Long-Memory Modeling and Forecasting of High-Carbon Intensity Rating Exchange-Traded Funds (ETFs)

Abstract

This paper offers statistical insights into the predictability of returns and volatility for exchange-traded funds (ETFs) with low to high-carbon intensities, utilizing three configurations of long-memory models: autoregressive fractionally integrated moving average (ARFIMA) combined with generalized autoregressive conditional heteroskedasticity (GARCH), ARFIMA integrated with fractionally integrated GARCH, and ARFIMA paired with hyperbolic GARCH. The findings reveal that high-carbon intensity ETFs generally yield higher positive returns and exhibit decreased volatility than their low-carbon intensity counterparts. Additionally, the study identifies volatility clustering, where lagged conditional variances exert a greater influence than significant lagged mean returns. The analysis also demonstrates the presence of positive long-term dependence in the time series of several high- and low-carbon intensity ETFs, indicating that forecasting using fractionally integrated models is feasible. However, the results suggest no definitive differences in the characteristics of high and low-carbon intensity ETFs concerning short-term, intermediate-term, and long-term memory processes, as some ETF datasets yielded insignificant findings. Notably, the papers observe anti-persistent characteristics, which caution investors against holding these ETFs for extended periods or relying heavily on current trends for decision-making.

Received: 20 December 2024 | Revised: 24 June 2025 | Accepted: 9 September 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support this work are available upon reasonable request to the corresponding author.

Author Contribution Statement

John Francis Diaz: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Florian Gerth: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – review & editing. Michael Young: Conceptualization, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization, Project administration.