A number of measures and initiatives have been taken globally to mitigate the environmental threats posed by reliance on fossil fuels and to facilitate the transition to a greener economy. This study aims to examine the extreme tail dependence of green assets on crude oil markets using daily returns of the crude oil and green assets from 30 May 2014 to 20 June 24 2024.
The study employs the time-varying cross-quantilogram method to capture the tail dependence structure between green assets and crude oil markets.
The results reveal a strong positive dependence between green assets (clean energy (CLEN), green bonds (GBs), environmental, social and governance (ESG) and sustainability) and crude oil markets in the extreme tail quantiles and weak positive dependence in the middle quantiles, underscoring the vulnerability of green assets to the adverse effects of crude oil shocks during crisis periods. The weak positive dependence between green assets and crude oil markets during normal periods indicates that investors consider green assets as a viable alternative investment. Further, the reliability and robustness of the findings are confirmed by the robustness checks.
From a practical perspective, the study highlights that green assets are vulnerable to crude oil market shocks during crisis periods. Therefore, to achieve financial stability and sustainable growth, among others, monetary authority and financial regulators should incorporate the dynamics in green assets such as CLEN, GBs, ESG and sustainability into systemic risk stress testing.
While most studies concentrated on the average or linear dependence between green assets and crude oil prices, there is limited understanding of how green assets co-move with oil prices in the tails of their return distributions, particularly during periods of extreme market conditions. Unlike previous studies, our study splits the tail quantile distribution into three categories: extreme left tail, central quantiles and extreme right tail. Each category has 10 quantile points. This approach improves the precision with which tail risk is measured.
