マクロ経済・金融変数を用いた炭素価格の予測

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:炭素価格予測におけるベイズVARと因子モデルの有効性、および確率的ボラティリティの役割を定量化した点が参考になる。

🏢実務担当者:EU ETS価格の予測手法を理解し、炭素価格リスクの評価やヘッジ戦略、投資判断に活用できる。

🏛政策担当者:炭素市場の価格安定性や監視ツールの設計に、マクロ・金融変数を組み込むことの有用性を示唆。

We tackle the issue of producing point, sign, and density forecasts for the monthly real price of carbon in the European Union Emissions Trading System (EU ETS). We show that Bayesian Vector Autoregressive (BVAR) models augmented with factors based on macroeconomic and financial variables yield improvements in point and density forecasting performance, with the largest point-forecast gains emerging at intermediate and longer horizons. In particular, the one-factor BVAR model provides lowest values at six and nine months ahead, while the baseline BVAR performs best at the one-year horizon. By contrast, a large BVAR including all predictors individually does not improve point forecast accuracy, supporting the use of parsimonious model specifications. Simple forecast pooling delivers modest gains from medium horizons onward, but does not mitigate the time variation in relative forecast performance. We also provide a qualitative comparison of model-based forecasts with survey expectations and forecasts released by data providers, which further highlights that relative forecast performance varies over time. Moreover, we consider verified emissions and show that stochastic volatility improves point forecasts, with reductions of about 6.5–7.1% at the one-year horizon. Lastly, we use model-based forecasts to construct market monitoring tools that track demand and price pressure in the EU ETS.

• openalex https://doi.org/10.1016/j.jedc.2026.105435first seen 2026-10-03 04:59:04