Stock Volatility Model and Prediction using GARCH: A Case of Bharti Airtel Ltd

Abstract: This paper investigated the possibility of stock price prediction using time series forecasting. We used the prices of Bharti Airtel, an Indian telecom provider that has had financial difficulties recently. Furthermore, we employ contextual factors (i.e., the decision dilemma of an investor) to choose whether to hold or sell the stock based on expected stock prices, to assist us in comprehending the use of time series methods. To understand the fundamentals, the company's financial performance from 2019 to 2023 has been analyzed. Additionally, we used data collected from Bharti Airtel's daily adjusted closing prices from February 2019 to February 2024. Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are used for the modeling. Keywords: Time Series, Prediction, GARCH, ARIMA, Forecasting Shares JEL Classification Number: D53, G17