Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Dependence structure analysis of KOSPI and NYSE based on time-varying copula models

  • Lee, Sangyeol (Department of Statistics, Seoul National University) ;
  • Kim, Byungsoo (Department of Statistics, Seoul National University)
  • Received : 2013.08.10
  • Accepted : 2013.09.30
  • Published : 2013.11.30
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Abstract

In this study, we analyze the dependence structure of KOSPI and NYSE indices based on a two-step estimation procedure. In the rst step, we adopt ARMA-GARCH models with Gaussian mixture innovations for marginal processes. In the second step, time-varying copula parameters are estimated. By using these, we measure the dependence between the two returns with Kendall's tau and Spearman's rho. The two dependence measures for various copulas are illustrated.

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References

  1. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  2. Cherubini, U., Luciano, E. and Vecchiato, W. (2004). Copula methods in finance, Wiley, West Sussex.
  3. Chiou, S. C. and Tsay, R. S. (2008). A copula-based approach to option pricing and risk assessment. Journal of Data Science, 6, 273-301.
  4. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom in ation. Econometrica, 50, 987-1008. https://doi.org/10.2307/1912773
  5. Francq, C. and Zakoian, J. (2004). Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes. Bernoulli, 10, 605-637. https://doi.org/10.3150/bj/1093265632
  6. Gourieroux, G. and Monfort, A. (1992). Qualitative threshold ARCH models. Journal of Econometrics, 52, 159-199. https://doi.org/10.1016/0304-4076(92)90069-4
  7. Joe, H. (1997). Multivariate models and dependence concepts, Chapman and Hall, London.
  8. Lee, S. and Lee, E. (2012). A study on the information transfer effect among the China stock markets. Journal of the Korean Data & Information Science Society, 23, 1075-1084. https://doi.org/10.7465/jkdi.2012.23.6.1075
  9. Lee, S. and Lee, T. (2008). Robust estimation for the order of finite mixture models. Metrika, 68, 365-390. https://doi.org/10.1007/s00184-007-0168-x
  10. Lee, S. and Lee, T. (2011). Value-at-risk forecasting based on Gaussian mixture ARMA-GARCH model. Journal of Statistical Computation and Simulation, 81, 1131-1144. https://doi.org/10.1080/00949651003752320
  11. Lee, T. and Lee, S. (2009). Normal mixture quasi-maximum likelihood estimator for GARCH models. Scandinavian Journal of Statistics, 36, 157-170.
  12. Nelson, R. (1999). An introduction to copulas, Springer, New York.
  13. Patton, A. J. (2006a). Estimation of multivariate models for time series of possibly different lengths. Journal of Applied Econometrics, 21, 147-173. https://doi.org/10.1002/jae.865
  14. Patton, A. J. (2006b). Modeling asymmetric exchange rate dependence. International Economic Review, 47, 527-556. https://doi.org/10.1111/j.1468-2354.2006.00387.x
  15. Rockinger, M. and Jondeau, E. (2006). Modeling the dynamics of conditional dependency between financial series. In Multi-moment Asset Allocation and Pricing Models, edited by Emmanuel Jurzenco and Betrand Maillet, Wiley, West Sussex, 195-222.
  16. Woodward, W. A., Parr, W. C., Schucany, W. R. and Lindsay, H. (1984). A comparison of minimum distance and maximum likelihood estimation of a mixture proportion. Journal of the American Statistical Association, 79, 590-598. https://doi.org/10.1080/01621459.1984.10478085

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