Sébastien Fries | Searcharxiv

arXiv2020

Understanding the explosive trend in EU ETS prices -- fundamentals or speculation?

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.

econ.EM↗

arXiv2018

Path prediction of aggregated $α$-stable moving averages using semi-norm representations

For $(X_t)$ a two-sided $α$-stable moving average, this paper studies the conditional distribution of future paths given a piece of observed trajectory when the process is far from its central values. Under this framework, vectors of the form $\boldsymbol{X}_t=(X_{t-m},\ldots,X_t,X_{t+1},\ldots,X_{t+h})$, $m\ge0$, $h\ge1$, are multivariate $α$-stable and the dependence between the past and future components is encoded in their spectral measures. A new representation of stable random vectors on unit cylinders -sets $\{\boldsymbol{s}\in\mathbb{R}^{m+h+1}: \hspace{0.3cm} \|\boldsymbol{s}\|=1\}$ for $\|\cdot\|$ an adequate semi-norm- is proposed in order to describe the tail behaviour of vectors $\boldsymbol{X}_t$ when only the first $m+1$ components are assumed to be observed and large in norm. Not all stable vectors admit such a representation and $(X_t)$ will have to be < > for $\boldsymbol{X}_t$ to admit one. The conditional distribution of future paths can then be explicitly derived using the regularly varying tails property of stable vectors and has a natural interpretation in terms of pattern identification. The approach extends to processes resulting from the linear combination of stable moving averages and applied to several examples.

math.PR↗