This paper examines the volatility pattern in Indian stock markets during the time period January 1, 2011 to March 31, 2014 using the daily closing prices of two stock indices, S&P BSE Sensex and CNX Nifty. This paper uses asymmetric GARCH models like Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH) to explain the volatility. Considering the minimum values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), TGARCH model is found to be a superior model for return volatility over EGARCH. The findings suggest that there is no volatility persistence as well as leverage effect in the data during the period under consideration.

Estimation of volatility in the equity market is a crucial issue in economics and finance. Volatility change in the stock prices has many adverse effects on an economy and also investment. Investors therefore have to depend on market estimates of volatility as a barometer of the vulnerability of financial markets. The existence of excess volatility or ‘noise’ also plays an important role in stock markets as a ‘signal’, which serves as an indicator so that investors can predict the future and make effective decisions. Examining volatility is therefore central to risk management in an economy.

As a concept, volatility is simple and intuitive. It measures the variability or dispersion about a central tendency. More meaningfully, it is a measure of how far the current price of an asset deviates from its average past prices. The greater the deviation, the greater is the volatility and the greater is the risk. Basically, volatility can indicate the strength behind a price movement. The study of volatility becomes more important due to the growing linkage of national markets and stock with the rest of the world markets and its property—that of its speedy transmissibility across markets.

Applied Economics Journal, Threshold GARCH (TGARCH), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Estimating, Volatility Pattern, Stock Markets, Exponential GARCH (EGARCH), The Indian Case.