Cross-quantile risk assessment: The interplay of crude oil, artificial intelligence, clean tech, and other markets

This paper explores the interconnections among oil, artificial intelligence (AI), clean technology, and traditional markets. We apply a novel generalized quantile-on-quantile connectedness method that assesses variable cross-quantile interdependencies, analyzing data from 2018 to 2023. Our study provides a detailed examination of risk transmission dynamics between oil, AI, clean technology, and major markets including equity, debt, and currency. Our findings indicate that tail risk spillovers are more pronounced than median quantiles. In contrast, the analysis shows negative spillovers across these tails in markets for U.S. government debt, the U.S. dollar, and gold. The dynamic risk transmission analysis reveals that while the stock and AI markets generally act as net transmitters of risk across all quantiles, the crude oil and USD index markets consistently receive net risk spillovers, particularly in the right tail of the distribution. Our results suggest that, on average, AI, and clean technology markets, along with the stock markets, are more likely to transfer risk spillovers compared to debt, currency, or other commodity markets. This positions the USD and crude oil as potential buffers against extreme risk transmissions emanating from the AI and clean technology sectors. This study highlights the complex risk dynamics and the pivotal role of oil in the interplay between emerging technologies and traditional financial markets. • Oil, artificial intelligence (AI), clean technology, other assets' linkages are studied. • Novel generalized quantile-on-quantile connectedness method is employed. • Stocks & AI are risk emitters, oil & USD index are receivers of risk spillovers. • AI, clean tech, stocks transmit more risk shocks than debt, currency, commodities. • USD & oil are potential buffers against extreme risk from the AI & clean tech.