  We discuss a Bayesian hierarchical copula model for clusters of financial
time series. A similar approach has been developed in Zhuang et al. (2020).
However, the prior distributions proposed there do not always provide a proper
posterior. In order to circumvent the problem, we adopt a proper global-local
shrinkage prior, which is also able to account for potential dependence
structure among different clusters. The performance of the proposed model is
presented through simulations and a real data analysis.
