Modeling and predicting of different stock markets with GARCH model

This paper is mainly talking about several volatility models and its ability to predict and capture the distinctive characteristics of conditional variance about the empirical financial data. In my paper, I choose basic GARCH model and two important models of the GARCH family which are E-GARCH model and GJR-GARCH model to estimate. At the same time, in order to acquire the forecasting performance, I consider to use two different distributions on error term: normal distribution and student-t distribution. Finally, for each set of empirical stock price, I could get the best model to predict the conditional variance of the stock return based on comparing the Root Mean Square Error (RMSE)’s values of different models. Here, I select several main global stock markets indexes: NASDAQ’s daily index (America), Standard and Poor’s 500 daily index (America), FTSE100 daily index (UK), HANG SENG daily index (Hong Kong) and NIKKEI daily index (Japan).