Modelling asymmetric and long memory volatility in green innovation stocks: fiegarch analysis of S&P BSE Greenex
DOI: https://doi.org/10.3846/bmee.2026.24016Abstract
Purpose – This research examines the long-memory volatility within the S&P BSE Greenex, considering increasing investor focus on sustainability and the impact of green innovation on corporate environmental outcomes.
Research methodology – The study analyses volatility using the Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedasticity (FIEGARCH) model.
Findings – The empirical results reveal that higher volatility is negatively associated with expected returns, consistent with the presence of persistent volatility clustering and a leverage effect in green stock returns.
Research limitations – This study does not address the influence of exogenous factors or sector-specific effects, highlighting potential scope for further research.
Practical implications – The study findings highlight the substance of volatility modelling in sustainable investing. The presence of long-memory effects suggests that historical volatility data can aid in forecasting future trends in green stocks. Policymakers are suggested to consider measures to stabilize market conditions while promoting environmental sustainability, as reduced volatility can enhance investor confidence and encourage long-term sustainable investments.
Originality/Value – To the best of the author’s knowledge, no prior research has examined the long-memory volatility of the sustainability index using the FIEGARCH model, highlighting the originality and contribution of this study.
Keywords:
volatility, long memory effects, sustainability, S&P BSE GreenexHow to Cite
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