Dependence Modeling with Copulas

Dependence Modeling with Copulas has made significant advances in the field during the last 15 years, including vine copula modeling of high-dimensional data. It develops generalizations of vine copula models, discusses other multivariate constructions, and presents extensive material on dependence and tail properties to assist in copula model selection. Numerical methods and algorithms for inference and simulation are also important in high-dimensional copula applications.

Format: Paperback / softback
Length: 480 pages
Publication date: 21 January 2023
Publisher: Taylor & Francis Ltd

Dependence Modeling with Copulas has witnessed significant advancements over the past 15 years, particularly in the area of vine copula modeling for high-dimensional data. Vine copula models are constructed by combining a sequence of bivariate copulas, forming a flexible framework for capturing complex dependence relationships. This comprehensive book delves into the development of generalizations of vine copula models, encompassing common and structured factor models that extend from the Gaussian assumption to copulas. It also explores other multivariate constructions, such as multivariate t-distributions and parametric copula families, each with distinct tail properties. Furthermore, the author provides extensive coverage on dependence and tail properties, aiding in the selection of appropriate copula models.

The author emphasizes the importance of numerical methods and algorithms for inference and simulation in high-dimensional copula applications. He presents these algorithms as pseudocode, illustrating their implementation for high-dimensional copula models. Additionally, the book incorporates results to determine dependence and tail properties of multivariate distributions, laying the foundation for future constructions of copula models.

By exploring these developments, Dependence Modeling with Copulas serves as a valuable resource for researchers and practitioners in the field of statistics, finance, and data analysis, enabling them to effectively model and analyze multivariate data with copulas.

Weight: 890g
Dimension: 254 x 178 (mm)
ISBN-13: 9781032477374