Seasonality

As we discussed earlier, energy markets are inherently seasonal in nature. There are
a number of ways to deal with this from fitting a sin/cosin curve to ‘normalization’
factors. The challenge is that no year is ‘normal’, which means that last year will be
very different from next year. At least 10–20 years’ data is needed to see any normal
trend. Unfortunately, in most cases, the power markets are not mature enough to
provide adequate pricing data.
Given this lack of data, the alternative is to try to model spot price volatility as a
function of weather data, which can then be cast into a normal year. Such models
can be useful providing the underlying production costs are similar in the study
years. Unfortunately this is seldom the case.
Seasonality can present itself in a number of forms.
Ω An increase in the volatility of the entire curve during certain times of the year
Ω An increase in a segment of the curve during certain times
Ω Parts of the curve always being more volatile.
Unfortunately, in power, we see all three effects.
Two approaches can provide useful outcomes. First, adjusting volatility for monthly
seasonal factors can provide useful results in most of the oil and gas markets. This
deals with the fact that the entire curve tends to be more volatile at certain times of
the year. Second, treating each month as an independent price variable can provide
more useful results in the power market, and to a lesser extent, in gas. This allows
for the fact that, say, summer is generally more volatile than spring and that their
volatilities are normally independent.
This latter approach abandons the use of a single forward curve for the actively
traded forward markets (up to 24 months), given the lack of storage and therefore
correlation linkage between forward months (or quarters). The linkages between the
months can then be rebuilt through a correlation matrix in the same way as linkages
are drawn between different products. This brings with it significant data and sizing
issues. Solving these seasonality issues is key to effective volatility estimation in
energy.