Dynamic portfolio with bitcoin, crude oil, artificial intelligence and clean energy indices

Abstract

This study examines the dynamic interconnections and portfolio implications of clean energy ETFs, artificial intelligence (AI) indices, crude oil, and Bitcoin within sustainable and technology-driven financial markets. Using a Time-Varying Parameter Vector Autoregression (TVP-VAR) framework and daily data from January 2019 to December 2024, we analyze time-varying spillovers and construct optimal portfolios based on dynamic connectedness measures. The results show that clean energy and AI-related assets display relatively stable portfolio weights, whereas Bitcoin exhibits highly volatile and generally limited allocations, particularly under risk-averse strategies. Conventional approaches such as the Minimum Variance and Risk Parity portfolios tend to favor traditional assets, while the Maximum Connectedness Portfolio enhances diversification by allocating more weight to weakly connected assets, including Bitcoin and green ETFs. The findings offer practical insights for resilience-oriented and innovation-driven portfolio construction.