Andrea Bastianin, Elisabetta Mirto, Yan Qin, Luca Rossini
arXiv 7 Feb 2024 · Econometrics · 11 citations (OpenAlex)
arXiv:2402.04828 · PDF · DOI · OpenAlex · Extracted main text
Putting a price on carbon -- with taxes or developing carbon markets -- is a widely used policy measure to achieve the target of net-zero emissions by 2050. This paper tackles the issue of producing point, direction-of-change, and density forecasts for the monthly real price of carbon within the EU Emissions Trading Scheme (EU ETS). We aim to uncover supply- and demand-side forces that can contribute to improving the prediction accuracy of models at short- and medium-term horizons. We show that a simple Bayesian Vector Autoregressive (BVAR) model, augmented with either one or two factors capturing a set of predictors affecting the price of carbon, provides substantial accuracy gains over a wide set of benchmark forecasts, including survey expectations and forecasts made available by data providers. We extend the study to verified emissions and demonstrate that, in this case, adding stochastic volatility can further improve the forecasting performance of a single-factor BVAR model. We rely on emissions and price forecasts to build market monitoring tools that track demand and price pressure in the EU ETS market. Our results are relevant for policymakers and market practitioners interested in monitoring the carbon market dynamics.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bjrnland, H., Cross, J. L., and Kapfhammer, F (2023) The drivers of emission reductions in the European carbon market | 1.000 | 10 | 5 | 100% |
| 2 | Chan, J. C (2023) Comparing stochastic volatility specifications for large Bayesian VARs | 0.737 | 3 | 2 | 100% |
| 3 | Giacomini, R. and Rossi, B (2010) Forecast comparisons in unstable environments | 0.644 | 4 | 1 | 100% |
| 4 | European Central Bank (2021) Climate change and monetary policy in the Euro Area | 0.644 | 2 | 2 | 100% |
| 5 | European Central Bank (2021) ECB presents action plan to include climate change considerations in its monetary policy strategy | 0.644 | 2 | 2 | 100% |
| 6 | Känzig, D. R (2023) The unequal economic consequences of carbon pricing | 0.644 | 2 | 2 | 100% |
| 7 | Rossi, B (2021) Forecasting in the presence of instabilities: How we know whether models predict well and how to improve them | 0.644 | 2 | 2 | 100% |
| 8 | Baumeister, C., Korobilis, D., and Lee, T. K (2022) Energy markets and global economic conditions | 0.644 | 2 | 2 | 100% |
| 9 | Clark, T. E. and Ravazzolo, F (2015) Macroeconomic forecasting performance under alternative specifications of time-varying volatility | 0.644 | 2 | 2 | 100% |
| 10 | Gneiting, T. and Ranjan, R (2011) Comparing density forecasts using threshold- and quantile-weighted scoring rules | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 38 scored citations.