Regularized Quantile Regression Averaging for probabilistic electricity price forecasting

Quantile Regression Averaging (QRA) has sparked interest in the electricity price forecasting community after its unprecedented success in the Global Energy Forecasting Competition 2014, where the top two winning teams in the price track used variants of QRA. However, recent studies have reported the method's vulnerability to low quality predictors when the set of regressors is larger than just a few. To address this issue, we consider a regularized variant of QRA, which utilizes the Least Absolute Shrinkage and Selection Operator (LASSO) to automatically select the relevant regressors. We evaluate the introduced technique – dubbed LASSO QRA or LQRA for short – using datasets from the Polish and Nordic power markets, a set of 25 point forecasts obtained for calibration windows of different lengths and 20 different values of the regularization parameter. By comparing against nearly 30 benchmarks, we provide evidence for its superior predictive performance in terms of the Kupiec test, the pinball score and the test for conditional predictive accuracy.