Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We study the importance of time‐varying volatility in modelling hourly electricity prices when fundamental drivers are included in the estimation. This allows us to contribute to the literature of large Bayesian VARs by using well‐known time series models in a large dimension for the matrix of coefficients. Based on novel Bayesian techniques, we exploit the importance of both Gaussian and non‐Gaussian error terms in stochastic volatility. We find that using regressors as fuel prices, forecasted demand and forecasted renewable energy is essential to properly capture the volatility of these prices. Moreover, we show that the time‐varying volatility models outperform the constant volatility models in both the in‐sample model‐fit and the out‐of‐sample forecasting performance.