Modelling Volatility in The Indian Stock Market: A GARCH-Based Analysis of Mutual Funds and Stocks

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.