Abstract

http://ssrn.com/abstract=1518628
 
 

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Copula Density Estimation by Total Variation Penalized Likelihood


Leming Qu


affiliation not provided to SSRN

Yi Qian


Northwestern University - Kellogg School of Management

Hui Xie


University of Illinois

September 3, 2009

Communications in Statistics – Simulation and Computation, Vol. 38, pp. 1891-1908, 2009

Abstract:     
Copulas are full measures of dependence among random variables. They are increasingly popular among academics and practitioners in financial econometrics for modeling comovements between markets, risk factors, and other relevant variables. A copula’s hidden dependence structure that couples a joint distribution with its marginals makes a parametric copula non-trivial. An approach to bivariate copula density estimation is introduced that is based on a penalized likelihood with a total variation penalty term. Adaptive choice of the amount of egularization is based on approximate Bayesian Information Criterion (BIC) type scores. Performance are evaluated through the Monte Carlo simulation.

Number of Pages in PDF File: 18

Keywords: Copula, Dependence modeling, Density estimation, Total variation

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Date posted: December 8, 2009  

Suggested Citation

Qu, Leming and Qian, Yi and Xie, Hui, Copula Density Estimation by Total Variation Penalized Likelihood (September 3, 2009). Communications in Statistics – Simulation and Computation, Vol. 38, pp. 1891-1908, 2009. Available at SSRN: http://ssrn.com/abstract=1518628

Contact Information

Leming Qu (Contact Author)
affiliation not provided to SSRN
Yi Qian
Northwestern University - Kellogg School of Management ( email )
2001 Sheridan Road
Evanston, IL 60208
United States
Hui Xie
University of Illinois ( email )
Chicago, IL 60612
United States
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