Showing papers by "Laura Serlenga published in 2023"

The “Greenium” in Green Bonds: How Did It Change with COVID-19?

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.

Testing for correlation between the regressors and factor loadings in heterogeneous panels with interactive effects

Abstract: Abstract A large literature on modelling cross-section dependence in panels has been developed through interactive effects. However, there are areas where research has not really caught on yet. One such area is the one concerned with whether the regressors are correlated with factor loadings or not. This is an important issue because if the regressors are uncorrelated with loadings, we can simply use the consistent two-way fixed effects (FE) estimator without employing any more sophisticated econometric methods such as the principal component (PC) or the common correlated effects estimators. We explore this issue, which has received surprisingly little attention and propose a Hausman-type test to address the matter. Further, we develop two nonparametric variance estimators for the FE and PC estimators as well as their difference, that are robust to the presence of heteroscedasticity, autocorrelation and slope heterogeneity. Under the null hypothesis of no correlation between the regressors and loadings the proposed test follows the $$\chi ^{2}$$ χ 2 distribution asymptotically. Monte Carlo simulation results confirm satisfactory size and power performance of the test even in small samples. Finally, we provide extensive empirical evidence in favour of uncorrelated factor loadings. In this situation, the FE estimator would provide a simple and robust estimation strategy which is invariant to nontrivial computational issues associated with the PC estimator.