The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey

The purpose of this study is to test predictive performance of Asymmetric Normal Mixture GARCH (NMAGARCH) and other GARCH models based on Kupiec and Christoffersen tests for Turkish equity market. The empirical results show that the NMAGARCH perform better based on %99 CI out-of-sample forecasting Christoffersen test where GARCH with normal and student-t distribution perform better based on %95 Cl out-of-sample forecasting Christoffersen test and Kupiec test. These results show that none of the model including NMAGARCH outperforms other models in all cases as trading position or confidence intervals and the real implications of these results for Value-at-Risk estimation is that volatility model should be chosen according to confidence interval and trading positions. Besides, NMAGARCH increases predictive performance for higher confidence internal as Basel requires