Special Issue "Copula Modeling with Applications"
Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 4030
2. China-ASEAN High-Quality Development Research Center, Shandong University of Finance and Economics, Jinan, China
3. Centre of Excellence in Econometrics, Chiang Mai University, Chiang Mai, Thailand
Interests: agricultural economics; high-quality development; TFP; economic modelling
Special Issues, Collections and Topics in MDPI journals
Interests: applied econometrics; econometric analysis; copula modelling; copula-based models
Interests: mathematical statistics, mathematical analysis; probability theory; copula modelling
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- bayesian estimation
- nonlinear analysis
- dependence modeling
- copula inference
- copula simulation
- factor copulas
- vine copulas
- risk measurement