Marina Friedrich, Sébastien Fries, Michael Pahle, Ottmar Edenhofer
arXiv 25 Jun 2019 · Econometrics · 14 citations (OpenAlex)
arXiv:1906.10572 · PDF · DOI · OpenAlex · Extracted main text
In 2018, allowance prices in the EU Emission Trading Scheme (EU ETS) experienced a run-up from persistently low levels in previous years. Regulators attribute this to a comprehensive reform in the same year, and are confident the new price level reflects an anticipated tighter supply of allowances. We ask if this is indeed the case, or if it is an overreaction of the market driven by speculation. We combine several econometric methods - time-varying coefficient regression, formal bubble detection as well as time stamping and crash odds prediction - to juxtapose the regulators' claim versus the concurrent explanation. We find evidence of a long period of explosive behaviour in allowance prices, starting in March 2018 when the reform was adopted. Our results suggest that the reform triggered market participants into speculation, and question regulators' confidence in its long-term outcome. This has implications for both the further development of the EU ETS, and the long lasting debate about taxes versus emission trading schemes.
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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 | Koch, N., Fuss, S., Grosjean, G., and Edenhofer, O (2014) Causes of the EU ETS price drop: Recession, CDM, renewable policies or a bit of everything?-New evidence self | 0.928 | 4 | 3 | 100% |
| 2 | Lutz, B. J., Pigorsch, U., and Rotfu, W (2013) Nonlinearity in cap-and-trade systems: The EUA price and its fundamentals | 0.928 | 4 | 3 | 100% |
| 3 | Fries, S (2018) Conditional moments of noncausal alpha-stable processes and the prediction of bubble crash odds self | 0.855 | 8 | 4 | 62% |
| 4 | Phillips, P. C. B., Shi, S., and Yu, J (2015) Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500 | 0.839 | 22 | 7 | 59% |
| 5 | Cai, Z (2007) Trending time-varying coefficient time series models with serially correlated errors | 0.754 | 7 | 3 | 43% |
| 6 | Aatola, P., Ollikainen, M., and Toppinen, A (2013) Price determination in the EU ETS market: Theory and econometric analysis with market fundamentals | 0.737 | 3 | 2 | 100% |
| 7 | Sharma, S. and Escobari, D (2018) Identifying price bubble periods in the energy sector | 0.737 | 3 | 2 | 100% |
| 8 | Shiller, R. J (2017) Narrative economics | 0.737 | 3 | 2 | 100% |
| 9 | Gouriéroux, C. and Zakoän, J.-M (2017) Local explosion modelling by non-causal process | 0.644 | 5 | 2 | 40% |
| 10 | Pedersen, T. and Montes Schütte, E (2017) Testing for Explosive Bubbles in the Presence of Autocorrelated Innovations | 0.644 | 3 | 2 | 67% |
Showing the top 10 of 73 scored citations.