Volatility Forecasting with Asymmetric Normal Mixture Garch Model: Evidence from South Africa
AbstractThis 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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Bibliographic InfoArticle provided by Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting.
Volume (Year): (2012)
Issue (Month): 2 (June)
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volatility forecasting; value-at-risk; asymmetric normal mixture GARCH; reality check.;
Find related papers by 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
- 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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