Time-frequency connectedness and multivariate portfolio strategies among AI, robotics, fintech, and green assets

Abstract This research investigates the time-varying connectedness among non-conventional asset classes, including Artificial Intelligence (AI), robotics, fintech, and green assets (Qgreen). Additionally, it explores hedging benefits by estimating bivariate and multivariate portfolio weights. The study employs the TVP-VAR-based frequency-connectedness approach and incorporates MVP, MCP, and MCoP strategies to construct asset portfolios. The study underscores the significant influence of short-term fluctuations on asset price volatility, highlighting the critical role of AI volatility, which is strongly connected to fintech and green assets. Additionally, the study underscores how global crises, such as COVID-19 and the Russia–Ukraine war, intensify long-term volatility transmission, thereby escalating systemic risk across markets. AI acts as an important spillover channel and, at times, as a modest net transmitter of volatility, likely driven by its rapid sectoral expansion. Consequently, the findings recommend cautious investment exposure to AI and underscore the exigency for robust risk management strategies, such as the MVP, to mitigate potential risks. The study’s findings emphasize the necessity for dynamic hedging and portfolio strategies to manage risks effectively during market volatility. Investors should prioritize sustainable investments, and policymakers should monitor sectoral volatility and support technological advancements to enhance market stability and resilience.