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

Abstract

This research paper examines how algorithmic trading impacts on the volatility of stock returns of major sectoral leader stocks in the Indian stock market. The study employs the daily data of HDFC, INFY, ITC, LT and RELIANCE to create an algorithmic trading intensity proxy, which is a scaled measure of the turnover and trading activity. An augmented GARCH(1,1) model using the algorithmic trading variable is used to model the conditional volatility and measure the trading volatility relationship. Analytical findings demonstrate that algorithmic trading leads to volatility in highly liquid and information sensitive stocks (INFY, HDFC, LT, and RELIANCE) to a substantial extent whereas defensive stock ITC indicates relatively low sensitivity. The estimated impact coefficients affirm unequal volatility reactions in sectors. Moreover, the analysis of vector autoregression (VAR) indicates that the transmission of volatility and algorithmic trading activity is very strong in the technology and financial stocks, and the feedback is weak in the consumer sectors. These results confirm the opinion that algorithmic trading positively affect market efficiency by incorporating information faster and at the same time increases short-term volatility in actively traded emerging-market equities.