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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.

Suggested Citation

  • Cifter, Atilla, 2012. "Volatility Forecasting with Asymmetric Normal Mixture Garch Model: Evidence from South Africa," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 127-142, June.
    • Handle: RePEc:rjr:romjef:v::y:2012:i:2:p:127-142
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    volatility forecasting; value-at-risk; asymmetric normal mixture GARCH; reality check.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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