Modelling Dependence in High Dimensions with Factor Copulas

Dong Hwan Oh and Andrew Patton
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Dong Hwan Oh: https://www.federalreserve.gov/econres/dong-hwan-oh.htm

No 2015-51, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

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.

Keywords: Copulas; correlation; dependence; systemic risk; tail dependence (search for similar items in EconPapers)
JEL-codes: C31 C32 C51 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2015-05-18
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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http://www.federalreserve.gov/econresdata/feds/2015/files/2015051pap.pdf Full text (application/pdf)
http://dx.doi.org/10.17016/FEDS.2015.051 http://dx.doi.org/10.17016/FEDS.2015.051 (application/pdf)

Related works:
Journal Article: Modeling Dependence in High Dimensions With Factor Copulas (2017)
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