Gas Storage
Figure 2: Forward Curve With Daily Granularity (Jul – Dec)
More sophisticated methods such
as presented by Boogert and de
Jong (2008) use a spot price model
for the valuation of gas storage.
This method aims to assess the
value of price movements beyond
the intrinsic value. Different price
paths are simulated on the basis of
an initial forward curve assuming
mean reversion and the time-
dependent drift of prices and
constant instantaneous (spot)
volatility. The different price paths are then combined with the Dynamic Intrinsic Optimisation
characteristics of the storage and optimised. According to our In this model, the market is represented by a forward curve
assessment, such an approach is able to capture between 34% with daily granularity, the price uncertainty by six factors, and
and 51% of the maximum storage value (Table 1). a simple storage strategy is derived from the repeated
maximisation of the intrinsic value.
Table 1: Modelling Approaches & Effectiveness
Model Value Captured
Smooth forward curves with seasonality
Intrinsic model monthly granularity 24 - 28%
The current forward price for a given day may be interpreted
Intrinsic model daily granularity 29 - 37%
as the certainty equivalent value of that day’s future spot price,
One factor models 43 - 51%
given current information. Using the forward curve tool in the
Dynamic intrinsic model 88 - 91% Elviz ETRM trading system [the energy trading system provided
Perfect foresight (maximum value) 100%
by Viz Risk Management], we construct a smooth, arbitrage
Source: Viz Risk Management
free forward curve on the basis of quoted market prices. An
hourly profile, or in this case a daily profile modulated on the
A one factor model can only partly describe price curve, additional seasonal profiles can be added if the
movements in the gas market. In order to be able to capture up granularity of market prices is insufficient.
to 90% of the value of the gas storage we propose to follow a On the basis of the information in the current forward curve
dynamic intrinsic model. we should be able to derive the intrinsic value of the storage
given the operational characteristics.
Figure 3: Example of a Set of Historic Forward Curves
Principal components
In order to find more information about how the
forward curve can move over time, we have analysed
historic forward curves over one year.
A Principal Component Analysis (PCA) is a widely
used method for simplifying such complex data
structures. The aim is to find the most important
factors (principal components) that describe the price
dynamics of the forward curve from day to day and
use them to simulate possible future markets.
The analysis shows that parallel ‘horizontal’ shifts
represent about 35% of the observed variations in the
forward curve. As all prices move up and down in a
parallelshift,itdoesnotaffectthevalueofthestorage.
We are interested in the other dynamics in the curve.
Source: Viz Risk Management
Six factors explain 90% of the movements in the
62 worldPower2009
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