Modeling Dependence in High Dimensions with Factor Copulas

• Activity Number: 269
• Type: Invited
• Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
• Sponsor: JBES-Journal of Business & Economic Statistics
• Author(s): Andrew Patton*+ and Dong Hwan Oh
• Companies: Duke University and Duke University
• Keywords: correlation ; dependence ; copulas ; tail dependence ; systemic risk

Abstract

This paper presents new models for the dependence structure, or copula, of economic variables based on a factor structure. The proposed models are particularly attractive for high dimensional applications, involving fifty or more variables. This class of models generally lacks a closed-form density, but analytical results for the implied tail dependence can be obtained using extreme value theory, and estimation via a simulation-based method using rank statistics is simple and fast. We study the finite-sample properties of the estimation method for applications involving up to 100 variables, and apply the model to daily returns on all 100 constituents of the S&P 100 index. 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 the proposed factor copula model provides superior estimates of some measures of systemic risk.