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I need to de-seasonalize Nat Gas futures data for a project and am hoping to get good suggestions. As we all know natural gas futures are priced higher for the winter months and to analyze/model the term structure we need to de-seasonalize the data.

Any ideas how would one do it?

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    $\begingroup$ Section 5.2.1 of the book "Commodity Option Pricing - A practioner's guide", by Iain Clark, have provided one such approach for deseasonalizing the natural gas forward curves. $\endgroup$ – Gordon Aug 14 '14 at 17:33
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As a starting point to this, determining seasonality for a given market is as follows:

i) Take several years of historical spot price time series, e.g. TTF spot prices. For year $i$ work out a yearly price $p_{yr,i}$ by taking the arithmetic average of daily spot prices. Do the same in respect of month number $j$ of the same year to get a monthly price $p_{mth,i}^{j}$. The monthly shaping factors $f_{i}$ are then $f_{i}=\frac{p_{mth,i}^{j}}{p_{yr,i}}$. Determine the $f_{i}$ for a number of years (where possible i use at least 3, but that is a judgement call), and use their average. As you say, winter will be more expensive, i.e. you expect $f_{i}>1$ for $i\in\{1,2,3,10,11,12\}$ and $f_{i}<1$ for $i\in\{4,5,6,7,8,9\}$

ii) if you need to use daily shaping, you can determine the ratio of weekday prices (numbered 1 to 7) to the monthly prices. This results in 7 weekday factors for each month, i.e. another 84 factors. This is how it is done in electricity, where intraweek shaping is very pronounced. I guess in gas you might find it sufficient to have only two factors per month, one for the workdays and one for the weekend.

Having determined the seasonality factors, one can turn them into a seasonality-related drift term $\mu(t)$ to describe W/S term structure.

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