Robust and sparse banking network estimation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F18%3A10239213" target="_blank" >RIV/61989100:27510/18:10239213 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1016/j.ejor.2018.03.041" target="_blank" >http://dx.doi.org/10.1016/j.ejor.2018.03.041</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ejor.2018.03.041" target="_blank" >10.1016/j.ejor.2018.03.041</a>

Result language

angličtina

Original language name

Robust and sparse banking network estimation

Original language description

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. 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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

50206 - Finance

Project

<a href="/en/project/GA15-23699S" target="_blank" >GA15-23699S: Risk Probability Functionals and Ordering Theory Applied to International Financial Markets and Portfolio Selection Problems</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

European Journal of Operational Research

ISSN

0377-2217

e-ISSN

—

Volume of the periodical

270

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

51-65

UT code for WoS article

000435062800004

EID of the result in the Scopus database

2-s2.0-85046146553