Abstract This study investigates the impact of AI-driven trading on market volatility, focusing on the role of algorithmic decision-making and energy consumption in shaping financial market dynamics. Using daily data from the S&P 500 index, three econometric models are applied: an OLS regression and a Poisson model to estimate the frequency of extreme price jumps, and a GARCH (1,1) model to analyze volatility clustering. The results indicate that the presence of AI in trading is positively associated with an increase in both market jumps and volatility. Additionally, higher energy consumption linked to AI-driven trading corresponds to greater market turbulence, suggesting that the computational intensity of algorithmic strategies may exacerbate financial instability. The GARCH model confirms that volatility clusters persist, and that AI trading intensifies short-term fluctuations. These findings highlight the dual nature of AI’s influence on financial markets, offering efficiency gains while introducing potential systemic risks. Future regulatory approaches should consider measures to mitigate excessive volatility induced by AI-based trading systems.
Alliata et al. (Tue,) studied this question.
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