Volatility Forecasting with Asymmetric Normal Mixture Garch Model: Evidence from South Africa

Atilla Cifter

Journal for Economic Forecasting, 2012, issue 2, 127-142

Abstract: 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 NMGARCH 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.

Keywords: volatility forecasting; value-at-risk; asymmetric normal mixture GARCH; reality check. (search for similar items in EconPapers)
JEL-codes: C32 C53 G17 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (1)

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