ALBERT

Description: 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.

Description: 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.

Description: 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.

Description: Smart beta strategies across economic regimes seek to address inefficiencies created by market-based indices, thereby enhancing portfolio returns above traditional benchmarks. Our goal is to develop a strategy for re-hedging smart beta portfolios that shows the connection between multi-factor strategies and macroeconomic variables. This is done, first, by analyzing finite correlations between the portfolio weights and macroeconomic variables and, more remarkably, by defining an investment tilting variable. The latter is analyzed with a discriminant analysis approach with a twofold application. The first is the selection of the crucial re-hedging thresholds which generate a strong connection between factors and macroeconomic variables. The second is forecasting portfolio dynamics (gain and loss). The capability of forecasting is even more evident in the COVID-19 period. Analysis is carried out on the iShares US exchange traded fund (ETF) market using monthly data in the period December 2013–May 2020, thereby highlighting the impact of COVID-19.