Communications for Statistical Applications and Methods

  • 제20권5호
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  • Pages.395-404
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  • 2013
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Skewness of Gaussian Mixture Absolute Value GARCH(1, 1) Model

  • Lee, Taewook (Department of Statistics, Hankuk University of Foreign Studies)
  • 투고 : 2013.06.26
  • 심사 : 2013.08.07
  • 발행 : 2013.09.30
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초록

This paper studies the skewness of the absolute value GARCH(1, 1) models with Gaussian mixture innovations (Gaussian mixture AVGARCH(1, 1) models). The maximum estimated-likelihood estimator (MELE) employed (a two- step estimation method in order to estimate the skewness of Gaussian mixture AVGARCH(1, 1) models. Through the real data analysis, the adequacy of adopting Gaussian mixture innovations is exhibited in reflecting the skewness of two major Korean stock indices.

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참고문헌

  1. Ding, Z. and Granger, C. W. J. (1996). Modeling volatility persistence of speculative returns: A new approach, Journal of Econometrics, 73, 185-215. https://doi.org/10.1016/0304-4076(95)01737-2
  2. Ding, Z., Granger, C. W. J. and Engle, R. F. (1993). A long memory property of stock market returns and a new model, Journal of Empirical Finance, 1, 83-106. https://doi.org/10.1016/0927-5398(93)90006-D
  3. Francq, C. and Zakoian, J. (2004). Maximum likelihood estimation of pure GARCH and ARMAGARCH processes, Bernoulli, 10, 605-637. https://doi.org/10.3150/bj/1093265632
  4. Goncalves, E., Leite, J. and Mendes-Lopez, N. (2009). A mathematical approach to detect the Taylor property in TARCH processes, IEEE Signal Processing Letters, 79, 602-610.
  5. Ha, J. and Lee, T. (2011). NM-QELE for ARMA-GARCH Models with non-Gaussian innovations, Statistics and Probability Letters, 81, 694-703. https://doi.org/10.1016/j.spl.2011.02.004
  6. Haas, M. (2009). Persistence in volatility, conditional kurtosis, and the Taylor property in absolute value GARCH processes, IEEE Signal Processing Letters, 79, 1674-1683.
  7. Haas, M., Mittnik, S. and Paolella, M. S. (2004). Mixed normal conditional heteroskedasticity, Journal of Financial Econometrics, 2, 211-250. https://doi.org/10.1093/jjfinec/nbh009
  8. He, C. and Terasvirta, T. (1999). Properties of moments of a family of GARCH processes, Journal of Econometrics, 92, 173-192. https://doi.org/10.1016/S0304-4076(98)00089-X
  9. Lee, S. and Lee, T. (2012). Inference for Box-Cox transformed threshold GARCH models with nuisance parameters, Scandinavian Journal of Statistics, 39, 568-589. https://doi.org/10.1111/j.1467-9469.2012.00805.x
  10. Lee, S. and Noh, J. (2013). An empirical study on explosive volatility test with possibly nonstationary GARCH(1, 1) models, Communications for Statistical Applications and Methods, 20, 207-215. https://doi.org/10.5351/CSAM.2013.20.3.207
  11. Lee, S., Park, S. and Lee, T. (2009). A note on the Jarque-Bera Normality Test for GARCH Innovations, Journal of the Korean Statistical Society, 39, 93-102. https://doi.org/10.1016/j.jkss.2009.04.005
  12. Lee, T. and Lee, S. (2009). Normal mixture quasi-maximum Likelihood estimator for GARCH Models, Scandinavian Journal of Statistics, 36, 157-170.
  13. Leroux, B. G. (1992). Consistent estimation of a mixing distribution, The Annals of Statistics, 20, 1350-1360. https://doi.org/10.1214/aos/1176348772
  14. McLachlan, G. and Peel, D. (2000). Finite Mixture Models, Wiley, New York.
  15. Pan, J., Wang, H. and Tong, H. (2008). Estimation and tests for power-transformed and threshold GARCH models, Journal of Econometrics, 142, 352-378. https://doi.org/10.1016/j.jeconom.2007.06.004
  16. Taylor, S. J. (1988). Modelling Financial Time Series, John Wiley and Sons, Chichester.