Liquidity at the Speed of AI: Algorithmic Trading and Systemic Risk Amplification

Arindam Nag

MPRA Paper from University Library of Munich, Germany

Abstract: This paper investigates whether artificial intelligence amplifies systemic risk in equity markets using daily data spanning February 2023 to December 2025, comprising 721 observations across the CBOE Volatility Index, S&P 500 and NASDAQ Composite returns, abnormal trading volume, and the Amihud illiquidity ratio. Employing descriptive statistical analysis, an event study framework, OLS regression with Newey-West HAC-corrected standard errors, and a six-lag Vector Autoregression, the results provide evidence broadly consistent with systemic risk amplification through the liquidity withdrawal channel. The regression results indicate that market illiquidity, as measured by the Amihud ratio, is a statistically significant predictor of volatility (coefficient = 1,144,957; p

Keywords: Artificial Intelligence; Algorithmic Trading; Systemic Risk; Market Volatility; Financial Stability; Liquidity Risk (search for similar items in EconPapers)
JEL-codes: G0 G10 G14 G18 G3 G33 O33 (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/128853/1/MPRA_paper_128853.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:128853

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().