Bayesian inference for dynamic vine copulas in higher dimensions
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We propose a class of dynamic vine copula models. This is an extension of static vine copulas and a generalization of dynamic C-vine and D-vine copulas studied 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 with latent AR(1) processes. This modeling approach is very flexible but estimation is not straightforward due to the high-dimensional parameter space. We propose a Bayesian estimation approach, which relies on a novel approximation of the posterior distribution. This approximation allows to use Markov Chain Monte Carlo methods, such as elliptical slice sampling, in a sequential way. In contrast to other Bayesian sequential estimation procedures for vine copula models as proposed by Gruber and Czado (2015), there is no need to collapse copula parameters to point estimates before proceeding to the next tree. Thus more information and uncertainty is propagated from lower to higher trees. A simulation study shows satisfactory performance of the Bayesian procedure. This dynamic modeling and inference approach can be applied in various fields, where static vine copulas have already proven to be successful, including environmental sciences, medicine and finance. Here we study the dependence among 21 exchange rates. For comparison we also estimate a static vine copula model and dynamic C-vine and D-vine copula models. This comparison shows superior performance of the proposed dynamic vine copula model with respect to one day ahead forecasting accuracy.
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