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We propose a class of dynamic vine copula models. This is an extension ofstatic vine copulas and a generalization of dynamic C-vine and D-vine copulasstudied by Almeida et al (2016) and Goel and Mehra (2019). Within this class,we allow for time-varying dependence by driving the vine copula parameters withlatent AR(1) processes. This modeling approach is very flexible but estimationis not straightforward due to the high-dimensional parameter space. We proposea Bayesian estimation approach, which relies on a novel approximation of theposterior distribution. This approximation allows to use Markov Chain MonteCarlo methods, such as elliptical slice sampling, in a sequential way. Incontrast to other Bayesian sequential estimation procedures for vine copulamodels as proposed by Gruber and Czado (2015), there is no need to collapsecopula parameters to point estimates before proceeding to the next tree. Thusmore information and uncertainty is propagated from lower to higher trees. Asimulation study shows satisfactory performance of the Bayesian procedure. Thisdynamic modeling and inference approach can be applied in various fields, wherestatic vine copulas have already proven to be successful, includingenvironmental sciences, medicine and finance. Here we study the dependenceamong 21 exchange rates. For comparison we also estimate a static vine copulamodel and dynamic C-vine and D-vine copula models. This comparison showssuperior performance of the proposed dynamic vine copula model with respect toone day ahead forecasting accuracy.

Bayesian inference for dynamic vine copulas in higher dimensions