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1.
J Int Money Finance ; 131: 102792, 2023 Mar.
Article in English | MEDLINE | ID: covidwho-2227105

ABSTRACT

This paper adopts quantile regressions to scrutinize the dynamics of green investment funds in relation to the outbreak of the COVID-19 pandemic. We use data on three of the largest green investment funds (BNP PARIBAS Funds Climate Impact, Nordea Global Climate & Environment, and AMUNDI Funds Global Ecology ESG), whose proceeds finance environmental-focused projects. We consider explicitly how different types of COVID-19 measures impact on these green assets. We show evidence that economic support due to COVID-19 has a positive effect on the green assets. The effect is especially strong when the returns are negative. We further report that strigency owing to the pandemic is also positively associated with green investment funds, but again, for negative returns. On the other hand, the effect of confirmed deaths is not as strong shows up mainly at lower quantiles. A similar results applies to infectious disease equity market volatility. We account for the broader macroeconomic environment and subject our models to a battery of sub-sample robustness checks. Our research offers interesting insights in terms of investment and portfolio diversification, that can be applied to the analysis of asset management and policy making.

2.
Journal of International Money and Finance ; : 102785, 2022.
Article in English | ScienceDirect | ID: covidwho-2150098

ABSTRACT

This study examines the association between firms’ ESG reputational risk and financial performance under the EU regulatory policy changes and the COVID-19 period. Analyzing a panel of 1,816 European listed firms during the period 2007-2021, we document evidence that firms with lower ESG reputational risk have reduced information asymmetry, are less financial constrained and perform better. To establish causality, we design a quasi-natural experiment focusing on the 2014/95/EU directive of non-financial disclosing and the COVID-19 exogenous shock. Our findings are robust to several estimation techniques that address endogeneity, self-selection, and model sensitivity.

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