Modelling Volatility in The Indian Stock Market: A GARCH-Based Analysis of Mutual Funds and Stocks
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https://doi.org/10.14419/8bdrpf37
Received date: December 4, 2025
Accepted date: December 19, 2025
Published date: January 16, 2026
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Volatility; Mutual Funds; EGARCH; MGARCH; Nifty Fifty Index -
Abstract
This study investigates the volatility dynamics of equity and debt mutual funds using advanced econometric techniques, specifically GARCH, EGARCH, and MGARCH models. By analysing daily returns of nifty fifty index and selected mutual funds comprising both debt and equity, the research aims to uncover patterns of volatility persistence, sensitivity to market shocks, and the distinct behaviors exhibited by different fund types. The findings reveal that both equity and debt funds display significant, though moderate, volatility clustering, as indicated by a consistent GARCH term across models. The arch term catches the short-term shocks that have a consistent effect on all funds, highlighting the pervasive effect of sudden market events. Notably, equity funds demonstrate a quicker stabilization following shocks, reflecting their adaptive nature, while debt funds exhibit prolonged volatility responses, underscoring their sensitivity to macroeconomic conditions. The MGARCH analysis further distinguishes the volatility profiles within the debt segment, showing that not all debt instruments react similarly to market disturbances. Portfolio managers and investors can use these results as equity funds may be better suited for dynamic investment strategies and higher risk tolerance, whereas debt funds require more conservative management and careful monitoring of external economic factors. The study also discusses the practical challenges and limitations of applying GARCH-family models, such as data constraints, model assumptions, and the omission of exogenous variables. Despite these limitations, the research provides a robust framework for understanding and managing mutual fund volatility, offering actionable insights for optimizing asset allocation, enhancing risk management, and improving investor communication. Future research is encouraged to incorporate broader datasets, alternative modelling approaches, and additional market factors to further refine volatility forecasting and portfolio strategy.
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How to Cite
Ms. Monika, Singh, D. S. ., & Jawa, D. R. . (2026). Modelling Volatility in The Indian Stock Market: A GARCH-Based Analysis of Mutual Funds and Stocks. International Journal of Accounting and Economics Studies, 13(1), 223-231. https://doi.org/10.14419/8bdrpf37
