Modelling Stock Market Volatility and its Impact on Investment Strategies: A GARCH and ARIMA approach

Abstract

The present work aims to examine the accuracy of stock price and volatility in the context of the Nifty 50 index in India by using ARIMA and GARCH models. The linear model ARIMA designed for analysing linear trends and seasonality in the time series data was suitable for determining the price changes but excluded the phenomenon of volatility clustering typical for financial markets. We thus employed the GARCH model which was capable of capturing time varying volatility and provided good forecasts of high and low volatility periods. Its implementation of the two models affords a comprehensive theoretical approach towards as well as evaluation of stock market phenomena, which helps investors in risk control, portfolio balance, and hedging. To highlight, life cycle theory in the study underlines the accented importance of volatility forecasting inherently to the magnitude of its real-life application in unstable markets such as the emerging markets of India. Hybridizing the benefits of trend analysis from ARIMA and volatility modelling from GARCH, this research provides a significant contribution toward improving financial forecasts and optimising investments’ returns.