The Impact of Algorithmic and High-Frequency Trading on Stock Market Volatility: An Empirical Analysis of Global Equity Markets

Abstract

Purpose: The proliferation of algorithmic trading (AT) and high-frequency trading (HFT) across global equity markets has fundamentally transformed the structure of modern financial markets. This paper examines how AT/HFT intensity influences stock market volatility across major global exchanges — namely the NYSE/Nasdaq (U.S.), London Stock Exchange (UK), Euronext (EU), Japan Exchange Group (JPX), Hong Kong Exchanges (HKEX), and the National Stock Exchange of India (NSE) — using high-frequency trade and quote data over the period 2010–2024. We construct multiple HFT intensity proxies including message traffic ratios, order cancellation intensity, and order-flow imbalance (OFI), and pair them with noise-robust realised volatility measures including bipower variation and jump components. To address endogeneity inherent in the AT/HFT–volatility relationship, we exploit discrete, quasi-exogenous regulatory interventions: the SEC's Market Access Rule (Rule 15c3-5), MiFID II's algorithmic trading provisions, and SEBI's successive algorithmic trading and co-location circulars in India. Employing GARCH(1,1) and E-GARCH models in conjunction with panel regressions and event-study difference-in-differences designs, we find that message-traffic-based HFT proxies are negatively associated with realised variance, while cancellation intensity is positively associated with short-term volatility spikes. Leverage effects are identified particularly around the 2020 COVID-driven volatility episode. Regulatory access controls appear to moderate — though not eliminate — HFT's amplification of jump volatility. These findings carry meaningful implications for market design policy, exchange infrastructure governance, and investor risk management across both developed and emerging equity markets.