eThesis - School of Business electronic theses

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PURPOSE OF THE STUDY This thesis studies hedging seasonal customer demand in electricity retail business and the main objective is to estimate the cost of updating the long-term hedges with shorter-term hedges for different seasonal demand. This thesis also tests optimal time to update hedges by comparing the costs of updating the hedges at the beginning, end and in the middle of the time period when the contracts are available.

DATA AND METHODOLOGY The data consist of Nord Pool daily closing price data from 2006 to 2009. Customer demand data is obtained from Fortum. The seasonality is modelled by a method developed by Borovkova and Geman (2006), which looks at the relationships between electricity forward prices. Seasonality is determined as the difference between monthly price and reference yearly average price. In this way the reference price does not contain seasonality. We further used the method Borovkova and Geman (2006b) developed for electricity forwards, and we use it to model the deviations of the price curve from flat de-seasoned curve, by using principal components. We then simulated possible future states of forward prices using them.

We made some adjustments to the Borovkova and Geman’s methods, as we used Nord Pool data, which has different forwards available than other markets.

We also constructed an electricity forward curve based on de-seasoned prices calculated with Borovkova and Geman’s (2006) method. The price curve and simulated cost of update can be combined to give a monthly electricity price for customer that includes both the latest market price information and the expected cost of updating the hedges later.

RESULTS This thesis uses methods developed by Borovkova and Geman (2006 and 2006b) and finds support for using their methods in practice and also that it is possible to adjust them to work with Nord Pool data. The simulations gave reasonable results as, when comparing the costs of updating the long-term hedges, the demand with higher seasonality gets higher costs.

For different update timing the average costs are on same level for early, middle and late update, however, the standard deviation of the update cost is higher the later the update is done, which is consistent with Samuelson’s effect of volatilities decreasing when time to maturity increases.