# Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH

arXiv: https://arxiv.org/abs/2105.13898 · HTML: https://mlsanity.com/paper/2105.13898/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 2 of 2)

- [Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models](https://mlsanity.com/paper/2009.10819/) (2020)
- [Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model](https://mlsanity.com/paper/2104.06259/) (2021)

## Built on by (most cited first, 12 of 12)

- [Stock Portfolio Optimization Using a Deep Learning LSTM Model](https://mlsanity.com/paper/2111.04709/) (2021)
- [Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model](https://mlsanity.com/paper/2204.01850/) (2022)
- [Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks](https://mlsanity.com/paper/2202.02728/) (2022)
- [Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model](https://mlsanity.com/paper/2111.04976/) (2021)
- [A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks](https://mlsanity.com/paper/2210.00984/) (2022)
- [Optimum Risk Portfolio and Eigen Portfolio: A Comparative Analysis Using Selected Stocks from the Indian Stock Market](https://mlsanity.com/paper/2107.11371/) (2021)
- [Stock Volatility Prediction using Time Series and Deep Learning Approach](https://mlsanity.com/paper/2210.02126/) (2022)
- [DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks](https://mlsanity.com/paper/2210.11532/) (2022)
- [A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market](https://mlsanity.com/paper/2305.17523/) (2023)
- [Portfolio Optimization: A Comparative Study](https://mlsanity.com/paper/2307.05048/) (2023)
- [Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stocks](https://mlsanity.com/paper/2309.13696/) (2023)
- [Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning](https://mlsanity.com/paper/2407.01572/) (2024)

## Abstract (arXiv)

Volatility clustering is an important characteristic that has a significant effect on the behavior of stock markets. However, designing robust models for accurate prediction of future volatilities of stock prices is a very challenging research problem. We present several volatility models based on generalized autoregressive conditional heteroscedasticity (GARCH) framework for modeling the volatility of ten stocks listed in the national stock exchange (NSE) of India. The stocks are selected from the auto sector and the banking sector of the Indian economy, and they have a significant impact on the sectoral index of their respective sectors in the NSE. The historical stock price records from Jan 1, 2010, to Apr 30, 2021, are scraped from the Yahoo Finance website using the DataReader API of the Pandas module in the Python programming language. The GARCH modules are built and fine-tuned on the training data and then tested on the out-of-sample data to evaluate the performance of the models. The analysis of the results shows that asymmetric GARCH models yield more accurate forecasts on the future volatility of stocks.