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Time Series Volatility Forecasting Using Linear Regression and GARCH

Returns and volatilities of prices are very important parameters in finance. In many cases it is interesting to predict future developments of volatilities. Theory and practice provide different methods and techniques for this problem. This paper uses a linear regression and a GARCH(1,1) model to predict volatility for data of the Dow Jones Industrial Average for the period between 6th April 2004 and 4th December 2009. The investigations aim to analyze, how the volatility changed before and after financial crisis and if the mentioned models are able to be used under circumstances of higher volatility.