Keywords:
Published in: Studies in non-linear dynamics & econometrics
Publication year: 2006

Due to its non-storable nature, electricity is a commodity with probably the most volatile spot prices, exemplified by occasional spikes. Appropriate pricing, portfolio, and risk management models have to incorporate these characteristics, and the spikes in particular. We investigate the nature of power spikes in a number of different markets. We test what time-series model is best able to capture the dynamics of these disruptive spot prices. We use regime-switching models to infer whether the price spikes should be treated as abnormal and independent deviations from the ‘normal’ price dynamics or whether they form an integral part of the price process. We test the time-series models on day-ahead markets in Europe and the US. We find that regime-switch models are better able to capture the market dynamics than a GARCH(1,1) or Poisson jump model. We also find clear differences between the markets and attribute part of the differences to the share of hydro-power in the total supply stack: hydro-power serves as an indirect means to store electricity, which has a dampening effect on spikes.

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Published in: Energy Risk
Publication year: 2004

Aurelian Tröndle presents a general framework for modelling prices of storable and non-storable energy assets, which sheds light on different market fundamentals, and shows how energy market volatility is seasonal and anything but stable.The model shows the stochastic properties of the underlying processes of price evolution

Keywords: weather derivatives
Published in: Weather
Publication year: 2004
Co-Author 1: Jeremy Penzer

The analysis of historical meterological data is vital for structuring weather derivatives. But how should weather traders deal with the trends that may exists in the data?

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Published in:
Publication year: 2002
Co-Author 1: Andrea Gigli

In this paper we propose an algorithm for pricing derivatives written on electricity in an incomplete market setting. A discrete time model for price dynamics which embodies the main features of electricity price revealed by simple time series analysis is considered. We use jointly Binomial and Monte Carlo methods for pricing under a risk-neutral measure of which we prove the existence.

Keywords:
Published in:
Publication year: 1999
Co-Author 1: Chris Strickland

In this paper we develop a single-factor modeling framework which is consistent with market observable forward prices and volatilities. The model is a special case of the multi-factor model developed in Clewlow and Strickland [1999b] and leads to analytical pricing formula for standard options, caps, floors, collars and swaptions. We also show how American style and exotic energy derivatives can be priced using trinomial trees, which are constructed to be consistent with the forward curve and volatility structure. We demonstrate the application of the trinomial tree to the pricing of a European and American Asian option. The analysis in this paper extends the results in Schwartz [1997] and Amin, et al. [1995].

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Published in:
Publication year: 2000
Co-author 1: Chris Strickland
Co-author 2: Vince Kaminski

Adapting and estimating a version of the mean-reversion model for energy markets

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Published in:
Publication date:
Co-author 1: Margaret Armstrong

Powernext started operating on 27 November 2001. Although the German exchange, EEX, has been functioning for much longer, the two have many common points. Both use the same system for fixing the day-ahead spot price, the one developed by NordPool. In contrast to Omel in Spain, power producers in France and Germany are not obliged to sell through the exchange. In addition, the crossborder transmission lines that physically link the French and German grids, not only make the electricity supply more reliable they also allow cross-border commercial transactions which should homogenize prices in both countries. So after nearly a year of operation it is interesting to compare the spot prices on the two exchanges in order to have a better understanding of the statistical properties of the prices in the two markets and the relationship between them. This information will be used when modelling the structure of the day-ahead spot prices.
The data used to carry out the study consists of the (hourly) spot prices for electricity from Powernext and EEX, for the period from 1 January 2002 to 2 December 2002. Data from the first five weeks of trading were not included because traded volumes were relatively low initially and so these data are not necessarily representative.
This report is divided into four sections. The first one presents the basic statistics, starting with the histograms of all the 8064 spot prices in the 336 days, for both exchanges. In time series data, it is usual to find three types of seasonality: daily, weekly and annual. As the available data cover less than one calendar year, it is too early to attempt to study annual trends. So we limit ourselves to studying daily and weekly fluctuations. Plotting the hourly average prices for each day of the week shows some interesting differences between Powernext and EEX, as well as a high degree of similarity. The second section studies the time correlations between the two time series. Rather than using auto and cross correlations which are sensitive to the estimate of the mean, we have chosen to use variograms. The third section focuses on the buy and sell curves that effectively determine the spot prices. Some preliminary conclusions are given in the fourth section.

Keywords: Ornstein Uhlenbeck, spot prices, power exchange
Published in:
Publication year: 2003

Overview:
1. Electricity as a commodity
2. A deregulated market for power
3. Case study: the NordPool power exchange
4. Physical properties of spot prices
5. Mathematical models for spot prices
6. Pricing power derivatives
7. Conclusions

Keywords:
Published in: Energy Risk Production 
Publication year: 2000
Co-author 1: Chris Strickland
Co-author 2: Vince Kaminski 

EPRM presents a method of modelling and estimating jumps in energy prices