A VaR-Based Methodology for Assessing Carbon Price Risk across European Union Economic Sectors

Abstract

The latest European Union measures for combating climate adopted in the “Fit for 55 package” envisage the extension of the Emissions Trading System, the first “cap-and-trade” system in the world created for achieving climate targets, which limits the amount of greenhouse gas emissions by imposing a price on carbon. In this context, our study provides an integrated assessment of carbon price risk exposure of all economic sectors in the European Union Member States, thus supporting decision making in determining the energy transition risk. We propose a novel approach in assessing carbon risk exposure using the Value at Risk methodology to compute the carbon price under the EU ETS, based on historical price simulation for January–August 2021 and ARMA-GARCH models for the October 2012–August 2021 period. We further built a value erosion metric, which allowed us to establish each sector’s exposure to risk and to identify differences between Eastern and Western EU countries. We find that the refining sector appears to be highly vulnerable, whereas there is higher potential for large losses in the energy supply and chemical sectors in Eastern EU Member States, given a different pace of industry restructuring.

Dependence Structure among Carbon Markets around the World: New Evidence from GARCH-Copula Analysis

“…Dependencies in carbon markets can be examined by combining these copula functions with a GARCH-type model including conditional heteroscedasticity, since this model successfully describes the characteristics of volatility clustering in carbon allowance prices. The GARCH family, e.g., EGARCH, MGARCH, GJR-GARCH, TGARCH and ARMA-GARCH models, has been extensively adopted by studies on carbon prices (see, for instance, Wang et al, 2019; Fu and Zheng, 2020; Bulai et al, 2021; Zhang and Wu, 2022). Along with the GJR-GARCH and the TGARCH, the EGARCH model has the very interesting feature of accommodating asymmetric reactions of volatility to positive and negative shocks.…”

Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach

“…Byun and Cho (2013) compared and analyzed the performance of GARCHtype models, an implied volatility from prices, and k-nearest neighbor models in predicting carbon option prices. Bulai et al (2022) predicted the carbon price in the EU ETS through the proposed ARMA-GARCH model.…”