  Volatility clustering is a crucial property that has a substantial impact on
stock market patterns. Nonetheless, developing robust models for accurately
predicting future stock price volatility is a difficult research topic. For
predicting the volatility of three equities listed on India's national stock
market (NSE), we propose multiple volatility models depending on the
generalized autoregressive conditional heteroscedasticity (GARCH),
Glosten-Jagannathan-GARCH (GJR-GARCH), Exponential general autoregressive
conditional heteroskedastic (EGARCH), and LSTM framework. Sector-wise stocks
have been chosen in our study. The sectors which have been considered are
banking, information technology (IT), and pharma. yahoo finance has been used
to obtain stock price data from Jan 2017 to Dec 2021. Among the pulled-out
records, the data from Jan 2017 to Dec 2020 have been taken for training, and
data from 2021 have been chosen for testing our models. The performance of
predicting the volatility of stocks of three sectors has been evaluated by
implementing three different types of GARCH models as well as by the LSTM model
are compared. It has been observed the LSTM performed better in predicting
volatility in pharma over banking and IT sectors. In tandem, it was also
observed that E-GARCH performed better in the case of the banking sector and
for IT and pharma, GJR-GARCH performed better.
