Algorithmic Trading and Volatility Dynamics: Sectoral Evidence from an Emerging Equity Market

Abstract

Algorithmic Trading (AT) has emerged as a transformative force in modern financial markets, fundamentally altering trade execution mechanisms, market liquidity, and volatility dynamics. The rapid adoption of automated trading systems, particularly in emerging markets like India, has intensified concerns regarding market stability, price discovery, and short-term volatility shocks. This study examines the impact of algorithmic trading on the volatility of top sectoral leaders within the NIFTY50 index, which represents the most liquid and influential firms across key sectors of the Indian economy. Using high-frequency market data from the National Stock Exchange (NSE), the study employs the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family of models to capture time-varying volatility patterns and assess the role of algorithmic trading intensity in shaping return volatility. Adopting a cross-sectional and sectoral approach, the research investigates whether algorithmic trading exacerbates volatility through rapid order placements, speculative behavior, and flash-crash-like events. The findings are expected to provide nuanced insights into the dual role of algorithmic trading in influencing volatility—both stabilizing and destabilizing—across sectoral leaders of the NIFTY50. The study offers significant implications for regulators, institutional investors, and policymakers in designing balanced regulatory frameworks that promote technological innovation while safeguarding market stability in developing economies.