High Dimensional Dynamic Stochastic Copula Models

35 Pages Posted: 25 Jul 2014

See all articles by Drew Creal

Drew Creal

University of Chicago - Booth School of Business - Econometrics and Statistics

Ruey S. Tsay

University of Chicago - Booth School of Business - Econometrics and Statistics

Date Written: May 10, 2014

Abstract

We build a class of copula models that captures time-varying dependence across large panels of financial assets. Our models nest Gaussian, Student's t, grouped Student's t, and generalized hyperbolic copulas with time-varying correlations matrices, as special cases. We introduce time-variation into the densities by writing them as factor models with stochastic loadings. The proposed copula models have flexible dynamics and heavy tails yet remain tractable in high dimensions due to their factor structure. Our Bayesian estimation approach leverages a recent advance in sequential Monte Carlo methods known as particle Gibbs sampling which can draw large blocks of latent variables efficiently and in parallel. We use this framework to model an unbalanced, 200-dimensional panel consisting of credit default swaps and equities for 100 U.S. corporations. Our analysis shows that the grouped Student's t stochastic copula is preferred over seven competing models.

Keywords: state space models, dynamic copulas, Bayesian estimation, particle filters, credit default swaps

JEL Classification: C32, G32

Suggested Citation

Creal, Drew and Tsay, Ruey S., High Dimensional Dynamic Stochastic Copula Models (May 10, 2014). Chicago Booth Research Paper No. 14-25, Available at SSRN: https://ssrn.com/abstract=2470724 or http://dx.doi.org/10.2139/ssrn.2470724

Drew Creal (Contact Author)

University of Chicago - Booth School of Business - Econometrics and Statistics ( email )

Chicago, IL 60637
United States

Ruey S. Tsay

University of Chicago - Booth School of Business - Econometrics and Statistics ( email )

Chicago, IL 60637
United States
773-702-6750 (Phone)
773-702-4485 (Fax)

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