A 2-stage elastic net algorithm for estimation of sparse networks with heavy tailed data

TL;DR

This paper introduces a novel 2-stage elastic net method for estimating sparse networks from heavy-tailed data, demonstrating superior robustness and accuracy over existing methods through simulations and real-world banking network analysis.

Contribution

The paper presents a new 2-stage elastic net estimator that effectively handles heavy-tailed data and outperforms traditional methods in network sparsity and accuracy.

Findings

Performs best for heavy-tailed data in simulations

Robust to distribution misspecification

Provides valuable insights into banking network properties

Abstract

We propose a new 2-stage procedure that relies on the elastic net penalty to estimate a network based on partial correlations when data are heavy-tailed. The new estimator allows to consider the lasso penalty as a special case. Using Monte Carlo simulations, we test the performance on several underlying network structures and four different multivariate distributions: Gaussian, t-Student with 3 and 20 degrees of freedom and contaminated Gaussian. Simulation analysis shows that the 2-stage estimator performs best for heavy-tailed data and it is also robust to distribution misspecification, both in terms of identification of the sparsity patterns and numerical accuracy. Empirical results on real-world data focus on the estimation of the European banking network during the Covid-19 pandemic. We show that the new estimator can provide interesting insights both for the development of network indicators, such as network strength, to identify crisis periods and for the detection of banking network properties, such as centrality and level of interconnectedness, that might play a relevant role in setting up adequate risk management and mitigation tools.