The deregulation of electricity markets in different parts of the world has exposed consumers to irregularities in electricity prices driven by the principle of supply and demand. Development of accurate statistical models contributes to establishing protective mechanisms and risk measurement policies for both suppliers, consumers. In this paper multi-factor modelling methodology, solely applied to the spot price of electricity or demand for electricity in earlier studies, is extended to a bivariate process of spot price of electricity and demand for electricity. The suggested model accommodates common idiosyncrasies observed in deregulated electricity markets such as cyclical trends in price and demand for electricity, occurrence of extreme spikes in prices, and a mean-reversion effect seen in the settling of prices from extreme values to the mean level over a short period of time. A time series model for de-seasonalised demand for electricity is used in combination with a linear regression model developed for logarithms of deseasonalised daily averages of electricity spot prices. The spiky behaviour of prices occurring in clusters, interpreted as 'a post-spike' effect, is addressed by a filtered Poisson (i.e. shot noise) factor of the model. The demand for electricity is found to be the primary stochastic factor driving the electricity prices. In the linear regression model for 'de-seasonalised' and 'de-spiked' spot prices the back-shifted variables play the role of exogenous variables. These variables capture the 'price' and 'demand' inter-dependence observed in practice. The historical data is obtained from the NSW section of Australian Energy Markets.
- Bivariate normal inverse gaussian distribution
- Filtered poisson process
- Gaussian copula