Stock Volatility Prediction using Time Series and Deep Learning Approach
Abstract
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.
Cite
@article{arxiv.2210.02126,
title = {Stock Volatility Prediction using Time Series and Deep Learning Approach},
author = {Ananda Chatterjee and Hrisav Bhowmick and Jaydip Sen},
journal= {arXiv preprint arXiv:2210.02126},
year = {2025}
}
Comments
This is the accepted version of the paper in the 2022 IEEE 2nd Mysore Sub Section International Conference, MysuruCon22. The conference will be organized in Mysuore, during October 16-17, 2022. The paper is 6 pages long, and it contains 10 figures and 8 tables