Robust and sparse banking network estimation

Network analysis is becoming a fundamental tool in the study of systemic risk and financial contagion in the banking sector. Still, the network structure must typically be estimated from noisy and aggregated data, as micro data on the status quo banking network structure are often unavailable, or the true network is unobservable. Graphical models can help researchers to infer network structures, but they are often criticized for relying too heavily on unrealistic assumptions. They also tend to yield dense structures that are difficult to interpret. Here, we propose the tlasso model for estimating sparse banking networks. The tlasso captures the conditional dependence structure between banks through partial correlations, and estimates sparse networks in which only the relevant links are identified. The model also accounts for the non-Gaussianity of financial data and it is robust to outliers and model misspecification.