Robust and Sparse Banking Network Estimation

35 Pages Posted: 24 Aug 2017

See all articles by Gabriele Torri

Gabriele Torri

University of Bergamo

Rosella Giacometti

University of Bergamo

Sandra Paterlini

University of Trento - Department of Economics and Management

Date Written: August 23, 2017


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 typically 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 can capture the conditional dependence structure between banks through partial correlations and detects sparse network structures 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. Our empirical analysis focuses on estimating the dependence structure of a sample of European banks from credit default swap data. We observe that the presence of communities in the banking network plays an important role in terms of systemic risk and contagion dynamics. We also introduce a decomposition of strength centrality that allows us to better characterize the role of each bank in the network and to identify the most relevant channels for the transmission of financial distress.

Keywords: Finance, Financial Networks, Tlasso, Graphical Models, Strength Centrality

JEL Classification: G10

Suggested Citation

Torri, Gabriele and Giacometti, Rosella and Paterlini, Sandra, Robust and Sparse Banking Network Estimation (August 23, 2017). Available at SSRN: or

Gabriele Torri (Contact Author)

University of Bergamo ( email )

Salvecchio 19
Bergamo, 24129

Rosella Giacometti

University of Bergamo ( email )

via dei Caniana 2
Bergamo, 24127

Sandra Paterlini

University of Trento - Department of Economics and Management ( email )

Via Inama 5
Trento, I-38100

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