Multivariate probabilistic forecasting of electricity prices with trading applications

This study extends recently introduced neural networks approach, based on a regularized distributional multilayer perceptron (DMLP) technique for a multivariate case electricity price forecasting. The performance of a fully connected architecture and a LSTM architecture of neural networks are tested. Different from previous studies we incorporate dependence between multiple exchanges (EPEX and Nord Pool). The empirical data application analyzes two auctions in the day-ahead electricity market for the United Kingdom market. Along with statistical evaluation of probabilistic forecasts we develop a flexible bidding strategy based on risk-adjusted investor utility function. The trading application leverages the differences of the two exchanges by having long/short positions in both. Our findings demonstrate while DMLP shows similar performance compared to the benchmarks, the algorithm is considerably less computationally costly. LASSO Quantile Regression is better in terms if statistical evaluation of distributional fit, while DMLP outperforms in terms of Sharpe ration (by 18%) in the trading application. • The distributional neural network model is enhanced for a multivariate case with incorporated dependence modelling. • In terms of the CRPS, it had comparable results with LASSO Quantile Regression (LASSO QR). • A flexible trading strategy with Risk-Reward is used to evaluate economic benefits, tested in the UK Day-ahead two auctions framework. • The economic application of Mean-CVaR strategy demonstrates an 18% higher Sharpe ratio than the LASSO QR. • According to SHAP values, oil and gas prices along with temperature have the greatest explanatory power.