References
- Billingsley, P. 1961. Statistical inference for Markov processes. Chicago, IL: The University of Chicago Press.
- Chen, C. W. S., W. Zona, S. Songsak, and L. Sangyeol. 2017. Pair trading based on quantile forecasting of smooth transition GARCH models. North American Journal of Economics and Finance 39 (2017):38–55. doi:https://doi.org/10.1016/j.najef.2016.10.015.
- Chen, X., and Y. Fan. 2006. Estimation of copula-based semiparametric time series models. Journal of Econometrics 130 (2):307–35. doi:https://doi.org/10.1016/j.jeconom.2005.03.004.
- Curto, J., J. Pinto, and G. Tavares. 2009. Modeling stock markets volatility using GARCH models with normal, students t and stable Paretian distributions. Statistical Papers 50 (2):311–21. doi:https://doi.org/10.1007/s00362-007-0080-5.
- Darsow, W. F., B. Nguyen, and E. T. Olsen. 1992. Copulas and Markov processes. Illinois Journal of Mathematics 36 (4):600–42. doi:https://doi.org/10.1215/ijm/1255987328.
- Emura, T., T. H. Long, and L. H. Sun. 2017. R routines performing estimation and statistical process control under copula-based time series models. Communications in Statistics - Simulation and Computation 46 (4):3067–87. doi:https://doi.org/10.1080/03610918.2015.1073303.
- Everitt, B. S. 1996. An introduction to finite mixture distributions. Statistical Methods in Medical Research 5 (2):107–27. doi:https://doi.org/10.1177/096228029600500202.
- Frees, E. W., and E. A. Valdez. 1998. Understanding the relationships using copulas. North American Actuarial Journal 2 (1):1–25. doi:https://doi.org/10.1080/10920277.1998.10595667.
- Huang, X. W., and T. Emura. 2019. Model diagnostic procedures for copula-based Markov chain models for statistical process control. Communications in Statistics - Simulation and Computation. doi:https://doi.org/10.1080/03610918.2019.1602647.
- Jarque, C. M., and A. K. Bera. 1987. A test for normality of observations and regression residuals. International Statistical Review / Revue Internationale de Statistique 55 (2):163–72. doi:https://doi.org/10.2307/1403192.
- Joe, H. 1997. Multivariate models and dependence. London, UK: Chapman & Hall.
- Kim, J.-M., J. Baik, and M. Reller. 2018. Control charts of mean and variance using copula Markov SPC and conditional distribution by copula. Communications in Statistics - Simulation and Computation. doi:https://doi.org/10.1080/03610918.2018.1547404.
- Kim, J.-M., and S.-Y. Hwang. 2017. Directional dependence via Gaussian copula beta regression model with asymmetric GARCH marginals. Communications in Statistics - Simulation and Computation 46 (10):7639–53. doi:https://doi.org/10.1080/03610918.2016.1248572.
- Long, T. H., and T. Emura. 2014. A control chart using copula-based Markov chain models. Journal of the Chinese Statistical Association 52 (4):466–96.
- MacDonald, I. L. 2014. Does Newton-Raphson really fail? Statistical Methods in Medical Research 23 (3):308–11. doi:https://doi.org/10.1177/0962280213497329.
- Nelsen, R. B. 2006. An introduction to copulas. 2nd ed. Springer Series in Statistics. New York, NY: Springer-Verlag.
- Seo, B., and D. Kim. 2012. Root selection in normal mixture models. Computational Statistics & Data Analysis 56 (8):2454–70. doi:https://doi.org/10.1016/j.csda.2012.01.022.
- Sun, L.-H., C.-S. Lee, and T. Emura. 2018. A Bayesian inference for time series via copula-based Markov chain models. Communications in Statistics - Simulation and Computation. doi:https://doi.org/10.1080/03610918.2018.1529241.
- Platen, E., and R. Rendek. 2008. Empirical evidence on student-t log-returns of diversified world stock indices. Journal of Statistical Theory and Practice 2 (2):233–51. doi:https://doi.org/10.1080/15598608.2008.10411873.
- Zangari, P. 1996. An improved methodology for measuring VaR. Risk Metrics Monitor 2nd quarter, Reuters/J.P. Morgan, 7–25.