Simulation for Mixture of Archimedean Copulas

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Many dependence structures can consist of mixed copulas. In order to analyze the dependence of stock, we present the method of estimation for mixed copula models. Via generating random samples and using maximum likelihood estimation, the parameters of mixture of Archimedean copulas are estimated. Numerical results show that this method estimates effectively the parameters and tail dependence coefficients. Therefore we can use the method to analyze dependence structure for stocks.

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738-743

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August 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Cherubini, U., Luciano, E., and Vecchiato, W., Copula Methods in Finance, England: John Wiley & Sons Ltd, (2004).

Google Scholar

[2] Coles, S. G., Heffernan, J. E., and Tawn, J. A., Dependence measures for extreme value analyses, Extremes, vol. 2, p.339–365, (1999).

Google Scholar

[3] Deheuvels, P., La fonction de dépendance empirique et ses propriétés -Un test non paramétrique d'indépendance, Académie Royale de Belgique - Bulletin de la classe des sciences – 5e Série, 65, pp.274-292, (1979).

DOI: 10.3406/barb.1979.58521

Google Scholar

[4] Dobric, J., and Schmid, F., Nonparametric Estimation of the Lower Tail Dependence in Bivariate Copulas, Journal of Applied Statistics, vol. 32(4), pp.387-407, (2005).

DOI: 10.1080/02664760500079217

Google Scholar

[5] Embrechts, P., McNeil, A., and Straumann, D., Correlation and dependency in risk management: properties and pitfalls, In: Dempster, M.A.H. (Ed. ), Risk Management: Value at Risk and Beyond, Cambridge University Press, p.176–223, (2002).

DOI: 10.1017/cbo9780511615337.008

Google Scholar

[6] Fortin, I. and Kuzmics, C., Tail dependence in stock return pairs, International Journal of Intelligent Systems in Accounting, Finance & Management, vol. 11, pp.89-107, (2002).

DOI: 10.1002/isaf.216

Google Scholar

[7] Frahm, G., Junker, M., and Schmidt, R., Estimating the tail dependence coefficient: Properties and pitfalls, Insurance: Mathematics and Economics, vol. 37, pp.80-100, (2005).

DOI: 10.1016/j.insmatheco.2005.05.008

Google Scholar

[8] Hu, L., Dependence patterns across financial markets: A mixed copula approach, The Ohio State University, working paper, (2004).

Google Scholar

[9] Joe, H., Multivariate Models and Dependence Concepts, New York, USA: Chapman & Hall/CRC, (1997).

Google Scholar

[10] McNeil, A. J., Frey, R., and Embrechts, P., Quantitative Risk Management, New Jersey, Princeton University Press, (2005).

Google Scholar

[11] Nelsen, R.B., An introduction to Copulas, New York, USA: Springer (2006).

Google Scholar

[12] Ozun, A., and Cifter, A., Portfolio Value-at-Risk with Time-Varying Copula: Evidence from the Americas, Journal of Applied Sciences 7. 14 : 1916-1923, (2007).

DOI: 10.3923/jas.2007.1916.1923

Google Scholar

[13] Schmidt, R., and Stadtmüller, U., Nonparametric estimation of tail dependence, University of Cologne, Department of Economic and Social Statistics. http: /www. uni-koeln. de/wiso-fak/wisostatsem/autoren/Schmidt, (2004).

Google Scholar

[14] Trivedi, P. K., and Zimmer, D. M., Copula Modeling: An Introduction for Practitioners, Foundations and Trends in Econometrics, vol. 1, no. 1, pp.1-111, (2005).

Google Scholar