Modeling Dependence in High Dimensions With Factor Copulas

Published

Journal Article

© 2017 American Statistical Association Journal of Business & Economic Statistics. This article presents flexible new models for the dependence structure, or copula, of economic variables based on a latent factor structure. The proposed models are particularly attractive for relatively high-dimensional applications, involving 50 or more variables, and can be combined with semiparametric marginal distributions to obtain flexible multivariate distributions. Factor copulas generally lack a closed-form density, but we obtain analytical results for the implied tail dependence using extreme value theory, and we verify that simulation-based estimation using rank statistics is reliable even in high dimensions. We consider “scree” plots to aid the choice of the number of factors in the model. The model is applied to daily returns on all 100 constituents of the S&P 100 index, and we find significant evidence of tail dependence, heterogeneous dependence, and asymmetric dependence, with dependence being stronger in crashes than in booms. We also show that factor copula models provide superior estimates of some measures of systemic risk. Supplementary materials for this article are available online.

Full Text

Duke Authors

Cited Authors

  • Oh, DH; Patton, AJ

Published Date

  • January 2, 2017

Published In

Volume / Issue

  • 35 / 1

Start / End Page

  • 139 - 154

Electronic International Standard Serial Number (EISSN)

  • 1537-2707

International Standard Serial Number (ISSN)

  • 0735-0015

Digital Object Identifier (DOI)

  • 10.1080/07350015.2015.1062384

Citation Source

  • Scopus