THE EFFECT OF ECONOMIC INDICATORS ON THE VOLATILITY OF INDIAN STOCK MARKET: USING INDEPENDENT COMPONENT REGRESSION
Abstract
This paper studies the impact of economic indicators on the volatility of the Indian stock market. Volatility of the most characterizing indicator of the Indian stock market i.e. Nifty has been calculated by using a GARCH (1,1) model. Twelve economic indicators have been taken to see the effect of them on the GARCH volatility of the Indian stock market indicator i.e. Nifty. Since for GDP, only quarterly data is available, for rest of the indicators quarterly average have been taken for the study. While using the linear regression model taking GARCH Volatility of Nifty as the dependent variable and the 12 economic indicators as the independent variables, multicollinearity among most of the economic indicators (7 out of 12) is experienced, so it is not possible to drop all of these variables. Analyzing the data it has been found that no economic indicators are following Normal distribution. To eradicate the multicollinearity, an Independent Component Analysis (ICA) has been adopted to get the independent components of those economic indicators which are showing high multicollinearity. After having the independent components of those 7 economic indicators, a linear regression model has been fitted to the data. It has also been seen that GDP is significant neither in the linear regression model before ICA nor after ICA. So, it is judicious to drop this independent variable (GDP) so as to increase the number of data points, since rest of the variables are having monthly data points. After taking the monthly data of the rest of the 11 indicators, the same set of analyses have been performed and has been seen that the result of the Independent Component Regression has been improved.
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