The Dynamic Linkages Among Artificial Intelligence, Sustainable Energies, Commodities, and Islamic Investments: Evidence from a Multivariate GARCH Model

Abstract

his study aims to contribute to optimal portfolio theory by examining the dynamic correlations between Artificial Intelligence (AI) and other alternative investments. Optimal portfolio theory focuses on maximizing returns while minimizing risk through efficient asset allocation methods. Drawing on the First Trust Nasdaq Artificial Intelligence and Robotics ETF as a representative proxy, the research investigates to what degree AI-related assets behave in an interdependent manner with other types of alternative investments. To analyze these dynamic correlations, the study employs the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model. The study uses time-series data from March 1, 2019, to January 27, 2025. This econometric model allows the estimation of evolving correlations and volatilities across AI assets and other investments. Our findings indicate that crude oil provides diversification benefits to all asset classes under study, and all types of investors can obtain diversification benefits from Islamic investments, except for investors in Artificial Intelligence ETFs. Furthermore, the Artificial Intelligence ETF can provide diversification benefits to all types of investors under study, except for Islamic investment-based investors. This study incorporates the Artificial Intelligence ETF into a research framework encompassing sustainable energy, commodities, and Islamic investment—a combination rarely explored in prior research—and provides guidance on portfolio diversification strategies for interested parties. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026.