Examination of garch model for determinants of infosys stock returns

The research on asset volatility in financial market is the foundation of finance. To measure and predict asset volatility accurately, Bollerslev built a generalised ARCH (GARCH) model based on the ARCH model. The GARCH process is often preferred by financial modeling professionals because it provides a more real-world context than other forms when trying to predict the prices and returns of financial instruments. It is the general process for a GARCH model involves three steps. The first is to estimate a best-fitting autoregressive model; secondly, compute autocorrelations of the error term and lastly, test for significance. The objective of the study is to GARCH (1,1) model for the volatility of Infosys stock returns and factors influencing the volatility in the returns of Infosys stock returns. The study covers monthly data ranging from Sept. 2009 to Nov.2015 having 98 observations. The empirical investigation considers returns of closing prices of all variables namely Infosys Stock Return as dependent and S&P CNX Nifty and Dow Jones Industrial Average as independent variables. Data for all variables are collected from the official websites of nseindia.com and yahoofinance.com. E-Views is used to analyze the data. It is concluded that despite there is a weakness of this student’s t distribution model about its non-normality of residuals, many suggest that non-normality in the residuals may not be that serious problem for estimation. Hence this model will be used for forecasting.