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.

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.