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September 16, 2025Open Access

Volatility Forecasting Using GARCH Models in Emerging Stock Markets: A Study of India

AAArif Abdelwhab AliBRAC University SDSardauna Abdul-Sobur Dembo

Key Points

EGARCH and TGARCH models effectively address asymmetric volatility shocks in the Indian stock market.
The GARCH(1,1) model accurately captures volatility clustering, a key feature in emerging markets.
FIGARCH reveals long-memory effects, though it lacks efficiency in forecasting compared to other models.
Research findings have significant implications for risk management and investment strategies in emerging markets.

Abstract

This study investigates volatility forecasting in the Indian stock market by examining the BSE Sensex and NSE Nifty 50 indices from 2001 to 2025. Using econometric models including GARCH(1,1), EGARCH, TGARCH, and FIGARCH, the research evaluates their effectiveness in capturing volatility clustering, persistence, asymmetry, and long-memory effects. The results reveal that EGARCH and TGARCH outperform in addressing asymmetric volatility shocks, while GARCH(1,1) effectively models volatility clustering. FIGARCH demonstrates the presence of long-memory effects but with limited forecasting efficiency compared to other models. The findings carry significant implications for investors, portfolio managers, and policymakers in emerging markets, providing insights into risk management, investment strategies, and systemic financial stability. This research contributes to the literature by offering comprehensive evidence on the comparative performance of volatility forecasting models in an emerging market context, particularly India.

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