ABSTRACT
This paper focuses on the study of the "greenium”, i.e., the premium on Green Bonds (GBs) vs. Traditional Bonds (TBs) whereby investors accept lower yields of GBs vs. TBs, which is caused by the important difference between them with reference to their contribution to the green transition, specifically paying attention to the influence of the COVID-19 pandemic on it. The conjecture of this paper is that the negative shock of rates due to the pandemic crisis has increased the greenium, as it has also increased the interest in projects of the green transition. In addition, a hypothesis is made that the risk of breaking the green promises might be higher for corporations than for governments and, hence, that the greenium would be lower for corporate GBs than for government GBs. Finally, the possibility that the post-pandemic changes of the greenium might vary depending on individual GBs' liquidity is considered. The empirical analyses provide support for the first two hypotheses but not for the third one. © 2023 by the authors.
ABSTRACT
One of the difficulties faced by policymakers during the COVID-19 outbreak in Italy was the monitoring of the virus diffusion. Due to changes in the criteria and insufficient resources to test all suspected cases, the number of ‘confirmed infected’ rapidly proved to be unreliably reported by official statistics. We explore the possibility of using information obtained from Google Trends to predict the evolution of the epidemic. Following the most recent developments on the statistical analysis of longitudinal data, we estimate a dynamic heterogeneous panel. This approach allows to takes into account the presence of common shocks and unobserved components in the error term both likely to occur in this context. We find that Google queries contain useful information to predict number patients admitted to the intensive care units, number of deaths and excess mortality in Italian regions. Copyright © 2022 Inderscience Enterprises Ltd.
ABSTRACT
One of the difficulties faced by policymakers during the COVID-19 outbreak in Italy was the monitoring of the virus diffusion. Due to changes in the criteria and insufficient resources to test all suspected cases, the number of 'confirmed infected' rapidly proved to be unreliably reported by official statistics. We explore the possibility of using information obtained from Google Trends to predict the evolution of the epidemic. Following the most recent developments on the statistical analysis of longitudinal data, we estimate a dynamic heterogeneous panel. This approach allows to takes into account the presence of common shocks and unobserved components in the error term both likely to occur in this context. We find that Google queries contain useful information to predict number patients admitted to the intensive care units, number of deaths and excess mortality in Italian regions.