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

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Cifter, Atilla
Ozun, Alper

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Abstract

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 these results shows 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.

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File URL: http://mpra.ub.uni-muenchen.de/2489/
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 2489.

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Date of creation: 01 Jan 2007
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Handle: RePEc:pra:mprapa:2489

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Related research
Keywords: Garch Asymmetric Normal Mixture Garch Kupiec Test Christoffersen Test Emerging markets

Find related papers by JEL classification:
G00 - Financial Economics - - General - - - General
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models

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  1. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June. [Downloadable!] (restricted)
  2. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June. [Downloadable!] (restricted)
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