References
- Brechmann, E. C. and Czado, C. (2015). COPAR-multivariate time series modeling using the copula autoregressive model, Applied Stochastic Models in Business and Industry, 31, 495-514. https://doi.org/10.1002/asmb.2043
- Brockwell, P. J. and Davis, R. A. (2006). Time Series: Theory and Methods, Second edition, Springer.
- Chan, N. H., Chen, J., Chen, X., Fan, Y., and Peng, L. (2009). Statistical inference for multivariate residual copula of GARCH models, Statistica Sinica, 19, 53-70.
- Chen, X. and Fan, Y. (2006a). Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification, Journal of Econometrics, 135, 125-154. https://doi.org/10.1016/j.jeconom.2005.07.027
- Chen, X. and Fan, Y. (2006b). Estimation of copula-based semiparametric time series models, Journal of Econometrics, 130, 307-335. https://doi.org/10.1016/j.jeconom.2005.03.004
- Embrechts, P., McNeil, A., and Straumann, D. (2002). Correlation and dependence in risk management: properties and pitfalls, In Risk Management: Value at Risk and Beyond, ed. M.A.H. Dempster, Cambridge University Press, Cambridge, 176-223.
- Genest, C., Ghoudi, K., and Rivest, L. P. (1995). A semiparametric estimation procedure of dependence parameters in multivariate families of distributions,
- Jondeau, E. and Rockinger, M. (2006). The copula-GARCH model of conditional dependencies: An international stock market application, Journal of International Money and Finance, 25, 827-853.Biometrika, 82, 545-552. https://doi.org/10.1016/j.jimonfin.2006.04.007
- Kim, G., Silvapulle, M. J., and Silvapulle, P. (2007). Comparison of semiparametric and parametric methods for estimation copulas, Computational Statistics and Data Analysis, 51, 2836-2850. https://doi.org/10.1016/j.csda.2006.10.009
- Lee, S. and Wei, C. Z. (1999). On residual empirical processes of stochastic regression models with applications to time series, The Annals of Statistics, 27, 237-261. https://doi.org/10.1214/aos/1018031109
- Li, Y., Xie, K., and Hu, B. (2013), Copula-ARMA model for multivariate wind speed and its applications in reliability assessment of generating systems, Journal of Electrical Engineering and Technology, 8, 421-427. https://doi.org/10.5370/JEET.2013.8.3.421
- Patton, A. J. (2009). Copula-Based Models for Financial Time Series, In Handbook of Financial Time Series, ed. Andersen, T.G., Davis, R.A., Kreiss, J.P. and Mikosch, T., Springer, Berlin Heidelberg, 767-785.
- Scholzel, C. and Friederichs, P. (2008). Multivariate non-normally distributed random variables in climate research-introduction to the copula approach, Nonlinear Processes in Geophysics, 15, 761-772. https://doi.org/10.5194/npg-15-761-2008
- Sun, W., Rachev, S., Fabozzi, F. J., and Kalev, P. S. (2009). A new approach to modeling co-movement of international equity markets: evidence of unconditional copula-based simulation of tail dependence, Empirical Economics, 36, 201-229. https://doi.org/10.1007/s00181-008-0192-3