Generative Learning of Heterogeneous Tail Dependence

TL;DR

This paper introduces a scalable multivariate generative model capturing complex, asymmetric tail dependencies in high-dimensional financial data, using a novel moment learning algorithm instead of likelihood methods.

Contribution

It presents a new generative model for heterogeneous tail dependence with a scalable parameter estimation method suitable for large datasets.

Findings

Outperforms copula-based benchmarks in finite-sample tests

Effective on both simulated and real-world datasets

Captures complex asymmetric tail dependencies

Abstract

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.