TY  - JOUR
TI  - Predictive power of newfangled GARCH models in assessing stock price volatility of Indian textile companies
AU  - Meher, Bharat Kumar
AU  - Kumar, Santosh
AU  - Anand, Abhishek
AU  - Birau, Ramona
AU  - Popescu, Virgil
AU  - Kumar, Sunil
AU  - Ninulescu, Petre Valeriu
AU  - Awais-E-Yazdan, Muhammad
T2  - Industria Textila
AB  - 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.
DA  - 2025/04/24/
PY  - 2025
DO  - 10.35530/IT.076.02.2023138
DP  - DOI.org (Crossref)
VL  - 76
IS  - 02
SP  - 171
EP  - 184
SN  - 12225347
UR  - https://www.revistaindustriatextila.ro/images/2025/2/003%20BHARAT%20KUMAR%20MEHER%20INDUSTRIA%20TEXTILA%20no.2_2025.pdf
Y2  - 2025/04/26/14:11:38
ER  -