Volatility spillover between the energy and AI markets: a TVP-VAR approach

With the growing energy consumption from AI model training and use, as well as the increasing integration of AI into the energy industry, the AI and energy markets have grown to become more interconnected. This paper uses a Time-Varying Parameter Vector Autoregression (TVP-VAR) model to analyze the volatility spillover between AI and Energy stocks. I find evidence that the AI industry acts as a net transmitter of volatility to the traditional energy market, but not the clean energymarket. I also find that volatility spillover between the energy and AI market increases during periods of economic uncertainty. These results are relevant to guiding infrastructure planning and investing in the AI and energy sectors. JEL classifications C32, G10, O3, Q01, Q42, Q43