VOLATILITY FORECASTING WITH ASYMMETRIC NORMAL MIXTURE GARCH MODEL: EVIDENCE FROM SOUTH AFRICA
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This paper investigates the relative performance of the asymmetric normal mixture generalized autoregressive conditional heteroskedasticity (NM-GARCH) and the benchmarked GARCH models with the daily stock market returns of the Johannesburg Stock Exchange, South Africa. The predictive performance of the NM-GARCH model is compared against a set of the GARCH models with the normal, the Student-t, and the skewed Student-t distributions. The empirical results show that the NM-GARCH outperforms all other competing models according to Christoffersen's (1998) tail-loss and White's (2000) reality check tests. This evidence shows that mixture of errors improves the predictive performance of volatility models.