Dynamic nonlinear connectedness between the green bonds, clean energy, and stock price: the impact of the COVID-19 pandemic

Abstract

This paper uses weekly data from July 01, 2011 to July 09, 2021 to examine the dynamic nonlinear connectedness between the green bonds, clean energy, and stock price around the COVID-19 outbreak in the global markets. By building a time-varying parameter vector autoregression model (TVP-VAR), the comparison analyses of pre- and during the COVID-19 sample groups verify the existence of nonlinear and dynamic correlation among the three variables. First, prior to the COVID-19 pandemic, the simultaneous impacts of clean energy on stock price increased over time. Second, the results of impulse responses at different horizons indicate that green bonds lead to a short-term increase of clean energy, and it exerts an increasingly positive impacts after the COVID-19 outbreak. The COVID-19 has weakened the negative impacts of green bonds on stock price in the medium term. Finally, through the analysis of impulse responses at different points, we find that stock prices will rise when clean energy is subjected to a positive shock, and this positive effect is stronger during economic recovery period than in the other two periods.

Dynamic connectedness of green bond with financial markets of European countries under OECD economies

“…Although the COVID-19 pandemic has damaged the global economy, it has also led to an expansion in the allocation of green and renewable energy (Wan et al 2021 ). This has led to increase in the number of green bonds (Chai et al 2022 ). Yi et al ( 2021 ) also confirmed it through their research that COVID-19 has improved the green bond market.…”

Dynamic Nonlinear Connectedness between the Financial Inclusion, Economic Growth, and China’s Poverty Alleviation: Evidence from a Panel VAR Analysis

“…But as we have discussed in Section 3.2, some scholars have questioned this research design. According to Chai et al [112], standard PVAR models with fixed parameters only allow nonlinear effects between variables to be described by IRF, provided that the regression coefficients do not change over different periods of IRF. However, the results of the IRF may be inaccurate due to the time-varying state of the variables.…”

Tail risk connectedness in clean energy and oil financial market

“…Concomitantly, this paper is motivated by uncertainties, which might cause tail interdependencies among different financial assets. Especially, the world has faced unprecedented events such as the COVID-19 pandemic (Huynh et al, 2021 ; Managi et al, 2022 ; Chai et al, 2022 ), Eurozone shocks (Foglia et al, 2022 ), and even the global network (Nguyen & Lambe, 2021 ). Hence, a detailed understanding of how the energy and financial markets are connected would identify the market structure during normal turbulence.…”

“…In our research, using the the Tail-Event driven NETwork (TENET, Härdle et al, 2016 ) risk model, we are able to offer fresh information on the degree of interconnection (spillover effect) between markets, providing a detailed picture of the relationship. Although many works have analysed this relationship using several different methodologies such as the VAR and VECM models (Henriques & Sadorsky, 2008 ; Kumar et al, 2012 ; Managi & Okimoto, 2013 ; Bondia et al, 2016 ; Chai et al, 2022 ); wavelets (Reboredo et al, 2017 ); copulas (Reboredo & Ugolini, 2018 ); GARCH and variants (Sadorsky, 2012a ; Lv et al, 2021 , 2012 , 2014 ) and framework and variants (Ahmad, 2017 ; Ferrer et al, 2018 ; Pham, 2019 ; Nasreen et al, 2020 ; Foglia & Angelini, 2020 ), none of these methods have been able to capture extreme spillover effects from a network perspective at the firm level. Our paper is close to a recent work of Saeed et al.…”