Economics at your fingertips  

Multivariate dependence modeling based on comonotonic factors

Lei Hua and Harry Joe

Journal of Multivariate Analysis, 2017, vol. 155, issue C, 317-333

Abstract: Comonotonic latent variables are introduced into general factor models, in order to allow non-linear transformations of latent factors, so that various multivariate dependence structures can be captured. Through decomposing each univariate marginal into several components, and letting some components belong to different sets of comonotonic latent variables, a great variety of multivariate models can be constructed, and their induced copulas can be used to model various multivariate dependence structures. The paper focuses on an extension of Archimedean copulas constructed by Laplace Transforms of positive random variables. The corresponding comonotonic factor models with one set of comonotonic latent variables and multiple sets of comonotonic latent variables are studied. In particular, we propose several parametric comonotonic factor models that are useful in accommodating both within-group and between-group dependence with possible asymmetric tail dependence. Numerical methods for estimation with the resulting copula models are discussed. There is an application using a dataset of body composition measurements to demonstrate the usefulness of the proposed parsimonious dependence models.

Keywords: Bi-factor; Copula; Dependence clusters/groups; Laplace Transform; Parsimonious dependence models (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2019-01-19
Handle: RePEc:eee:jmvana:v:155:y:2017:i:c:p:317-333