  Misperceptions about extreme dependencies between different financial assets
have been an im- portant element of the recent financial crisis. This paper
studies inhomogeneity in dependence structures using Markov switching regular
vine copulas. These account for asymmetric depen- dencies and tail dependencies
in high dimensional data. We develop methods for fast maximum likelihood as
well as Bayesian inference. Our algorithms are validated in simulations and
applied to financial data. We find that regime switches are present in the
dependence structure of various data sets and show that regime switching models
could provide tools for the accurate description of inhomogeneity during times
of crisis.
