Suchergebnisse

AbstractThe Machado-Mata decomposition building on quantile regression has been extensively analyzed in the literature focusing on gender wage inequality. In this study, we generalize the Machado-Mata decomposition to the expectile regression framework, which, to the best of our knowledge, has never been applied in this strand of the literature. In contrast, in recent years, expectiles have gained increasing attention in other contexts as an alternative to traditional quantiles, providing useful statistical and computational properties. We flexibly deal with high-dimensional problems by employing the Least Absolute Shrinkage and Selection Operator. The empirical analysis focuses on the gender pay gap in Germany and Italy. We find that depending on the estimation approach (i.e. expectile or quantile regression) the results substantially differ along some regions of the wage distribution, whereas they are similar for others. From a policy perspective, this finding is important as it affects conclusions about glass ceiling and sticky floors.

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.

AbstractIn recent years, companies have increasingly been characterized by environmental, social, and governance (ESG) scores, and investors and academics have raised questions concerning financial performance and investment risks. Now, as the European Banking Authority has acknowledged that ESG risks can potentially impact the economic and financial system, the debate on systemic risk has gained traction. Understanding the relationship between ESG merit and systemic risk is of utmost importance for the stability of the economic and financial system, still, research is limited. Relying on real‐world European and United Stated data, we quantify systemic risk by means of QL‐CoVaR. Empirical analyses of the entire period from 2007 to 2021 show that companies with high ESG scores tend to exhibit low QL‐CoVaR values indicating a positive effect of ESG scores. Such evidence is confirmed by clustering the individual companies into ESG portfolios and focusing on COVID‐19. Additional insights using the individual pillars are also provided.