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Modeling Dependence in High Dimensions With Factor Copulas

Publication ,  Journal Article
Oh, DH; Patton, AJ
Published in: Journal of Business and Economic Statistics
January 2, 2017

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.

Duke Scholars

Published In

Journal of Business and Economic Statistics

DOI

EISSN

1537-2707

ISSN

0735-0015

Publication Date

January 2, 2017

Volume

35

Issue

1

Start / End Page

139 / 154

Related Subject Headings

  • Econometrics
  • 49 Mathematical sciences
  • 38 Economics
  • 35 Commerce, management, tourism and services
  • 15 Commerce, Management, Tourism and Services
  • 14 Economics
  • 01 Mathematical Sciences
 

Citation

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Oh, D. H., & Patton, A. J. (2017). Modeling Dependence in High Dimensions With Factor Copulas. Journal of Business and Economic Statistics, 35(1), 139–154. https://doi.org/10.1080/07350015.2015.1062384
Oh, D. H., and A. J. Patton. “Modeling Dependence in High Dimensions With Factor Copulas.” Journal of Business and Economic Statistics 35, no. 1 (January 2, 2017): 139–54. https://doi.org/10.1080/07350015.2015.1062384.
Oh DH, Patton AJ. Modeling Dependence in High Dimensions With Factor Copulas. Journal of Business and Economic Statistics. 2017 Jan 2;35(1):139–54.
Oh, D. H., and A. J. Patton. “Modeling Dependence in High Dimensions With Factor Copulas.” Journal of Business and Economic Statistics, vol. 35, no. 1, Jan. 2017, pp. 139–54. Scopus, doi:10.1080/07350015.2015.1062384.
Oh DH, Patton AJ. Modeling Dependence in High Dimensions With Factor Copulas. Journal of Business and Economic Statistics. 2017 Jan 2;35(1):139–154.

Published In

Journal of Business and Economic Statistics

DOI

EISSN

1537-2707

ISSN

0735-0015

Publication Date

January 2, 2017

Volume

35

Issue

1

Start / End Page

139 / 154

Related Subject Headings

  • Econometrics
  • 49 Mathematical sciences
  • 38 Economics
  • 35 Commerce, management, tourism and services
  • 15 Commerce, Management, Tourism and Services
  • 14 Economics
  • 01 Mathematical Sciences