Estimation of Volatility in BSE-SENSEX by Fitting GARCH (1,1) Model

This paper aimed to investigate the volatility of the Bombay Stock Exchange of India aka BSE Sensex based on the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. The study empirically tested the volatility of SENSEX using daily data for 10 years, between 2009 and 2019. The empirical analysis is based on closing prices of index-daily observations (4833) and provided additional insight regarding volatility patterns. Augmented Dickey-Fuller (ADF) test was used to test for stationarity and GARCH (1, 1) model was employed to estimate volatility. The results of the ADF test revealed that financial data was stationary. Results of the GARCH (1, 1) model stated that there existed persistent and robust volatility. This implied that the index experienced frequent small gains and few significant losses which would lead to high risk and in turn the chance of higher return. To investigate volatility shocks, GARCH methodology, which is an advanced econometric method preferred to depict actual effects was employed.