Modeling Volatility and Dependence of European Carbon and Energy Prices

TL;DR

This paper introduces a comprehensive multivariate time series model to analyze and forecast European carbon and energy prices, capturing their volatility, dependencies, and effects of geopolitical events.

Contribution

It proposes a novel VECM-Copula-GARCH model tailored for European EUA and energy prices, incorporating inflation and emissions normalization for improved analysis.

Findings

Time-varying correlations between prices are significant.

Model outperforms benchmarks in forecasting accuracy.

Geopolitical events influence price dependencies.

Abstract

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.