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

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  • Cifter, Atilla

    ()
    (Istanbul Kemerburgaz University, School of Economics and Administrative Sciences, Istanbul)

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.

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File URL: http://www.ipe.ro/rjef/rjef2_12/rjef2_2012p127-142.pdf
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Bibliographic Info

Article provided by Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting.

Volume (Year): (2012)
Issue (Month): 2 (June)
Pages: 127-142

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Handle: RePEc:rjr:romjef:v::y:2012:i:2:p:127-142

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Related research

Keywords: volatility forecasting; value-at-risk; asymmetric normal mixture GARCH; reality check.;

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References

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