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AbstractIn this article, we study the relevance of green finance from a portfolio and a network perspective. The estimates are derived from a regularized graphical model, which allows us to deal with two important issues. First, we refer to the curse of dimensionality, as we focus on a relatively large set of companies. Second, we explicitly take into account the heavy‐tailed distributions of financial time series, which reflect the impact of crises and recessions. Focusing on a time interval spanning across well‐known tail events, from the US subprime crisis to the recent outbreak of the COVID‐19 pandemic, we show that the selected green stocks offer a relevant contribution to the minimization of the overall portfolio risk. Moreover, they outperform the gray assets in terms of risk, profitability, and risk‐adjusted return in a statistically significant way. These findings are consistent with the estimates obtained from the network analysis. Indeed, the gray stocks exhibit a greater connection within the dynamic networks and, then, are more exposed to the risk of a greater propagation of negative spillover effects during stressed periods. Interestingly, the relevance of the green stocks increases when moving from the standard Gaussian to the leptokurtic setting. The policy implications suggested by these results induce policymakers to undertake synergistic interventions with private finance aimed at supporting a green economy and environmentally responsible companies.

Summarization: Environmental policy measures to reduce greenhouse gases emission to mitigate climate change are common practice in many developed countries, encompassing both technology-push measures and demand-pull measures. The effectiveness of such policies continues to need investigation, especially when shortsighted measures lead to suboptimal investments in the absence of a credible government commitment. The aim of this paper is to assess the effectiveness of a comprehensive strategy for renewable energy sources in a dynamic stochastic general equilibrium model estimated for the Euro area using Bayesian estimation techniques, which includes carbon tax and subsidy measures. To this end, we compare the cost-effectiveness of technology-push measures and demand-pull measures. Our findings show that the environmental policy based on technology-push measures may produce better dynamic effects than demand-pull measures based on a subsidy policy of equal monetary amount. In fact, RES price parity is estimated to occur sooner by implementing technology-push measures than demand-pull measures. ; Παρουσιάστηκε στο: Renewable Energy