Predictive power of newfangled GARCH models in assessing stock pricevolatility of Indian textile companies

This research endeavours to contribute to the existing body of knowledge by assessing the predictive power of various GARCH models in the specific context of Indian textile companies listed on stock exchanges. The GARCH family encompasses several models, each designed to address specific aspects of volatility dynamics. By evaluating the performance of these models against historical stock price data, we aim to shed light on their efficacy in forecasting volatility patterns and enhancing risk management strategies for investors in the Indian textile sector by applying symmetric and asymmetric models, namely: FIGARCH, FIEGARCH, GJR-GACRH, EGARCH and GARCH (1.1). The object of the study includes quantitative analysis, estimation and forecasting of daily volatility with Normal, Students-t distributions and generalized error distribution constructs of various Indian textile market i.e. KPR Mill Limited (NLKPRM), The Trident Group (NLTRIE), Page industry limited (NLPAGE), Welspun India Limited (NLWLSP) and, Alok Industries Limited (NLALOK). The objective is to discern the impact of the global financial crisis on the linkages across these textile markets. The sample data spans a long period from April 2013 to May 2023 and includes the COVID-19 pandemic, the war between Russia and Ukraine, Current conflicts in the Middle East and climate risk.