East PJM and Mid-Columbia (Mid-C) power pricing as market data examples. TPI is a leading provider of independent and impartial real-time price information across the global Over-The-Counter (OTC) financial and commodity markets. It is the market data division of Tullett Prebon, one of the world’s leading interdealer brokers that acts as an intermediary between participants in the wholesale financial markets.
TPI provides a broad set of data fields on over 35,000 securities traded in the OTC markets on a live, intraday, end-of-day, and historical basis. The ZEMA Suite allows users to collect TPI’s data reports, validate them, and administer them to the correct people within an organization. Using ZEMA’s powerful analytical tools, users can manipulate the data using a range of techniques including filtering, aggregation, weighting, interpolation, extrapolation, and curve manipulations and then use the findings to support various stages of the investment process.
For the purpose of this webinar, we focused on TPI’s energy offering. For our first example we took a closer look at the U.S. East power pricing and selected the mid-price report for PJM’s ATC (Availability Transfer Capability). Blending the monthly, quarterly, seasonal, and annual contracts in PJM into one single curve requires time and attention to detail. ZEMA eliminates this challenge by automating the process and applying a blended forward curve formula that can be weighted for when there are over-lapping contracts and values. As you can see in the table below, if the quarterly and seasonal contracts are the only two contract values available, ZEMA allows us to choose how we would like to weight those contracts.
We also demonstrated ZEMA’s priority blending formula that allows us to determine which data source we’d like to prioritize over another. For example, if there is no value in data series a, then we’d prioritize to choose data series b. If there is no value in data series b, then choose data series c, etc.
In our third example, we showed how ZEMA can take longer contracts (such as calendar years) and, utilizing historical data, break them down into quarterly contracts. We built a Mid-C Peak annual curve out to 2022 and, in order to infer the quarterly pricing, we used historical ICE day ahead power going back to 2009 (See Figure 3).
To see what the quarterly price has been for Mid-C for the last three years, we used a shape average of that historical price using ZEMA’s shape average formula (Figure 4). We then applied that against the straight average to see the shape of the historical price in comparison to the average, and applied that to the forward curves to get the implied quarterly price (Figure 5).
The final example shown during the webinar demonstrated ZEMA’s ability to share intellectual property across an organization using the ZEMA Data Direct Microsoft Excel Add-In. Data Direct imports and embeds one or many queries into a spreadsheet and allows users to refresh the data automatically in order to leverage pre-existing functions to call analysis from users’ own Excel VBA code.
At ZE, we run these webinars to offer attendees a glimpse into the advanced data management and analytical capabilities of the ZEMA Suite, while showcasing the range of market data available from our key partners. To see when our next public webinar is, check our webinar calendar.
For more information on TPI’s offering you can contact Christopher McGuigan at email@example.com