Transcript Managing Volume Risk in a Retail Energy Business.

Managing Volume Risk in a
Retail Energy Business.
Jon Stamp
Head of Portfolio Management, npower Commercial
29th January 2008
Introduction
Aims of this presentation:

To focus on Gas Swing as an example of a factor that can create
substantial volume risk for a retail energy business.

Outline what gas swing is and illustrate it graphically.
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Highlight the risks that arise as a result of gas swing and that influence
the process of modelling it.

Discuss some ways to model gas swing risk – so that it can be
forecast, priced and hedged.

Suggest possible ways to mitigate the volume risk related to gas swing.

Put forward some areas that provide further mathematical challenges.
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Gas Swing - What is it?
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
More problematic on residential side of business where most customers are
NDM (Non Daily Metered) and particularly weather sensitive.

Seasonally normal demand (SND) is forecast by summing customer meters
with End User Category (EUC), a profile of customer type created by National
Grid.

Long term position is established by hedging to SND. SND changes monthly
as customer numbers fluctuate.
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Nearer real time, weather (the main driver of gas consumption) forecasts
become more reliable and have considerable influence on demand levels.
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Short term trading (STT) takes place within the final 10 days to balance this.

Swing is the difference in volume between the long term position and STT
position and results in a change to revenue and a change to cost.

The mathematical challenge is to model, estimate and mitigate the costs of
swing.
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Therms
Gas Swing – change in volume
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Deviation
Seasonal Normal Demand
Gas Swing - The risks

If demand > hedged position, need to buy extra gas, greater demand pushes
prices up, may have to buy at a price greater than tariff.

If demand < hedged position, need to sell gas, lower demand pushes prices
down, may have to sell at a price lower than tariff.
Swing cost = DfSND * ( TF - DA )
Where: TF= tariff price, DfSND= deviation from seasonal normal demand
(which is assumed to be the hedged position), DA= day ahead price
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Gas Swing - The risks

DfSND and DA must be simulated to calculate the gas swing cost. However
they can not be simulated separately as they are correlated with each other.
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
Temperature and demand are approximately -80% correlated. Implying
temperature drives demand, this must be modelled with caution because it
is not always true. For example the increased use of air conditioning in the
summer can increase demand when temperatures are high.
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Demand and price are approximately 48% correlated. This relationship is
most strongly recognisable 12 days from delivery when accurate weather
forecasts become available.
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Modelling these sometimes unpredictable relationships can be a challenge.
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Gas Swing - How can it be modelled?
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
Gas Swing models aim to simulate forecasted spot price paths. They can
then be used to give an associated deviation from SND based on the
relationship between demand and price.
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A pricing equation can be developed using Geometric Brownian motion, with
Poisson jump processes used to simulate price spikes.
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Temperature, and in turn demand, has a tendency to experience persistence
in the data i.e. any day’s temperature has a correlation to the previous day’s
temperature.
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Hence, the demand process is modelled using an ARMA (Auto-Regressive
Moving Average) to establish the specification and consistency of the deviation
trends.
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Monte Carlo modelling can be used to simulate thousands of price and volume
deviations.
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The simulations can be used to construct a graph showing the distribution of
the gas swing costs. Probability can be added to this.
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Gas Swing - The risks (illustrative)
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Cost in £m
Swing
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Hedged Swing
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Hedge to minimise, the risk of incurring and size of, gas swing costs. The hedging
costs may be greater overall but the probability of being exposed to large swing costs
will have been reduced.
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Difficult to estimate the exact effect of the hedge. It will also alter as the composition of
the portfolio changes. Modelling these changes can be troublesome.
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Gas Swing - Mitigating the risk.
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Gas Storage Contracts – Physical storage facilities, can inject during
low price periods and withdraw in high price periods. Protects against
short term price spikes but not against a collapse in prices. Need to
value the purchasing of storage and create a model that forecasts
optimal timings for injection and withdrawal.
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LNG Gas Storage - Liquefied Natural Gas that is available for delivery
at very short notice and in greater volumes than gas storage. Capacity
is only available to purchase in an annual auction and only provides
protection against rising prices. Need to create a model for bidding in
the auction, and for injection and withdrawal as above.
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Gas Swing - Mitigating the risks
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Weather Swaps - Financial instruments that pay out when weather is
unseasonably high or cold. Can protect against selling back gas into a low price
market or buying in high price market. Relies on the correlation between
weather, demand and price remaining as expected. A model is required to value
product.
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Swing Contracts - Financial options that payout when portfolio of customers’
demand deviates from seasonal normal. Focuses on demand rather than weather
thus provides some protection when weather and demand correlation moves in
the opposite way to expected. Need to create a model to value the product.
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Demand Side Management - Interact with customers to incentivise selling back
of gas on high price days. Need to model the portfolio effect of customers
altering their consumption behaviour. Only applicable to larger customers who
are usually daily metered.
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Further mathematical challenges
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Modelling the portfolio effect and impact of layering on the hedge, from using
a variety of products to mitigate gas swing risk.
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Price is often assumed to be normally distributed, when it is actually fat tailed
(Leptokinic). Using Poisson analysis helps to capture this, but it is just an
overlay to the model and it would be more accurate to incorporate it.
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Price Volatility is not constant as would be expected. One method which
could be explored to overcome this would be using ARCH (Autoregressive
Conditional Heteroscedastic) modelling.
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Consumer behaviour is ever-changing, hence models need to be developed
to factor in these changes e.g. increases in price sensitivity and demand
destruction.
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Historic data reflects the market conditions at that time, e.g. doubts over the
security of supply pushing up prices. These may no longer be present e.g.
completion of the interconnector improving the supply network. To reflect this,
models need to be created that recognise historic regime switches.
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Backup slide
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Appendices
Price Simulations (20th June 2006) vs Day Ahead Actuals
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Forward Curve 20th June 06
p/thm
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Spectron Day Ahead Prices
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Date
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Jun-07
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