Volatility forecasting using the GARCH framework on the OMXS30 and MIB30 stock indices

There are many models on the market that claim to predict changes in financial assets as stocks on the Stockholm stock exchange (OMXS30) and the Milano stock exchange index (MIB30). Which of these models gives the best forecasts for further risk management purposes for the period 31st of October 2003 to 30th of December 2008? Is the GARCH framework more successful in forecasting volatility than more simple models as the Random Walk, Moving Average or the Exponentially Weighted Moving Average? The purpose of this study is to find and investigate different volatility forecasting models and especially GARCH models that have been developed during the years. These models are then used to forecast the volatility on the OMXS30 and the MIB30 indices. The purpose of this study is also to find the model or models that are best suited for further risk management purposes by running the models through diagnostics checks. Daily prices together with the highest and lowest prices during one trading day for the OMXS30 and MIB30 indices were collected from Bloomberg for the period 31st of October 2003 to 30th of December 2008. These data were then processed in Microsoft Excel and Quantitative Micro Software EViews to find the most successful model in forecasting volatility for each of the two indices. The forecasting was performed on-step ahead for the period 1st of July 2008 to 30th of December 2008. This study has examined the forecasting ability of various models on the OMXS30 and MIB30 indices. The models were the Random Walk (RW), the Moving Average (MA), the Exponentially Weighted Moving Average (EWMA), ARCH, GARCH, EGARCH, GJR-GARCH and APGARCH. The results suggest that the best performing model was EGARCH(1,1) for both indices