Econ Papers Daily / 期刊论文
基于宏观经济与金融变量预测碳价格
Forecasting the Price of Carbon with Macroeconomic and Financial variables
摘要
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.
中文摘要
本文研究欧盟排放交易体系（EU ETS）中月度实际碳价格的点预测、符号预测和密度预测问题。我们表明，基于宏观经济和金融变量构建的因子增强贝叶斯向量自回归（BVAR）模型在点预测和密度预测性能上均有所改善，其中点预测的最大改进出现在中期和较长预测期限。具体而言，单因子BVAR模型在提前六个月和九个月时给出最低值，而基准BVAR在一年期预测期限表现最佳。相比之下，包含所有单个预测变量的大规模BVAR并未提高点预测精度，这支持使用简约模型设定。简单预测池化在中期及更长预测期限带来适度改进，但并未缓解相对预测表现的时间变化。我们还将基于模型的预测与调查预期及数据提供商发布的预测进行了定性比较，进一步凸显相对预测表现随时间变化。此外，我们考察经核证的排放量，并表明随机波动率改善了点预测，在一年期预测期限上约降低6.5–7.1%。最后，我们使用基于模型的预测构建市场监测工具，以追踪EU ETS中的需求和价格压力。