Trade Policy Shocks and the Indian Equity Market: An Empirical Analysis of U.S. Tariff Changes and Sectoral Stock Returns

This study aims to understand how major US tariff-policy announcements have influenced the prices of Indian equities. The study further explores conditional volatility and financial transmission channels from January 2018 to June 2026. This study employs a combined event-study framework. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) volatility modelling along with a five-variable Structural Vector Autoregression (SVAR) was also used. The study was carried out for a 251 trading day estimation window and three event windows around six policy announcements. The reported results show a statistically significant negative response of the NIFTY 50, with a cumulative average abnormal return (CAAR) of −1.42% over the [−1,+1] window. Sectoral responses are heterogeneous: NIFTY Metal (−3.65%), and NIFTY IT (−2.84%) show larger negative responses, whereas NIFTY FMCG (−0.22%) is statistically insignificant. GARCH estimates indicate positive event-related variance shifts for NIFTY 50, IT and Metal, but not FMCG. Under the specified Cholesky identification, the 10-day forecast-error variance of NIFTY IT is associated with FPI-flow and USD/INR shocks accounting for 24.3% and 18.7%, respectively, while TPU shocks account for 29.1% of NIFTY Metal variance. The results showcase heterogeneous short-run responses across the selected sectors while the transmission estimates highlight conditionality on the specified identification structure. These findings indicate that U.S. tariff announcements are associated with heterogeneous short-run spillovers into Indian equities, with financial channels complementing direct trade exposure.

Tariff policy, Trade policy, Indian equity market, GARCH, FPI flows.

1. Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly, Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024 [Google Scholar] [Crossref]

2. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1 [Google Scholar] [Crossref]

3. Caldara, D., Iacoviello, M., Molligo, P., Prestipino, A., & Raffo, A. (2020). The economic effects of trade policy uncertainty. Journal of Monetary Economics, 109, 38–59. https://doi.org/10.1016/j.jmoneco.2019.11.002 [Google Scholar] [Crossref]

4. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773 [Google Scholar] [Crossref]

5. Faniband, M., & Singh, S. (2026). Volatility spillover effects from US and China trade policy uncertainty to the stock market: Evidence from an emerging economy. Cogent Economics & Finance, 14(1), Article 2639204. https://doi.org/10.1080/23322039.2026.2639204 [Google Scholar] [Crossref]

6. Kumar, R., & Moussa, F. (2025). The impact of US reciprocal tariff announcements on global stock markets: An event study analysis. International Journal of Accounting, Business and Finance, 4(1), 26–37. https://doi.org/10.55429/ijabf.v4i1.205 [Google Scholar] [Crossref]

7. MacKinlay, A. C. (1997). Event studies in economics and finance. Journal of Economic Literature, 35(1), 13–39. [Google Scholar] [Crossref]

8. Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48. https://doi.org/10.2307/1912017 [Google Scholar] [Crossref]

9. Yu, D., Tang, J., & Zhou, M. (2026). Trade policy uncertainty and stock returns: A tale of two periods. International Review of Financial Analysis, 109, 104789. https://doi.org/10.1016/j.irfa.2025.104789 [Google Scholar] [Crossref]

• A Study to Assess the Impact of a Nurse-Led Educational Intervention on Knowledge Regarding Oral Health Among Primigravida in Selected Hospitals of Navi Mumbai
• Attitude towards Mathematics and Science in Relation to STEM Career Aspirations among Senior Secondary School Students
• AI-Driven Personalized Learning in Educational Systems: A Framework for Adaptive Learning and Decision Support
• Institutionalizing Indigenous Peoples Education in Philippine Basic Education: Development of the Integrated Institutionalization Framework for Indigenous Peoples Education (IIF-IPEd)
• Toward an Integrated Theory of Enterprise Risk Management: A Multi-Theoretical Conceptual Framework