Abstract
This article presents an original predictive strategy, based on a new mid-term forecasting model, to be used for trading physical electricity futures. The forecasting model is used to predict the average spot price, which is used to estimate the Risk Premium corresponding to electricity futures trade operations with a physical delivery. A feed-forward neural network trained with the extreme learning machine algorithm is used as the initial implementation of the forecasting model. The predictive strategy and the forecasting model only need information available from electricity derivatives and spot markets at the time of negotiation. In this paper, the predictive trading strategy has been applied successfully to the Iberian Electricity Market (MIBEL). The forecasting model was applied for the six types of maturities available for monthly futures in the MIBEL, from 1 to 6 months ahead. The forecasting model was trained with MIBEL price data corresponding to 44 months and the performances of the forecasting model and of the predictive strategy were tested with data corresponding to a further 12 months. Furthermore, a simpler forecasting model and three benchmark trading strategies are also presented and evaluated using the Risk Premium in the testing period, for comparative purposes. The results prove the advantages of the predictive strategy, even using the simpler forecasting model, which showed improvements over the conventional benchmark trading strategy, evincing an interesting hedging potential for electricity futures trading.
Highlights
The extreme learning machine (ELM) neural network, applied as the implementation of the mid-term forecasting model of the monthly average spot price, used the six input variables described in Section 2.1 and the sigmoidal activation function
A simpler forecasting model than the ELM neural network model was selected to be used with the predictive trading strategy
This benchmark model was an ordinary least square forecasting model (OLS model), whose coefficients were adjusted with the data corresponding to the training dataset. This model obtained worse forecasting results than the ELM neural network model in the initial selection phase with the cross-validation procedure, i.e., the average root mean square error (RMSE) with the five folds used as testing sets was
Summary
New trading scenarios for electricity markets have arisen over the last three decades. Wholesale electricity markets are organized in a set of different niche markets, in which agents exchange energy and reserves from mid-term to very short-term periods. The products traded in European forward markets include electricity forwards, electricity futures, electricity swaps, contract for differences (CfDs), electricity price area differentials (EPADs) and spreads and electricity options [2]. The most traded product, by volume of energy, is the electricity forward, which corresponds to the bilateral contract, by which the buyer and seller agree on a price for a volume of electricity and for a specified delivery period in the future. Electricity futures can be physical contracts, i.e., exchange contracts for the delivery of a quantity of electricity over a specified period or financial (cash-settled) contracts, which correspond to back-up transactions or speculation transactions
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