Dynamic stochastic copula models: estimation, inference and applications
Version of Record online: 30 JUN 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Journal of Applied Econometrics
Volume 27, Issue 2, pages 269–295, March 2012
How to Cite
Hafner, C. M. and Manner, H. (2012), Dynamic stochastic copula models: estimation, inference and applications. J. Appl. Econ., 27: 269–295. doi: 10.1002/jae.1197
- Issue online: 16 FEB 2012
- Version of Record online: 30 JUN 2010
- Manuscript Accepted: 18 APR 2010
- Manuscript Received: 15 JUL 2009
- Projet d'Actions de Recherche Concertées. Grant Number: 07/12/002
We propose a new dynamic copula model in which the parameter characterizing dependence follows an autoregressive process. As this model class includes the Gaussian copula with stochastic correlation process, it can be viewed as a generalization of multivariate stochastic volatility models. Despite the complexity of the model, the decoupling of marginals and dependence parameters facilitates estimation. We propose estimation in two steps, where first the parameters of the marginal distributions are estimated, and then those of the copula. Parameters of the latent processes (volatilities and dependence) are estimated using efficient importance sampling. We discuss goodness-of-fit tests and ways to forecast the dependence parameter. For two bivariate stock index series, we show that the proposed model outperforms standard competing models. Copyright © 2010 John Wiley & Sons, Ltd.