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Board of Governors of the Federal Reserve System (US)
Finance and Economics Discussion Series
Modelling Dependence in High Dimensions with Factor Copulas
Dong Hwan Oh
Andrew J. Patton
Abstract

his paper 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 fifty 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.


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Download http://dx.doi.org/10.17016/FEDS.2015.051
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Dong Hwan Oh & Andrew J. Patton, Modelling Dependence in High Dimensions with Factor Copulas, Board of Governors of the Federal Reserve System (US), Finance and Economics Discussion Series 2015-51, 18 May 2015.
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Keywords: Copulas; correlation; dependence; systemic risk; tail dependence
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