Nigerian Stock Index: A search for optimal GARCH model using high frequency data

This paper attempts to fit the best Generalized Autoregressive Conditional Heteroscedastic (GARCH) model for All Share Index (ASI) of Nigerian Stock Exchange (NSE) returns. A search is made on various GARCH variants specified on the assumptions of stationarity and asymmetry. Fractionally integrated types are also considered to capture the possibility of return series having property of long range dependency. The parameter estimations are carried out on the assumptions of normality and non-normality of GARCH innovations, with models and forecasts evaluated using information criteria and loss functions respectively. Under normality assumption, Hyperbolic GARCH (HYGARCH(1,d,1)) model is selected and Integrated GARCH (IGARCH(1,1)) and Fractionally Integrated Exponential GARCH (FIEGARCH(1,d,1)) models selected under the Student t and Generalized Error Distributions. Of these three models, HYGARCH(1,d,1) is the overall best model.