Extreme return connectedness across environmental, social and governance indices and Brent crude oil markets

Purpose This paper aims to examine the extreme return spillover between crude oil and ESG stocks for 10 developed and 11 emerging economies from 4 January 2016 to 3 October 2024. Design/methodology/approach The paper extends the generalized VAR methodology proposed by Diebold and Yilmaz (2012) (DY12) to quantify the dynamics of spillovers across ESG indices and crude oil. The authors use the quantile connectedness approach by Ando et al. (2022) to explore the quantile connectedness with various quantiles (q), such as bearish, normal and bullish market conditions. Findings The critical findings of the paper are as follows: firstly, the study reports extreme spillover at the tails, especially during COVID-19, resulting in asymmetry in tail dependency within the network. Secondly, asymmetry in the tail dependence is maximum during COVID-19. Thirdly, crude oil acts as a major recipient, but the degree of receiving return shocks from ESG market innovations intensifies during extreme market conditions. Lastly, the network analysis depicts the complex market dynamics during the bearish phase mainly for the emerging markets. Originality/value Unlike the previous studies which uses the vector autoregression (VAR) models, cointegration methods, wavelet analysis, cross-correlation techniques, copula approaches and GARCH models which fails to capture the dynamics of return spillovers under extreme market conditions and derived from forecast-error variance decomposition to account for tail-specific dynamics, this study offers a more comprehensive understanding of tail dependence and asymmetry in spillover effects using the median-based quantile VAR (QVAR) approach between crude oil and ESG indices, and tested across 10 developed and 11 emerging markets.