Modelling The Financial Impact of Policy Innovation: GARCH-M Evidence from India’s Textile Sector Pre- And Post-NTTM

Abstract

This study investigates the evolving risk–return dynamics of Bombay Stock Exchange (BSE)-listed textile companies in light of India’s transformative policy shift-specifically, the launch of the National Technical Textiles Mission (NTTM) in 2020-21. Using the Generalized Autoregressive Conditional Heteroskedasticity-in-Mean (GARCH-M) model, the paper evaluates how volatility influences expected returns across two critical policy regimes: the pre-NTTM phase (2015-16 to 2019-20) and the post-NTTM phase (2020-21 to 2024-25). Eleven textile firms, including Raymond, Vardhman Textiles, Arvind, and Bombay Dyeing, were selected based on consistent trading data and sectoral relevance. The analysis reveals significant volatility clustering and asymmetric return behavior, with increased sensitivity to policy reforms in the post-NTTM period. Key indicators such as ARCH effects, kurtosis, and skewness support the presence of non-linear risk characteristics, while GARCH-M coefficients underscore the measurable impact of risk on expected returns. This research contributes to the literature on financial modelling and sectoral policy by highlighting the implications of regulatory interventions on investor behavior and market efficiency in a traditional yet reform-driven industry. The findings have broader relevance for policymakers, institutional investors, and academics engaged in sector-specific volatility modeling and industrial transformation studies.