Uncovering Sparse Financial Networks with Information Criteria

Speaker: Dr Fu Ouyang

Affiliation: The University of Queensland

Location: Level 6 Boardroom (629), Colin Clark Building (#39), St Lucia Campus

Teams: https://teams.microsoft.com/meet/42479477765668?p=Mo75WMOeZIBHDobgN9

Passcode: VR7WR2Ly

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 and 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 in finite samples, as well as 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.