Dear Colleagues,

In probability, a copula is a multivariate cumulative distribution function for which the marginal distribution of each variable is uniform. Copulas are important because of Sklar’s Theorem, which states that any multivariate joint distribution can be written in terms of univariate marginal distribution functions and a copula which describes the dependence structure between the variables. Copulas are popular in high-dimensional statistical applications as they allow one to easily model and estimate the distribution of random vectors by estimating marginals and copula separately. 

A recent trend in mathematics, statistics, and econometrics is to relax the multivariate Gaussian or Student’s t distribution assumptions using more flexible copula functions. As such, the copula provides a useful tool to investigate dependence structure, to fit high dimensional data, to measure tail dependence, to relax the unrealistic assumptions of independence and linear correlation, etc. In big data applications, methods for estimating copula parameters for high dimension data and its lengthy computation time are also major difficulties. Bayesian methods and parallel Markov chain Monte Carlo (MCMC) algorithms provide new approaches for estimating copulas under the big data scenario.

Developing copula models for applications in finance and economics has become an intensive research effort until now. In view of that, it is useful to devote a Special Issue to current research and reviews on the topic. This Special Issue on copulas addresses mathematical modeling, nonlinear analysis, statistical inference, and econometrics in finance and economics. 

Potential topics include but are not limited to the following:

• Copula-based models
• High dimensional data with copulas
• Nonlinear time series models
• Dependence modeling with copulas
• Risk measurement and portfolio strategy using copulas
• Copula inference
• Time-varying copulas
• Vine copulas, factor copulas, and their applications in finance and economics
• Bayesian methods for copulas
• Other methods and applications related to copulas. 

Dr. Jianxu Liu
Dr. Woraphon Yamaka
Dr. Zheng Wei
Dr. Shenxiang Xie
Guest Editors

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• copulas
• bayesian estimation
• nonlinear analysis
• dependence modeling
• copula inference
• copula simulation
• factor copulas
• vine copulas
• risk measurement
• portfolio