Modeling Volatility and Dependence of European Carbon and Energy Prices
This work addresses uncertainty in carbon and energy markets for policymakers and investors, but it is incremental as it applies an existing multivariate modeling framework to new data.
The authors tackled the problem of modeling volatility and dependence among European carbon and energy prices by proposing a VECM-Copula-GARCH model, achieving forecasting performance evaluated over an eight-year out-of-sample period with analysis of time-varying correlations and impacts of geopolitical events like the Russian invasion of Ukraine.
We study the prices of European Emission Allowances (EUA), whereby we analyze their uncertainty and dependencies on related energy prices (natural gas, coal, and oil). We propose a probabilistic multivariate conditional time series model with a VECM-Copula-GARCH structure which exploits key characteristics of the data. Data are normalized with respect to inflation and carbon emissions to allow for proper cross-series evaluation. The forecasting performance is evaluated in an extensive rolling-window forecasting study, covering eight years out-of-sample. We discuss our findings for both levels- and log-transformed data, focusing on time-varying correlations, and in view of the Russian invasion of Ukraine.