• Energy Research

In this paper, we examine various characteristics of both base and peak electricity spot prices and their returns, and investigate dependence structures, extreme co-movements, risk spillovers, and integration relationships among the five major European electricity markets, including France, Germany, the Netherlands, Spain, and the UK. To do so, we propose a new perspective by applying a hybrid of ARMA-GARCH, static and dynamic copulas, and dynamic state-space models with the Kalman filter to address the issue. Based on the results of the ARMA-GJR-GARCH model, we first find that there are spillover effects in the returns of both base and peak spot prices in the five European electricity markets, and there are heteroskedastic, asymmetric, and leverage effects with negative and positive shocks, including spikes and drops during both base and peak load periods. Hence, a decrease in prices will boom the variance of the returns, and a decrease in returns can lead to a much greater increase in volatility. Second, there exist some extents of positive dependencies, tail dependencies, and extreme co-movements among the European electricity markets based on the copula models. In addition, we find that the degree of (tail) dependence and the potential state of market integration are stronger and higher during the peak period than the base period, implying that the European electricity markets could boom or crash together, especially during the peak load period. Further, the results of both the dynamic copulas and dynamic state-space models show that most pairs of the European electricity markets co-move symmetrically and have a time-varying dependence, but do not appear to grow over time. Finally, we provide an application of the copula-GARCH model in estimating and predicting risk spillovers across the five European electricity markets. We document that there are high-risk spillover effects in the European electricity markets because the values of the Conditional Value-at-Risk (CoVaR) are large. Also, we find that the more integrated the market, the more the systematic risk contribution of the market as indicated by ΔCoVaR. Our findings provide useful information regarding the dependence, integration, risk management, and asset pricing for the European electricity markets.

This paper attempts to model both static and dynamic dependence structures and measure impacts of energy consumptions (both renewable (EC) and non-renewable (REN) energies), economic globalization (GLO), and economic growth (GDP) on carbon dioxide (CO2) emissions in Argentina over the period 1970-2020. For analyses purpose, the current research deploys the novel static and dynamic copula-based ARIMA-fGARCH with different submodels. The static bivariate copula results show that the growth rates of the pairs EC-CO2 and GDP-CO2 are asymmetrically positive co-movements and have high left tail (extreme) dependencies, implying that the increase in non-renewable energy and economic growth can critically contribute to the environmental degradation, and the decrease in the consumption of non-renewable energy at a high level will consequently reduce the CO2 emissions at the same level. Based on several copula-based dependence measures, we document that between the two factors, the non-renewable energy has a stronger impact than the economic growth regarding the CO2 emissions. On the other hand, the growth rates of both economic globalization and renewable energy symmetrically negatively co-move with the growth rates of the CO2 emissions, but they have no extreme dependencies, indicating that these factors contribute to Argentina's environmental quality, in which the factor of renewable energy has a greater impact. Furthermore, the dynamic copula outcomes show that the (tail) dependencies of CO2 emissions on the non-renewable energy and economic growth are time-varying, while the pairs REN-CO2 and GLO-CO2 possess only dynamic dependencies, but no dynamic tail dependencies. Moreover, through the dynamic copula-based dependence, the environmental Kuznets curve (EKC) hypothesis can be estimated and illustrated explicitly. In addition, we leverage multivariate vine copulas for modelling dependence structures of the five variables simultaneously, which can reveal rich information regarding conditional associations among the relevant variables. Some policy implications are also provided to mitigate CO2 emissions.