The work investigates the volatility connectedness between oil price and clean energy firms over the period 2011–2020 (including the COVID-19 outbreak). Using the volatility spillover models, and dynamic conditional correlation, we are able to identify the volatility spillover effect between these financial markets and its implications for portfolio diversification. The results indicate a significant change in both static and dynamic volatility connectedness around the COVID-19 outbreak. For instance, total connectedness index changes from 21.36% (pre-COVID-19) to 61.23% (COVID-19). This finding shows the strong effect of the COVID-19 pandemic on these financial markets. Furthermore, we show how the WTI oil from the volatility transmitter (before the outbreak of the pandemic) becomes a risk receiver after the start of the global pandemic COVID-19. Our findings indicate that recent pandemic intensified volatility spillovers, supporting the financial contagion effects. Finally, we determine the optimal hedge ratios and portfolio weights. The estimates provided suggest the need for active portfolio management, taking into account the distinct characteristics of each sector and thus, the firm. For example, the optimal weight analysis shows how the clean sector has become important in optimal diversification strategies. Our results can be used for portfolio decisions and regulatory policymaking, particularly in the current context of high uncertainty.

This research investigates the connectedness and the tail risk spillover between clean energy and oil firms, from January 2011 to October 2021. To this, we use the Tail-Event driven NETworks (TENET) risk model. This approach allows for a measurement of the dynamics of tail-risk spillover for each sector and firm. Hence, we can provide a detailed picture of the existing extreme relationships within these markets. We find that the total connection between the markets varies during the period analysed, showing how the uncertainty in oil price plays a critical role in the risk dynamics for oil companies. Also, we find that relationships between energy firms tend to be intrasectoral; that is, each sector receives (emits) risk from (to) itself. These results can have important practical implications for risk management and policymakers.

This paper studies the dependence between the clean energy markets and brown assets (oil and Bitcoin) over the years 2011–2019. For this purpose, we use the VAR for VaR framework to capture the extreme dependence (tail risk). Moreover, we compute the Granger-causality in risk to study the impact of the Paris Agreement on these markets. We provide novel evidence of the relationship between the clean and oil markets. Notably, the results suggest that they are highly integrated in terms of risk spillover: their lagged returns, risks and extreme events influence both the VaRs of the clean energy sector and oil prices. Additionally, there is a symmetrical and an asymmetrical effect between returns and risks depending on market condition (downside/upside). The focus on the Paris Agreement demonstrates that this event is not neutral concerning the risk transmission. The effects of spillover from oil to clean energy are present before the agreement, while afterwards, we do not find evidence. Finally, the findings provide fresh insights into the relationship between clean energy and Bitcoin. The empirical analysis shows a significant spillover effect of extreme events between the two markets, suggesting a possible substitution effect.

In this paper, we measure the systemic risk with a novel methodology, based on a “spatial-temporal” approach. We propose a new bank systemic risk measure to consider the two components of systemic risk: cross-sectional and time dimension. The aim is to highlight the “time-space dynamics” of contagion, i.e., if the CDS spread of bank i depends on the CDS spread of other banks. To do this, we use an advanced spatial econometrics design with a time-varying spatial dependence that can be interpreted as an index of the degree of cross-sectional spillovers. The findings highlight that the Eurozone banks have strong spatial dependence in the evolution of CDS spread, namely the contagion effect is present and persistent. Moreover, we analyse the role of the European Central Bank in managing contagion risk. We find that monetary policy has been effective in reducing systemic risk. However, the results show that systemic risk does not imply a policy intervention, highlighting how financial stability policy is not yet an objective.

Do tail events in the oil market trigger extreme responses by the clean-energy financial market (and vice versa)? This paper investigates the relationship between oil price and clean-energy stock with a novel methodology, namely extreme events study. The aim is to investigate an asymmetry effect between the response to good versus bad days. The results show how the two markets influence each other more negatively, i.e., extreme negative events significantly impact the other market. Furthermore, we document how the impact of the shock transmitted by oil prices to clean-energy stocks is less than the amount of shock transmitted oppositely. These findings have important implications for investor and renewable energy policies.

Currently, energy consumption has increased exponentially. Using fossil fuels to produce energy generates high shares of carbon dioxide emissions and greenhouse gases. Moreover, financial authorities at the global and European levels have recognized that climate change poses new risks for individual financial institutions and financial stability. The analysis contributes to the literature in two critical ways. First, the research attempts to develop a map of the transition risk of the EU. In detail, it defines an indicator that will identify the transition risk the EU bears. Second, it analyzes any relationships between the CO₂ emissions, economic growth, and the renewable energy of each European country from 1995 to 2020, highlighting the short and long-run relationships. The methodology used is the ARDL. The results show the long-run relationship between GDP, renewable energy consumption, and CO₂ emissions is evident. Indeed, economic growth may increase environmental pollution in Europe, while an increase in using renewable energy may reduce CO₂ emissions. Therefore, this implies the trade-off between economic development and CO₂ emissions. Furthermore, the results indicate the difference in the short-run relationship across countries. However, the results demonstrate that the choice of the European Union to increase the use of renewable energies is more than fair.