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Misperceptions about extreme dependencies between different financial assetshave been an im- portant element of the recent financial crisis. This paperstudies inhomogeneity in dependence structures using Markov switching regularvine copulas. These account for asymmetric depen- dencies and tail dependenciesin high dimensional data. We develop methods for fast maximum likelihood aswell as Bayesian inference. Our algorithms are validated in simulations andapplied to financial data. We find that regime switches are present in thedependence structure of various data sets and show that regime switching modelscould provide tools for the accurate description of inhomogeneity during timesof crisis.

Detecting regime switches in the dependence structure of high dimensional financial data