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

TL;DR

This paper applies GARCH-based models to forecast stock volatility in Indian auto and banking sectors, demonstrating that asymmetric GARCH models provide more accurate predictions based on historical data from 2010 to 2021.

Contribution

It introduces and compares various GARCH models for sector-specific Indian stocks, highlighting the effectiveness of asymmetric models in volatility forecasting.

Findings

Asymmetric GARCH models outperform symmetric ones in prediction accuracy.

Models trained on historical data effectively forecast future volatility.

Sector-specific models capture unique volatility patterns.

Abstract

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.