Volatility forecasting on global stock market indices : Evaluation and comparison of GARCH-family models forecasting performance

Sammanfattning: Volatility is arguably one of the most important measures in financial economics since it is often used as a rough measure of the total risk of financial assets. Many volatility models have been developed to model the process, where the GARCH-family models capture several characteristics that are observed in financial data. An accurate volatility forecast is of great value for monetary policymakers, risk managers, investors and for assessing the price and value of financial derivates. The purpose of this thesis is to evaluate if asymmetric or symmetric GARCH models generate better volatility forecast and specifically which model that is superior. Two symmetric models, the GARCH and IGARCH, and two asymmetric models, the EGARCH and TGARCH are used. Daily volatility forecasts on the returns from 10 stock market indices, where the predicted forecasts cover the period from 2020-03-31 to 2021-03-31, will be compared to the realized volatility by four different evaluation measures. The evidence suggests that the symmetric GARCH models, on average, produce the best volatility forecast and specifically the IGARCH model.