AM  Vol.5 No.13 , July 2014
Measuring Dependence Risk of Funds with Copula in China
Abstract: The aim of this paper is to measure dependence risk of fund market with copulas in China. Firstly, we introduce several common copula functions, then estimate parameters of copula function, and discuss how to select the optimal copula function. Finally, according to Shanghai and Shenzhen fund data, empirical analysis was done. Different combinations of risk values were obtained.
Cite this paper: Tang, J. and Sun, G. (2014) Measuring Dependence Risk of Funds with Copula in China. Applied Mathematics, 5, 1863-1869. doi: 10.4236/am.2014.513179.

[1]   Sklar, A. (1959) Functions de Repartition an Dimension Set Leursmarges. Publications de L’In-stitut de Statistique de L’Universite de Paris.

[2]   Nelsen, R.B. (2006) An Introduction to Copulas. Springer-Verlag, New York.

[3]   Bouye, E., Durrleman, V., Nikeghbali, A., et al. (2000) Copulas for Finance: A Reading Guide and Some Application. Working Paper, City University Business School-Financial Econometrics Research Centre, Londres.

[4]   Chen, S.D., Hu, Z.Y. and Kong, F.L. (2006) Using Copula Function to Measure the Monte Carlo of Risk Value. Social Science Journal of Jilin University, 46, 85-91.

[5]   Rosenberg, J.V. and Schuermann, T. (2006) A General Approach to Integrated Risk Management with Skewed, Fat-tailed Risks. Journal of Financial Economics, 79, 569-614.

[6]   Li, S. and Lu, Z.D. (2008) The Copulas Connect Function in the Application of the Measure on Risk Value. The Financial Management, 20, 10-16.

[7]   Genest, C. and Rivest, L.P. (1993) Statistical Inference Procedures for Bivariate Archimedean Copula. Journal of the American Statistical Association, 8, 1034-1043.

[8]   Ouyang, Z.S. and Wang, F. (2008) The Dependent Risk Measure of the Treasury Bonds Market Which Based on Copulas Connect Method. Statistical Research, 25, 82-85.

[9]   Li, Y. and Cheng, X.J. (2006) The Copulas Connect Tail Correlation Analysis of the Shanghai Composite Index and Hang Seng Index. Systems Engineering, 24, 88-92.

[10]   Liu, W. and Yang, A.L. (2011) The Finite Mixture Model Unsupervised Learning Algorithm Which Based on BIC Criterion and Gibbs Sampling. Electronic Journals, 39, 134-139.