Multivariate probabilistic forecasting of electricity prices with trading applications
扩展了基于正则化分布多层感知器的神经网络方法，用于多变量电力价格预测，并开发了基于风险调整效用的灵活交易策略，在英国日前电力市场两拍卖框架下测试，发现分布多层感知器在夏普比率上比LASSO分位数回归高18%。
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.