Forecasting the Price of Carbon with Macroeconomic and Financial variables∗

We tackle the issue of producing point, sign, and density forecasts for the monthly real price of carbon within the European carbon market, EU ETS. We show that a Bayesian Vector Autoregressive (BVAR) model, augmented with factors based on macroeconomic and financial variables, yields accuracy gains over a set of benchmark forecasts in both point and density forecasts. We also provide a qualitative comparison of model-based forecasts with survey expectations and forecasts released by data providers. Moreover, we consider verified emissions and demonstrate that adding stochastic volatility can further improve the forecasting performance of a single-factor BVAR model. Lastly, we rely on forecasts to build market monitoring tools that track demand and price pressure in the EU ETS.