Copula Models of Dependence
Arkady Shemyakin, Alexander Kniazev
Abstract
Arkady Shemyakin, Alexander Kniazev
Abstract
This chapter introduces the definition of a copula, and discusses the basic properties of copulas. The role of Sklar's theorem is that not just every copula function with marginal distributions as arguments is a valid bivariate distribution. It states that every valid bivariate distribution can be represented as a copula of its marginals. The chapter contains the simplest examples of copula functions. It describes the methods of construction of joint distributions using two most popular subclasses of copula functions: elliptical copulas and Archimedean copulas. One of attractive features of copula models is a natural way to organize simulation from joint distributions, whose dependence structure is defined by copulas. The chapter considers simulation from the elliptical family and Archimedean copulas. Copula models are becoming a popular instrument of applied statistical analysis in such fields as finance, risk management, or engineering.
A significance statement is not available in the OpenAlex record.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
This chapter introduces the definition of a copula, and discusses the basic properties of copulas. The role of Sklar's theorem is that not just every copula function with marginal distributions as arguments is a valid bivariate distribution. It states that every valid bivariate distribution can be represented as a copula of its marginals. The chapter contains the simplest examples of copula functions. It describes the methods of construction of joint distributions using two most popular subclasses of copula functions: elliptical copulas and Archimedean copulas. One of attractive features of copula models is a natural way to organize simulation from joint distributions, whose dependence structure is defined by copulas. The chapter considers simulation from the elliptical family and Archimedean copulas. Copula models are becoming a popular instrument of applied statistical analysis in such fields as finance, risk management, or engineering.
Key concepts: Copula (linguistics), Bivariate analysis, Joint probability distribution, Marginal distribution, Econometrics, Mathematics, Tail dependence, Statistical physics