Prices, Time series analysis, Econometrics, Economics, Electricity, and Numerical analysis
The ambition to create a single European market for electricity has been explicit since the Single European Act of 1988. This article investigates the degree to which this goal has been achieved in terms of the convergence of electricity prices. Two commonly used tests of convergence are applied, β-convergence and a cointegration test using annual electricity price data for nine European Union member states from 1978 to 2003. The results suggest that convergence did occur for most of the countries in the sample over this period. [ABSTRACT FROM AUTHOR]
Demand (Economic theory), Econometrics, Elasticity (Economics), Time series analysis, Prices, Electric power consumption, Electricity, and Households
Measurement of residential demand for electricity has taken on increased importance with the rapid increase in real energy prices and the identification of the electricity sector as a central focus of energy policymaking. Reliable analysis of proposals to revise electricity rate structures and projections of future supply needs must be based on quantitative judgments about price and income elasticities as well as the effects of other major variables. To date, virtually all econometric studies of household demand have used aggregate time-series or cross-section data and some measure of the average residential price per kilowatt -hour of electricity. Because the marginal price per unit of electricity is not constant under the declining-block rates used by utilities, such studies may contain biases that can be especially serious when analyzing the effect of any change in rate structure. The empirical research reported in the article is based on micro-level data for 3825 geographic areas throughout the county of Los Angeles, California. By adopting this disaggregated approach to estimating demand equations, authors are able to measure the marginal price faced by households, control for eight major appliances, and include the important influence of weather.
Hellström, Jörgen, Lundgren, Jens, and Yu, Haishan
Energy Economics. Nov2012, Vol. 34 Issue 6, p1774-1781. 8p.
Energy economics, Empirical research, Markets, Time series analysis, Economic models, Prices, Economic structure, Electricity, and Caloric expenditure
Abstract: The paper empirically explores the possible causes behind electricity price jumps in the Nordic electricity market, Nord Pool. A time-series model (a mixed GARCH–EARJI jump model) capturing the common statistical features of electricity prices is used to identify price jumps. By the model, a categorical variable is defined distinguishing no, positive and negative jumps. The causes for the jumps are then explored through the use of ordered probit models in a second stage. The empirical results indicate that the structure of the market plays an important role in whether shocks in the demand and supply for electricity translate into price jumps. [Copyright &y& Elsevier]
Energy Economics. Sep2011, Vol. 33 Issue 5, p859-869. 11p.
Prices, Demand (Economic theory), Income, Time series analysis, Elasticity (Economics), Durable goods wholesalers, and Electricity
Abstract: This paper examines the determinants of residential electricity demand in Greater Buenos Aires between 1997 and 2006. During the second half of this period, residential electricity tariffs remained nominally fixed, while rising incomes increased sales of durables. Our study is one of few that use monthly data to examine the contribution of prices to residential consumption growth, and it appears to be the first time-series study to explicitly consider the impact of air conditioners on residential demand. Results indicate that durables have an impact on residential electricity demand. Simulations illustrate how prices, income, and durables impact future demand. [Copyright &y& Elsevier]
Prices, Statistics, Time series analysis, Hedging (Finance), Electricity, and Equilibrium
This study investigates the extent to which predicted electricity spot prices from a statistical model, along with consensus forecasts issued by the Australian Financial Market Association (AFMA), provide unbiased price estimates of a forward contract price over a specified time to expiration. The statistical model is a regime switching time series model which is based on the dynamics of the market mechanism. To evaluate a price estimate, two criteria are utilized in order to conclude appropriateness for use in the marking-to-market process. First is the requirement that the predicted prices converge to the spot price at expiration of a hedging contract. The second criterion refers to the mis-pricing due to the price estimates over the days leading up to the contract expiration. Over the data period under consideration, the ranking of alternatives for generating price predictions is clear. On both criteria the Stevenson (2001) model is preferred. Of significance is the lack of support for the consensus (market) prices. They do not converge to the spot price at equilibrium and, further, they generate a considerable overvaluation of the risk management portfolio. [ABSTRACT FROM AUTHOR]
Time series analysis, Markov processes, Prices, Electricity, and Switching circuits
In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. In particular we calibrate AR/ARX ("X" stands for exogenous/fundamental variable--system load in our study), AR/ARX-GARCH, TAR/TARX and Markov regime-switching models to California Power Exchange (CalPX) system spot prices. We then use them for out-of-sample point and interval forecasting in normal and extremely volatile periods preceding the market crash in winter 2000/2001. We find evidence that (i) non-linear, threshold regime-switching (TAR/TARX) models outperform their linear counterparts, both in point and interval forecasting, and that (ii) an additional GARCH component generally decreases point forecasting efficiency. Interestingly, the former result challenges a number of previously published studies on the failure of non-linear regime-switching models in forecasting. [ABSTRACT FROM AUTHOR]
Prices, Pricing, Risk management in business, Time series analysis, Electricity, and Poisson algebras
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. [ABSTRACT FROM AUTHOR]