Stock Market Volatility of Indian Software Companies Listed on NSE: A GARCH Model Approach

Volume 11, Issue 3, 2026

International Journal of Commerce and Management Studies, ISSN 2456-3684

Paper Title

Author Name and Affiliation

Dr. P. Vasavi

Professor, Department of BBA Aviation Management, Andhra Loyola College, Vijayawada

Email address:vasavi.palla74@gmail.com

2. D. Uma Kumari

Asst Professor, Department of Business Administration, Andhra Loyola College, Vijayawada

Email address:umapolisetty@gmail.com

Abstract

The stock market plays a vital role in economic development by facilitating capital formation and providing investment opportunities. Volatility in stock prices is a key indicator of market risk and uncertainty, making its analysis essential for investors and financial analysts. Although numerous studies have examined stock market volatility in India, most have focused on banking stocks or market indices, with limited attention given to software companies listed on the National Stock Exchange (NSE) using econometric models such as GARCH. To address this research gap, the present study examines the volatility behavior of five leading software companies—Tata Consultancy Services (TCS), Infosys, Wipro, HCL Technologies, and Tech Mahindra—during the period from April 2015 to March 2020. Daily stock price data were collected from Yahoo Finance and analyzed using the Generalized Autoregressive ConditionalHeteroscedasticity (GARCH (1,1)) model in R programming with the quantmod and rugarch packages. The findings reveal that Wipro exhibited the lowest volatility, indicating relatively stable stock performance, whereas Tech Mahindra and Infosys experienced comparatively higher volatility. Volatility forecasts further indicate a gradual decline in market fluctuations during the forecast period. The study confirms the effectiveness of the GARCH model in capturing volatility clustering in the Indian software sector. The findings provide practical insights for investors in portfolio diversification and risk management, assist portfolio managers in developing informed investment strategies, and support policymakers in understanding volatility patterns for improving market stability and regulatory decision-making.

Keywords

Stock Market Volatility, NSE, GARCH Model, Software Companies, Forecasting, Indian IT Sector

References

E. Balaban and A. Bayar, “Stock Returns and Volatility: Empirical Evidence from Emerging Markets,” 2005.
K. R. French, “Stock Returns and Volatility,” Journal of Financial Economics, vol. 17, no. 1, pp. 3–29, 1987.
I. Ali, “Volatility Clustering and Leverage Effect in Indian Stock Markets,” 2016.
J. K. John and R. Amudha, “Volatility Behaviour of NSE Nifty Stocks using GARCH Models,” 2019.
R. Birau and J. Trivedi, “Long-term Volatility Analysis of NSE CNX-100 Index,” 2015.
T. Bollerslev, “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, vol. 31, no. 3, pp. 307–327, 1986.
R.F.Engle, “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, vol. 50, no. 4, pp. 987–1007, 1982.
C. Brooks, Introductory Econometrics for Finance. Cambridge, U.K.: Cambridge University Press, 2019.
S. K. Sharma and P. Gupta, “Forecasting Stock Market Volatility in India Using GARCH Family Models: Evidence from NSE Sectoral Indices,” Journal of Risk and Financial Management, vol. 16, no. 8, 2023.
M. Kumar and R. Singh, “Volatility Modelling of Indian Equity Markets: A Comparative Analysis of GARCH, EGARCH and TGARCH Models,” International Review of Economics & Finance, vol. 89, pp. 250–266, 2024.
A. Patel and S. Mehta, “Stock Market Volatility and Risk Forecasting in Emerging Markets: Evidence from the Indian IT Sector,” Finance Research Letters, vol. 63, 2024.
P. Das and S. Banerjee, “Time-Varying Volatility and Market Efficiency in the Indian Stock Market: Evidence from GARCH Models,” Economic Analysis and Policy, vol. 84, pp. 415–428, 2025.
N. Verma and A. Joshi, “Machine Learning and GARCH-Based Volatility Forecasting in Emerging Equity Markets,” Research in International Business and Finance, vol. 72, 2025.

DOI

DOI: https://doi.org/10.67061/ijcams.2026.vol.11.issue.03.8124

Download PDF