Title: A study on unfolding asymmetric volatility in selected IT stocks in NSE

Authors: K.S. Suryanarayana; V.S. Prasad Kandi; K. Ravi Kiran Yasaswi

Addresses: Department of MBA, KL Business School, Koneru Lakshmaiah Education Foundation, Vaddeswaram Campus, Guntur District-522502, Andhra Pradesh, India ' Department of MBA, KL Business School, Koneru Lakshmaiah Education Foundation, Vaddeswaram Campus, Guntur District-522502, Andhra Pradesh, India ' School of Management Studies, Lakireddy Balireddy College of Engineering, Mylavaram, Andhra Pradesh, India

Abstract: The day trading of Nifty Index futures uses the generalised autoregressive conditionally heteroscedastic (GARCH) model to determine volatility. Our study aims to assess the asymmetric volatility of historical data and forecast future volatility for a small number of carefully chosen giant IT stocks that have been included in the Nifty IT Index often over an extended time. Preliminary tests like the Ljung Box Test and the Lagrange multiplier test are used to get a clear picture of the volatility. The National Stock Exchange, a stand-in for the NSE, was studied for asymmetries. A prior study examined asset prices and volatility in the Indian stocks market. The investigation assessed volatility using asymmetry GARCH. The model limits volatility. EGARCH also captured asymmetric volatility. After determining volatility, the GARCH technique with the GARCH (1, 1) order is used to test statistically. As a result, the Nifty IT Index exhibits asymmetric volatility. This study shows that open interest has a smaller impact on volatility than volume and that noise trading occurs when there is a complete lack of bidirectional causality in any one occurrence.

Keywords: National Stock Exchange of India; information technology; daily returns volatility; Casula relation; generalised autoregressive conditionally heteroscedastic model; GARCH; asymmetric volatility.

DOI: 10.1504/IJEF.2025.145277

International Journal of Electronic Finance, 2025 Vol.14 No.2, pp.214 - 228

Received: 16 Mar 2023
Accepted: 21 Jun 2023
Published online: 31 Mar 2025 *

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