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Trading in the Downstream European Gas Market: A Successive Oligopoly Approach

Maroeska G. Boots, Fieke A.M. Rijkers and Benjamin F. Hobbs

Year: 2004
Volume: Volume 25
Number: Number 3
DOI: 10.5547/ISSN0195-6574-EJ-Vol25-No3-5
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Abstract:
A model of successive oligopoly is applied to the European natural gas market. The model has a two-level structure, in which Cournot producers are also Stackelberg leaders with respect to traders, who may be Cournot oligopolists or price takers. Several conclusions emerge. First, successive oligopoly ("double marginalization") yields higher prices and lower consumer welfare than if oligopoly exists only on one level. Second, due to the high concentration of traders, prices are distorted more by market power in trading than in production. Third, trader profits depend on whether producers can price discriminate among consuming sectors; if so, producers collect a greater share of the profits. Finally, when traders increase in number, prices approach competitive levels. Thus, it is important to prevent concentration in the downstream gas market. If oligopolistic trading cannot be prevented, vertical integration should not be discouraged, especially if it would increase the number of traders.



Forecasting Nonlinear Crude Oil Futures Prices

Saeed Moshiri and Faezeh Foroutan

Year: 2006
Volume: Volume 27
Number: Number 4
DOI: 10.5547/ISSN0195-6574-EJ-Vol27-No4-4
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Abstract:
The movements in oil prices are very complex and, therefore, seem to be unpredictable. However, one of the main challenges facing econometric models is to forecast such seemingly unpredictable economic series. Traditional linear structural models have not been promising when used for oil price forecasting. Although linear and nonlinear time series models have performed much better in forecasting oil prices, there is still room for improvement. If the data generating process is nonlinear, applying linear models could result in large forecast errors. Model specification in nonlinear modeling, however, can be very case dependent and time-consuming.In this paper, we model and forecast daily crude oil futures prices from 1983 to 2003, listed in NYMEX, applying ARIMA and GARCH models. We then test for chaos using embedding dimension, BDS(L), Lyapunov exponent, and neural networks tests. Finally, we set up a nonlinear and flexible ANN model to forecast the series. Since the test results indicate that crude oil futures prices follow a complex nonlinear dynamic process, we expect that the ANN model will improve forecasting accuracy. A comparison of the results of the forecasts among different models confirms that this is indeed the case.





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