Modeling Dependence in High Dimensions With Factor Copulas
By D. Oh, Andrew J. Patton. 2015
265 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/f6d98506a56ea1e6736a8a4760f811e960cc0bcc
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In Economics and Econometrics
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Citations in the corpus
- Copula-Based Models of Systemic Risk in U.S. Agriculture: Implications for Crop Insurance and Reinsurance Contracts (2015, unknown): “as Oh and Patton’s (2012) factor model approach, do not suffer”
- Sparse Bayesian time-varying covariance estimation in many dimensions (2016, methodology): “ parallelize estimation (executed on graphical processors), Lopes et al. (2016) who treat the Cholesky-decomposed covariance matrix within the framework of Bayesian time-varying parameter models, and Oh and Patton (2017) who choose a copula-based approach to link separately estimated univariate mode”