Hedging Clean/ Dirty Energy with Artificial Intelligence: A Comparison Between DCC and GAS Models

Abstract

From the existing literature, it is important to note that multivariate GARCH models are widely used to forecast correlation and hedging among different kinds of assets, but without providing a comparison with other types of modelling. This paper compares the forecasting performances of classical DCC-GARCH model with that of the novel multivariate Generalized Autoregressive Score (GAS) model in analyzing the volatilities, correlations and hedging effectiveness of Artificial Intelligence (AI) stock ETF with both Clean and Dirty energies. The estimation results show that the degree of connectedness for the Clean-AI pair is more pronounced than that of Dirty-AI. Furthermore, AI-based assets provide significantly better hedging potential and stronger diversification gains for Dirty energy than for Clean energy. DCC-GARCH model effectively captures the persistent hedging structure in Clean energy portfolios, whereas the GAS framework proves more suitable for Dirty energy due to its ability to track abrupt market adjustments and geopolitical shocks. Overall, our empirical results reveal important practical implications