Resumen de The comparison of the long memory in volatility for carbon and energy exchange-traded funds

Jo-Hui Chen, Pei-Hsuan Hsieh

• This study examines the presence of long memory in carbon and energy ETFs. It employs the Steel index, CBOE Crude Oil Volatility Index (OVX), and S&P500 index in conjunction with the Autoregressive Fractionally Integrated Moving Average (ARFIMA) combined with Fractionally Integrated Generalized Autoregressive Conditionally Heteroskedasticity (FIGARCH) and Hyperbolic Generalised Autoregressive Conditionally Heteroskedasticity (HYGARCH) models to measure and identify the optimal model for testing long memory. The empirical results indicate that both carbon and energy ETFs exhibit a long memory effect, suggesting predictability in these ETFs. A predictable volatility structure facilitates the portfolio risk evaluation and informs long-term investment decisions because a long memory in volatility exists. The empirical results reveal that the Steel and S&P 500 indexes positively impact both carbon and energy ETFs, while OVX hurts energy ETFs. The ARFIMA-HYGARCH model is superior to the ARFIMA-FIGARCH models when testing long memory, incorporating the aforementioned three indices.