Rambharat, B. Ricky, Brockwell, Anthony E., and Seppi, Duane J.
Journal of the Royal Statistical Society: Series C (Applied Statistics). Apr2005, Vol. 54 Issue 2, p287-299. 13p. 1 Chart, 7 Graphs.
PRICES, MARKOV processes, MONTE Carlo method, and ELECTRICITY
We introduce a discrete time model for electricity prices which accounts for both transitory spikes and temperature effects. The model allows for different rates of mean reversion: one for weather events, one around price jumps and another for the remainder of the process. We estimate the model by using a Markov chain Monte Carlo approach with 3 years of daily data from Allegheny County, Pennsylvania. We show that our model outperforms existing stochastic jump diffusion models for this data set. Results also demonstrate the importance of model parameters corresponding to both the temperature effect and the multilevel mean reversion rate. [ABSTRACT FROM AUTHOR]
IEEE Transactions on Power Systems. Nov2009, Vol. 24 Issue 4, p1649-1656. 8p. 6 Charts, 4 Graphs.
PRICING, PRICES, RISK premiums, COINTEGRATION, MARKETING, MANAGEMENT, ECONOMICS, and ELECTRICITY
In contrast to forwards and futures on storable commodities, prices of long-term electricity forwards exhibit a dynamics different to that of short-term and midterm prices. We model long-term electricity forward prices through demand and supply of electricity, adjusted with a risk premium. Long-term prices of electricity, oil, coal, natural gas, emission allowance, imported electricity, and aluminum are modeled with a vector autoregressive model (VAR). For estimation, we use weekly prices of far-maturity forwards relevant for the Nordic electricity market. Although electricity prices experienced a few substantial shocks during the period we analyzed, there is no evidence of a structural break. Cointegration analysis reveals two stationary long-run relationships between all variables except the gas price, indicating that these variables move together over time. We find some influence of the risk premium, however not on the long-term electricity forwards at Nord Pool. [ABSTRACT FROM AUTHOR]
This paper investigates the day-ahead forecasting performance of fundamental price models for electricity spot prices, intended to capture: (i) the impacts of economic, technical, strategic and risk factors on intra-day prices; and (ii) the dynamics of these effects over time. A time-varying parameter (TVP) regression model allows for a continuously adaptive price structure, due to agent learning, regulatory and market structure changes. A regime-switching regression model allows for discontinuities in pricing due to temporal irregularities and scarcity effects. The models that invoke market fundamentals and time-varying coefficients exhibit the best predictive performance among various alternatives, in the British market. [Copyright &y& Elsevier]