Financial Risk Management
Application of GARCH Models for Modeling Stock Market Volatility: An Empirical Study

Article Details
Pub. Date : Jun, 2019
Product Name : The IUP Journal of Financial Risk Management
Product Type : Article
Product Code : IJFRM41906
Author Name : N Shabarisha and J Madegowda
Availability : YES
Subject/Domain : Finance Management
Download Format : PDF Format
No. of Pages : 13



Return is the major attribute of an investment asset which can be construed as a random variable, and the ‘variability in return’ can be interpreted as volatility. Forecasting volatility and modeling it are the most prolific areas for research. This paper empirically investigates the conditional variance (volatility) pattern in Indian stock market based on financial time series data that consists of daily closing prices of CNX Nifty 50 market index for 10 years from April 2006 to March 2016. For the purpose of estimating conditional variance (volatility) in the daily returns of the index, Autoregressive Conditional Heteroskedasticity (ARCH) models are employed. Both symmetric and asymmetric models are used to capture stylized facts about CNX Nifty 50 market index returns such as volatility clustering and leverage effect. The findings of the study show that the asymmetric models are a better fit than symmetric models, confirming the presence of volatility clustering and leverage effect.


‘Volatility’ (conditional variance) is a key factor in pricing financial derivative instruments. It may be noted that volatility refers to the spread of all likely outcomes of an uncertain variable (Poon, 2005). Volatility forecasting and modeling is, therefore, decisive for option pricing, management of risk and for portfolio management. Statistically, it (i.e., volatility) is frequently measured as the sample standard deviation as: