  We propose a multivariate generative model to capture the complex dependence
structure often encountered in business and financial data. Our model features
heterogeneous and asymmetric tail dependence between all pairs of individual
dimensions while also allowing heterogeneity and asymmetry in the tails of the
marginals. A significant merit of our model structure is that it is not prone
to error propagation in the parameter estimation process, hence very scalable,
as the dimensions of datasets grow large. However, the likelihood methods are
infeasible for parameter estimation in our case due to the lack of a
closed-form density function. Instead, we devise a novel moment learning
algorithm to learn the parameters. To demonstrate the effectiveness of the
model and its estimator, we test them on simulated as well as real-world
datasets. Results show that this framework gives better finite-sample
performance compared to the copula-based benchmarks as well as recent similar
models.
