  We introduce a new class of multivariate heavy-tailed distributions that are
convolutions of heterogeneous multivariate t-distributions. Unlike commonly
used heavy-tailed distributions, the multivariate convolution-t distributions
embody cluster structures with flexible nonlinear dependencies and
heterogeneous marginal distributions. Importantly, convolution-t distributions
have simple density functions that facilitate estimation and likelihood-based
inference. The characteristic features of convolution-t distributions are found
to be important in an empirical analysis of realized volatility measures and
help identify their underlying factor structure.
