Dependence Modeling with Copulas Chapman & Hall/CRC Monographs on Statistics and Applied Probability Series
Auteur : Joe Harry
Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine copula models, including common and structured factor models that extend from the Gaussian assumption to copulas. It also discusses other multivariate constructions and parametric copula families that have different tail properties and presents extensive material on dependence and tail properties to assist in copula model selection.
The author shows how numerical methods and algorithms for inference and simulation are important in high-dimensional copula applications. He presents the algorithms as pseudocode, illustrating their implementation for high-dimensional copula models. He also incorporates results to determine dependence and tail properties of multivariate distributions for future constructions of copula models.
Introduction. Basics: Dependence, Tail Behavior, and Asymmetries. Copula Construction Methods. Parametric Copula Families and Properties. Inference, Diagnostics, and Model Selection. Computing and Algorithms. Applications and Data Examples. Theorems for Properties of Copulas. Appendix. Index.
Date de parution : 01-2023
17.8x25.4 cm
Date de parution : 09-2016
17.8x25.4 cm
Thèmes de Dependence Modeling with Copulas :
Mots-clés :
Tail Dependence; Copula Families; vine copula modeling of high-dimensional data; Bivariate Copula; generalizations of vine copula models; Hierarchical Archimedean Copula; parametric copula families; Factor Copula; dependence structures and tail properties of copulas; Copula Models; high-dimensional copula applications; Archimedean Copula; dependence and tail properties of multivariate distributions; Bivariate Margins; Tail Dependence Parameters; Pair Copula Construction; Tail Order; Lower Tail Dependence; Vine Copula; Bivariate Copula Families; Regular Vine; Tree T2; Multivariate Extreme; BIC Value; Positive Dependence Condition; Tail Asymmetry; Conditional Cdf; Copula Density; GARCH Parameter; Log λL