Uncovering Sparse Financial Networks with Information Criteria

TL;DR

This paper introduces an information criterion-based method to identify sparse and meaningful financial networks from FEVD data, improving interpretability and systemic risk analysis.

Contribution

It reformulates FEVD-based connectedness as a regression problem and develops a model selection framework for consistent sparse network recovery.

Findings

Effective in finite samples through Monte Carlo simulations

Robust to heavy-tailed errors and approximate sparsity

Reveals sparse structures in empirical financial data

Abstract

Empirical measures of financial connectedness based on Forecast Error Variance Decompositions (FEVDs) often yield dense network structures that obscure true transmission channels and complicate the identification of systemic risk. This paper proposes a novel information-criterion-based approach to uncover sparse, economically meaningful financial networks. By reformulating FEVD-based connectedness as a regression problem, we develop a model selection framework that consistently recovers the active set of spillover channels. We extend this method to generalized FEVDs to accommodate correlated shocks and introduce a data-driven procedure for tuning the penalty parameter using pseudo-out-of-sample forecast performance. Monte Carlo simulations demonstrate the approach's effectiveness with finite samples and its robustness to approximately sparse networks and heavy-tailed errors. Applications to global stock markets, S&P 500 sectoral indices, and commodity futures highlight the prevalence of sparse networks in empirical settings.