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This paper examines the performance of different kind of GARCH models with Gaussian, Student-t and generalized error distribution for Colombo Stock Exchange (CSE), in Sri Lanka. Analyzing the daily closing price index of CSE from January 02, 2007 to March 10, 2013. It was found that the Asymmetric GARCH models give better result than symmetric GARCH model. According to distributional assumption these models under Student-t as well as generalized error provided better fit than normal distributional assumption. The Non-Parametric Specification test suggest that the GARCH, EGARCH, TARCH and APARCH models with Student-t distributional assumption are the most successful model for CSE. Engle (1982) proposed to model time-varying conditional variance with the Auto Regressive Conditional Heteroskedasticity (ARCH) processes that used past disturbances to model the variance of the series. Early empirical evidence showed that high ARCH order has to be selected in order to catch the dynamic of the conditional variance. The Generalized ARCH (GARCH) model of Bollerslev (1986) was an answer to this issue. It was based on an infinite ARCH specification and it allows reducing the number of estimated parameters from

Choosing the Best Performing Garch Model for Sri Lanka Stock Market by Non-Parametric Specification Test