SKEW NORMAL AND SKEW STUDENT-T DISTRIBUTIONS ON GARCH(1,1) MODEL

The Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) type models have become important tools in financial application since their ability to estimate the volatility of time series data. In empirical literature, presence skewness and heavy-tails impacts on how well GARCH-type able capture market sufficiently. This study estimates asset returns based GARCH(1,1) model assuming Skew Normal Student-t distributions for errors. are applied daily FTSE100 IBEX35 stock indices from January 2000 December 2017. parameters estimated by using Reduced Gradient Non-Linear method Excel’s Solver also Adaptive Random Walk Metropolis implemented Matlab. estimation results fitting real data demonstrate that is a promising way estimating with non-Normal distribution, indicated accuracy Solver. performance evaluated log-likelihood ratio test it indicates distribution provides best fitting, followed Student-t, Skew-Normal, distributions.