Synthetic Underlying
Last updated
Last updated
Weather has a very large effect on the global economy. The ability to trade weather through derivatives offers many possibilities for companies to hedge their adverse weather exposure.
As a standalone asset class that cannot be manipulated, weather derivatives offer a powerful and unique opportunity for speculators and portfolio managers to diversify their risks.
A large problem trading desks have when dealing with weather derivatives is that the weather variable does not present in a way that makes their effect on (or correlation with) other traded assets very clear.
For example, here is a historical price chart for natural gas:
Yet, here are some of the weather variable charts used:
Being a very seasonal variable, it is very difficult to see how weather anomalies directly affect traditional assets that move more like a random walk.
WeatherMage brings a unique method of viewing weather derivatives to a trader's arsenal. By building on the concept of the augmented average, presenting how this average moves through time gives a new perspective on the weather variable itself.
Each point on the main screen map is a tradable location. When a trader clicks on a point, an information screen pops up.
On this screen, the synthetic underlying is presented in the lower right graph. Its construction is very intuitive:
Before the season starts, it begins with the unbiased average. On the graph, the unbiased average is the horizontal gray line.
As the season starts, the system will continuously import actual degree day readings for all of the traded cities. The daily differences between the actual data and the unbiased average are then accumulated and added to the original unbiased average. This is then plotted through time, alongside the original unbiased average.
The result is a chart that moves like a random walk, similar to the other traded markets. This is the gold line in the chart above. In this example, it is clear that CAT in Seoul, South Korea, underperformed in the first month, but has since recovered and is now slightly above the unbiased average.
Another example of the synthetic underlying's usefulness is tracking weather anomalies occurred in the May-Sep 2024 season where the majority of India experienced a significant heat wave.
The following is the information panel for New Delhi, India as of Jul 12, 2024:
Notice how the synthetic underlying increased consistently and dramatically as a result of these temperatures.
If the trader were to click on the "Deviations" menu item from the front page, this heat wave can also be seen on the map (red rectangle around India).
As the legend shows, these heat circles are two-plus (2+) standard deviation heat events above the unbiased average.
By learning to read WeatherMage synthetic underling, alongside the deviation plots on the main map, weather traders can easily see trends in the weather anomalies in the current season.
Combining this with the difference plots on the side panels can provide a powerful trade timing tool against correlated cities/regions, as well as other markets.
